{"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"templateName":"quickstart-page-template","cssClassNames":"page basicpage summit-page","language":"en","description":"Process data incrementally in Snowflake with DCDF Data Architecture and Dynamic Tables for efficient, cost-effective data pipelines.","title":"Getting Started with DCDF Data Architecture Incremental Processing & Logical Partitions","analyticsPageType":"quickstart-page-template","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,"analyticsEnabled":true,"isPasswordProtected":false,"coveoConfig":{"pipeline":"snowflake.com","searchHub":"snowflake.com","organizationId":"snowflakecomputingproduction8neljofn","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d"},":type":"snowflake-site/components/structure/page","analyticsData":{"excludeFromAnalytics":false,"subCategory":"","pageType":"quickstart-page-template","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/content/snowflake-site/global/en/developers/guides/dcdf-incremental-processing","language":"en","category":"general","pageName":"Getting Started with DCDF Data Architecture Incremental Processing & Logical Partitions","contentTags":["snowflake-site:taxonomy/product/data-engineering","snowflake-site:taxonomy/snowflake-feature/transformation","snowflake-site:taxonomy/solution-center/certification/quickstart"]},"analyticsContentTags":["snowflake-site:taxonomy/product/data-engineering","snowflake-site:taxonomy/snowflake-feature/transformation","snowflake-site:taxonomy/solution-center/certification/quickstart"],":items":{"root":{"columnClassNames":{"markup_editor_1950346551":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","modal_container":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"experiencefragment-banner":{"id":"experiencefragment-2bb4a32f22","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"pushdown_banner_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-5a85c51a2b",":type":"snowflake-site/components/container",":items":{"pushdown_banner_copy":{"id":"pushdown-banner-f1ddcc077b","contentHeadline":"Snowflake World Tour hits your city","contentDescription":"See how leading teams deploy agents at scale. Find a stop near you. Register free.","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"/en/world-tour/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Register now"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy"]},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"],"classNames":"aem-xf"},"experiencefragment-header":{"id":"experiencefragment-30a8c7ffdf","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-6b542e958e",":type":"snowflake-site/components/container",":items":{"markup_editor":{"id":"markup-editor-5bbfebbde9","title":" ","cssContent":".footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.mega-nav__sign-in.snowflake-button-container{display:none}@media screen and (min-width:768px){.mega-nav__sign-in.snowflake-button-container{display:inline-block;font-family:'Texta',sans-serif;font-weight:800 !important}}@media screen and (min-width:1024px) and (max-width:1199px){.snowflake-mega-nav-header-buttons-container .snowflake-button-blue .snowflake-button-container{font-size:13px !important}.snowflake-language-navigation .language-icon{width:18px !important;height:18px !important;margin-right:4px !important}}.mega-nav__sign-in svg{display:none}.nav-item__platform-parent-why-sf.snowflake-mega-nav-nav-item\u003Ea:hover,.nav-item__platform-parent.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent !important}.nav-platform-sidebar .snowflake-mega-nav-nav-item:hover.blue-icon .snowflake-mega-nav-nav-item-icon__inner{background-color:var(--ui-01) !important}@media screen and (min-width:1024px){.snowflake-mega-nav-navigation-dropdown{overflow:hidden}.meganav-platform-features{padding-left:64px}.meganav-platform-features::before{content:'';transform:translateX(-64px);display:block;z-index:0;width:100%;height:100%;position:absolute;top:0;background:#f7f9fa}.nav-item--si.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent}.nav-item--si{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:8px}.nav-item__platform-parent{border-bottom:1px solid #ccc;margin-bottom:8px;padding-bottom:16px}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description::after{content:'What Snowflake can do for you \u003E';display:block;color:var(--ui-01);margin-top:16px}.nav-item__platform-parent .snowflake-mega-nav-nav-item-description::after{content:'View the platform \u003E';display:block;color:var(--ui-01);margin-top:16px}}@media screen and (min-width:1367px){.snowflake-mega-nav-nav-item-description{font-size:13px !important;line-height:20px !important}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{font-size:17px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-title,.nav-item__platform-parent .snowflake-mega-nav-nav-item-title{font-size:24px !important;line-height:32px !important;margin-bottom:8px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description,.nav-item__platform-parent .snowflake-mega-nav-nav-item-description{font-size:14px !important;line-height:20px !important}}html.wf-texta-n9-loading .display-1-v2{font-size:48px!important;line-height:50px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-4-v2{font-size:18px!important;line-height:24px!important;font-family:sans-serif!important}@media screen and (min-width:768px){html.wf-texta-n9-loading .display-2-v2{font-size:48px!important;line-height:50px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:55.5px!important;line-height:54px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .heading-5-v2,html.wf-lato-n4-loading .snowflake-card-v2-advanced-text .snowflake-text p{font-size:15.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:34px!important;line-height:38px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-4,html.wf-texta-n8-loading .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-regular .snowflake-button-container{font-size:13px!important;line-height:20px!important;letter-spacing:.25px!important;font-family:sans-serif!important}}@media screen and (min-width:1024px){html.wf-lato-n4-loading .snowflake-mega-nav-nav-item-description{font-size:11.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .snowflake-button-compact .snowflake-button-container{font-size:12px!important;letter-spacing:0!important;line-height:18px!important}}@media screen and (min-width:1367px){html.wf-lato-n4-loading .hp-hero__eyebrow a\u003Eb:first-child{font-size:11px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .hp-hero__eyebrow a{font-size:13px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-2-v2{font-size:61px!important;line-height:60px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:74.5px!important;line-height:74px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:41px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-3-v2{font-family:sans-serif!important;letter-spacing:-.75px!important;font-size:33.75px!important}html.wf-texta-n9-loading .heading-4-v2{font-size:19.5px!important;line-height:26px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2{font-size:12px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:14px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-1,html.wf-lato-n4-loading .cq-Editable-dom[data-cq-data-path*=text] ol\u003Eli,html.wf-lato-n4-loading .snowflake-text li,html.wf-lato-n4-loading .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text li,html.wf-lato-n4-loading .text-size-large .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-large.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom span[data-testid=text-content],html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Ep,html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Eul\u003Eli{font-size:17.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content],html.wf-texta-n8-loading .snowflake-button-link .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-link-back .snowflake-button-container{font-size:15.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-3,html.wf-lato-n4-loading .text-size-small .snowflake-text li,html.wf-lato-n4-loading .text-size-small .snowflake-text p,html.wf-lato-n4-loading .text-size-small .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-small.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}}#industryPlatformSection,.sc-hero{background-position:top left;background-size:20% auto}.bwalignc,.bwalignr{list-style-position:inside}.snowflake-text p sup{font-size:10px}#industryPlatformSection .industry-platform__row .snowflake-flexible-column-container-items,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container,.snowflake-hero-system-content-container{gap:16px}.agenda-item p,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.partner-details p{margin:0!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::after,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::before,.hide-logo .snowflake-case-study-card-logo,.partner-page__powered-by-logo,.sc-hero div.code-toolbar\u003E.toolbar,.snowflake-card-v2-advanced.no-link .snowflake-card-v2-advanced-button,.snowflake-partner-hero-card-badge-container{display:none!important}.section--card-mobile-carousel .snowflake-flexible-column-container-items-with-carousel{max-width:100%!important}@media screen and (min-width:768px){.button-group-pair .snowflake-button-container.inline-button--desktop,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;display:inline-block!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:flex-start!important}.button-group-pair.center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center!important}.section--card-mobile-carousel{margin-left:var(--tablet-portrait-margin,48px)!important;margin-right:var(--tablet-portrait-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-portrait-margin) * 2)!important}}@media screen and (min-width:1024px){.section--card-mobile-carousel{margin-left:var(--tablet-horizontal-margin,48px)!important;margin-right:var(--tablet-horizontal-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-horizontal-margin) * 2)!important}.snowflake-mega-nav-header-mobile-icon{display:none!important}}@media screen and (min-width:1367px){.section--card-mobile-carousel{margin-left:var(--desktop-margin,6.5%)!important;margin-right:var(--desktop-margin,6.5%);width:87%!important;width:calc(100% - var(--desktop-margin) * 2)!important}.logo-container{min-width:143px}.sc-hero__headline .heading-1-v2{font-size:60px}.snowflake-mega-nav-navigation-title{font-size:17px}.snowflake-mega-nav-dropdown-footer-wrapper .snowflake-title-v2 .snowflake-title-v2-line:first-child{font-size:16px!important;line-height:24px!important}}.hero--home{overflow:hidden;background-color:var(--ui-01);z-index:2}.hp-hero__subheadline{width:90%}.hero--home .snowflake-button-container{transition:.3s}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-secondary a:hover,.hero--home .snowflake-button-white a:hover{transition:.3s;background-color:var(--ui-02)!important;color:var(--ui-05)!important}.hero--home .snowflake-button-secondary a:hover{border-color:var(--ui-05)!important}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-white a:hover{border-color:var(--ui-02)!important}.bwalignc,.hp-hero__eyebrow{text-align:center}.hp-hero__eyebrow a{display:inline-flex;flex-direction:column;justify-content:center;cursor:pointer;padding:8px;border-radius:var(--spacing-01);gap:8px;align-items:center;background-color:#45aee3;color:var(--ui-03);font-family:Texta,sans-serif;font-weight:800;font-size:16px;line-height:22px;transition:background-color .3s}.hp-hero__eyebrow a:hover{background-color:#7fc6ea;text-decoration:none;transition:background-color .3s}.hp-hero__eyebrow a\u003Eb:first-child{text-transform:uppercase;white-space:nowrap;display:inline-block;background-color:var(--ui-02);color:var(--ui-05);font-size:12px!important;line-height:16px!important;font-family:Lato,sans-serif;font-weight:500!important;padding:3px 6px;border-radius:2px;letter-spacing:1px}@media screen and (min-width:767px){.hp-hero__eyebrow{text-align:left}.hp-hero__eyebrow a{flex-direction:row;text-align:left}}.hero--home__inner .offset-video,.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{max-height:200px;overflow:hidden}.hero--home__inner .offset-video .wistia-responsive-padding{padding-top:100%}.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{position:absolute!important;top:0;left:0;width:100%}.offset-video__bg-image{z-index:-1}@media screen and (min-width:768px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{position:absolute!important;max-height:none;top:0;left:0;width:250%;padding-bottom:250%;transform:translate(0,-50%);height:0}.workloads_7.unistore{max-width:317px}}.promo-banner--homepage{z-index:2}.homepage-banner-offset-container::after{content:\"\";display:block;position:absolute;bottom:0;z-index:1;left:0;width:100%;height:80%;background:#fff}.section--quicklinks .snowflake-button-full-width a{padding-left:24px!important;padding-right:24px!important;transition:box-shadow .25s cubic-bezier(.4,0,.2,1);text-align:left;display:flex;justify-content:center;align-items:center}.section--quicklinks .snowflake-button-full-width a:hover{box-shadow:0 16px 16px 0 rgb(0 0 0 / .16);transition:box-shadow .25s cubic-bezier(.4,0,.2,1)}.section--quicklinks .snowflake-button-container:focus-visible a::before,.section--quicklinks .snowflake-button-full-width a::before{content:\"\";width:23px;height:23px;flex-shrink:0;margin-right:12px;display:inline-block;background-size:cover;background-repeat:no-repeat;background-position:center}#industryPartnerSlider .snowflake-navigation-icon.swiper-button-disabled,#partnerResources .section--resource-hub a svg,.button-tabs span.snowflake-tabs-navigation-item:after,.customer-card--hide-cta .snowflake-case-study-card-button,.dot-tabs span.snowflake-tabs-navigation-item::after,.partner-sidebar__mobile-expand,html:not(.aem-AuthorLayer-initial):not(.aem-AuthorLayer-Edit) .tab-content:not(.is-active){display:none}.section--quicklinks .snowflake-button-full-width a.pricing::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/decorative-icons/pricing-icon.svg)}.section--quicklinks .snowflake-button-full-width a.snowflake_on_snowflake::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon_snowflake-bug.svg)}.section--quicklinks .snowflake-button-full-width a.virtual_hands_on_labs::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__training.svg)}.section--quicklinks .snowflake-button-full-width a.weekly_demo::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__webinars.svg)}@media screen and (min-width:1024px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{left:-50%}.section--quicklinks .snowflake-flexible-column-container-items{gap:24px}.snowflake-quote-item-inner{padding:32px 24px 24px!important}}#communitiesOuter_overflowBottomGray::after{max-height:100px}#caseStudyOuter_overflowBottomMidBlue::after{max-height:180px}#caseStudyInner .snowflake-case-study-card .snowflake-wistia-video{border-radius:0!important}#caseStudyInner .snowflake-case-study-card{box-shadow:none!important;border-radius:0}#caseStudyInner{max-width:1200px;margin:0 auto;box-shadow:rgb(152 162 179 / .1) 0 10px 20px 0,rgb(152 162 179 / .25) 0 2px 6px 0;border-radius:8px;overflow:hidden;position:relative;z-index:1}.case-study__logo-bar\u003E.snowflake-flexible-column-container-items{background:#f7f9fa;padding:32px 16px 40px}.case-study__logo-bar .cmp-image__image{width:90%;margin:0 auto;max-width:240px}.hp-platform__text-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child),.sc-sidebar__group .snowflake-button-link{margin-top:8px}.workloads_7.unistore{margin-left:auto;margin-right:auto}#homepageFootnotesInner .snowflake-simple-stat-disclaimer .snowflake-text p{color:#fff!important}.snowflake-simple-stat-disclaimer .snowflake-text p\u003Ea{border-bottom:1px solid var(--ui-03);color:var(--text-03)}.snowflake-card-v2-advanced{color:inherit}#workloadCardGridOuter .snowflake-card-v2-base-front{gap:0}.video-modal.snowflake-modal-window-open-inner{background-color:#fff0;padding:8px;border:none}.snowflake-container-arrow-dotted-faded .snowflake-container-arrow-dotted-faded-image{width:40%!important;max-width:420px;top:4%!important}.list--blue-bullets ul{margin:0!important;padding:0!important;list-style-type:none}.list--blue-bullets li{margin:0;padding:0 0 0 32px;position:relative}.list--blue-bullets li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.list--blue-bullets li:not(:last-child){margin-bottom:1rem}.logo-tabs .snowflake-navigation-container,.snowflake-simple-stat-content:empty,.summit-speaker-card .snowflake-card-v2-advanced-text{margin-bottom:0}#techResourceInner,#techResourceOuter,div.overflow-bottom--blue,div.overflow-bottom--gray,div.overflow-bottom--mid-blue,div.overflow-bottom--white,div.overflow-top--blue,div.overflow-top--gray,div.overflow-top--mid-blue,div.overflow-top--white,div[id$=overflowBottomGray],div[id$=overflowBottomMidBlue],div[id$=overflowTopBlue],div[id$=overflowTopGray]{position:relative}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{content:\"\";display:block;position:absolute;left:0;width:100%;height:40%}div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{top:0}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after{bottom:0}div.overflow-bottom--white::after,div.overflow-top--white::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopWhite]::after{background:#fff!important}div.overflow-bottom--gray::after,div.overflow-top--gray::after,div[id$=overflowBottomGray]::after,div[id$=overflowTopGray]::after{background:#f6f9fa!important}div.overflow-bottom--mid-blue::after,div.overflow-top--mid-blue::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowTopMidBlue]::after{background:#11567f!important}div.overflow-bottom--blue::after,div.overflow-top--blue::after,div[id$=overflowBottomBlue]::after,div[id$=overflowTopBlue]::after{background:#259edc!important}.snowflake-premium-content-banner.promo-banner--no-shadow{box-shadow:none!important}#industryPartnerSlider .cmp-image__image,#industryPartnerSlider .section--partner-tabs .snowflake-image-container .cmp-image__image,#partnerSidebar,.has-shadow .cmp-image__image{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25)}.content-chip--has-desc{align-items:flex-start;padding:20px!important}.content-chip--has-desc .snowflake-content-chip-image{max-width:100px}.content-chip--has-desc .snowflake-content-chip-image__image{aspect-ratio:1}.content-chip--has-desc .snowflake-title-v2-line:first-child{font-size:18px!important}.content-chip--has-desc .snowflake-title-v2-line:nth-child(2){color:#000!important;font-weight:500!important;font-size:16px!important;line-height:22px!important;margin-top:2px!important}.content-chip--has-desc .snowflake-content-chip-button{margin-top:6px!important;font-size:18px!important;display:none}.square-image .snowflake-content-chip-image{aspect-ratio:1;max-width:120px}.section--logo-bar.smaller-logos .snowflake-image-container .cmp-image__image{max-width:200px;margin:0 auto}.snowflake-card-v2-advanced-tag,.snowflake-content-chip-tag{padding:3px 6px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-button,.snowflake-card-v2-advanced-title:first-child,.summit-pricing-block__aside ul{margin-top:0}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:40px;height:40px;display:flex;justify-content:center;align-items:center;margin:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{width:12px;height:12px;background:var(--ui-12);border-radius:100%}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p,.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{font-size:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{background:var(--ui-01)}.button-tabs .snowflake-navigation-container .swiper-wrapper{padding:8px 0}.button-tabs .snowflake-navigation-container .swiper-slide{margin:0 6px}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{padding:8px 24px;background-color:#f6f9fa;border-radius:48px;margin:0}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{text-transform:uppercase;font-family:Texta,sans-serif;font-weight:700}.button-tabs .border-top{border-top:1px solid #ccc}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{background-color:var(--ui-01);box-shadow:0 2px 6px 0 rgb(152 162 179 / .25),0 10px 20px 0 rgb(152 162 179 / .1)}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{color:#fff}.button-tabs.has-icons .snowflake-navigation-container .snowflake-tabs-navigation-item p::before{content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-position:center center;margin-right:12px;vertical-align:middle;margin-top:-3px}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:220px;padding-bottom:50%;height:0;margin:0 8px!important;background-size:cover;background-repeat:no-repeat;opacity:.5;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item:hover{opacity:.75;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{opacity:1;transition:opacity .3s}.dot-tabs .aem-container.cmp-tabs,.logo-tabs .aem-container.cmp-tabs{display:flex;flex-direction:column-reverse}.snowflake-icon.is-center{margin:0 auto;display:block}#industryPartnerSlider .snowflake-flexible-column-container-items,#partnerLogoSquare .snowflake-flexible-column-container-items{gap:24px}#techResourceOuter::after{content:\"\";display:block;position:absolute;top:0;left:0;width:100%;height:40%;background:#f6f9fa}#techResourceInner{z-index:1}.partner-tier-tag h6{display:inline-block!important;padding:2px 6px;border-radius:2px;color:#666}.partner-tier-tag.registered h6{background-color:#f6f9fa}.partner-tier-tag.elite h6{background-color:#11567f;color:#fff}.partner-tier-tag.premier h6{background-color:#b14c77;color:#fff}.partner-tier-tag.select h6{background-color:#5094a0;color:#fff}.partner-details\u003Espan{display:flex;gap:24px}.partner-details a{color:inherit!important;font-weight:400!important}.partner-details p::before{content:\"\";display:inline-block;vertical-align:middle;width:16px;height:16px;background-repeat:no-repeat;background-position:center;transform:translateY(-1px);background-size:auto 90%;margin-right:6px}.partner-details__location::before{background-image:url(\"data:image/svg+xml,%3Csvg width='13' height='18' viewBox='0 0 13 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M6.25 17.7531C6.4375 17.7531 6.6 17.6844 6.7375 17.5531C6.875 17.4219 6.95 17.2531 6.95 17.0531C6.95 16.8531 7.075 16.4281 7.3 15.7969C7.5875 15.0281 7.925 14.3156 8.30625 13.6406C8.8 12.7781 9.3125 12.1031 9.85 11.6094C10.75 10.7969 11.4125 9.96563 11.85 9.12188C12.2875 8.27813 12.5063 7.40313 12.5063 6.49063C12.5063 5.36563 12.2187 4.31563 11.6437 3.33438C11.0937 2.40313 10.3438 1.65938 9.4 1.10938C8.43125 .534376 7.375 .246876 6.24375 .246876C5.1125 .246876 4.06875 .534376 3.0875 1.10938C2.15625 1.65938 1.4125 2.40313 .862498 3.33438C.287498 4.31563 0 5.36563 0 6.49063C0 7.47188 .262499 8.42813 .787499 9.35938C1.14375 10.0031 1.65625 10.6656 2.3125 11.3344C2.75625 11.8031 3.24375 12.4781 3.78125 13.3656C4.225 14.0969 4.63125 14.8594 5 15.6656C5.35 16.3844 5.53125 16.8531 5.55625 17.0656C5.55625 17.2594 5.625 17.4156 5.7625 17.5531C5.9 17.6844 6.0625 17.7531 6.25 17.7531ZM6.16875 14.9156C5.775 14.0656 5.325 13.2469 4.825 12.4594C4.275 11.5594 3.7625 10.8719 3.28125 10.3969C2.625 9.71563 2.1375 9.05938 1.825 8.43438C1.5125 7.80313 1.35625 7.16563 1.35625 6.50313C1.35625 5.61563 1.575 4.80313 2.0125 4.05313C2.45 3.30313 3.04375 2.71563 3.7875 2.27813C4.5375 1.84063 5.35 1.62188 6.2375 1.62188C7.125 1.62188 7.9375 1.84063 8.6875 2.27813C9.4375 2.71563 10.0312 3.30313 10.475 4.04688C10.9187 4.80313 11.1375 5.62188 11.1375 6.50313C11.1375 7.90313 10.3937 9.26563 8.9125 10.5969C8.35 11.1094 7.8125 11.7906 7.3 12.6406C6.88125 13.3344 6.50625 14.0969 6.16875 14.9219V14.9156ZM6.26875 8.36563C6.65625 8.36563 7.01875 8.26563 7.35625 8.07188C7.69375 7.87813 7.95625 7.60938 8.14375 7.28438C8.3375 6.95313 8.43125 6.59063 8.43125 6.19688C8.43125 5.80313 8.33125 5.43438 8.1375 5.10313C7.9375 4.76563 7.675 4.50313 7.3375 4.31563C7 4.12813 6.6375 4.02813 6.24375 4.02813C5.85 4.02813 5.4875 4.12813 5.15625 4.32188C4.825 4.52188 4.56875 4.78438 4.375 5.12188C4.18125 5.45938 4.0875 5.82188 4.0875 6.20938C4.0875 6.59688 4.1875 6.95938 4.38125 7.29688C4.58125 7.63438 4.84375 7.89688 5.18125 8.08438C5.51875 8.27813 5.88125 8.37188 6.26875 8.37188V8.36563ZM6.24375 7.50313C5.8875 7.50313 5.575 7.37188 5.31875 7.11563C5.0625 6.85938 4.93125 6.55313 4.93125 6.19063C4.93125 5.82813 5.0625 5.52188 5.31875 5.26563C5.575 5.00938 5.88125 4.87813 6.24375 4.87813C6.60625 4.87813 6.9125 5.00938 7.16875 5.26563C7.425 5.52188 7.55625 5.82813 7.55625 6.19063C7.55625 6.55313 7.425 6.85938 7.16875 7.11563C6.9125 7.37188 6.60625 7.50313 6.24375 7.50313Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}.partner-details__website::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='16' viewBox='0 0 18 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M2.61587 2.96889C2.61587 2.75109 2.79633 2.57062 3.01413 2.57062C3.23192 2.57062 3.41238 2.75109 3.41238 2.96889C3.41238 3.18669 3.23192 3.36716 3.01413 3.36716C2.79633 3.36716 2.61587 3.18669 2.61587 2.96889ZM4.21512 2.96889C4.21512 2.75109 4.39558 2.57062 4.61338 2.57062C4.83117 2.57062 5.01163 2.75109 5.01163 2.96889C5.01163 3.18669 4.83117 3.36716 4.61338 3.36716C4.39558 3.36716 4.21512 3.18669 4.21512 2.96889ZM5.81438 2.96889C5.81438 2.75109 5.99484 2.57062 6.21264 2.57062C6.43043 2.57062 6.61089 2.75109 6.61089 2.96889C6.61089 3.18669 6.43043 3.36716 6.21264 3.36716C5.99484 3.36716 5.81438 3.18669 5.81438 2.96889ZM17.2518 .697559H1.19085C.811258 .697559 .506348 1.0025 .506348 1.38209V14.6179C.506348 14.9975 .811258 15.3024 1.19085 15.3024H17.2518C17.6314 15.3024 17.9363 14.9975 17.9363 14.6179V1.38209C17.9363 1.0025 17.6314 .697559 17.2518 .697559ZM16.5673 2.06035V3.90853H1.86914V2.06035H16.5673ZM1.86914 13.9334V4.78593H16.5673V13.9334H1.86914Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}#partnerSidebar{border-radius:4px;background-color:#fff;padding:24px 24px 32px;border-bottom:6px solid #29b5e8}#partnerSidebar h5,.newsletter-disclaimer p{font-size:14px!important}#partnerSidebar ul{margin-top:0;list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px}#partnerSidebar li{border:1px solid;border-radius:2px;padding:0 4px!important;font-size:11px!important;letter-spacing:.25px;text-transform:uppercase}div.snowflake-partner-hero-card{width:100%;margin:0}.partner-details__logo{max-width:380px;margin:0 auto}@media screen and (max-width:767px){.left-alignment .hp-hero__subheadline{margin-left:auto;margin-right:auto}.left-alignment .hp-hero__headline .snowflake-title-v2-line,.left-alignment .hp-hero__subheadline .snowflake-title-v2-line{text-align:center}.hero--home__inner .snowflake-flexible-column-container-items-top-padding-large{padding-top:var(--spacing-02)}.section--logo-bar\u003E.snowflake-flexible-column-container-items{display:flex;flex-wrap:wrap;flex-direction:row;justify-content:center;gap:8px}.section--logo-bar\u003E.snowflake-flexible-column-container-items\u003Ediv{width:calc(33.33% - 8px)}.partner-sidebar__mobile-expand{display:inline-block;color:#249edc;border-color:#249edc!important}#partnerSidebar li:nth-child(n+6),.summit-nav__links .snowflake-button-tertiary{display:none}.sc-body__sidebar{background-color:#f6f9fa;padding:24px}.sc-body__content{padding:0 24px 24px}.summit-speaker-card .snowflake-card-v2-advanced-content{padding:24px}}#partnerResources h6,.snowflake-tabs-navigation-item p.body-1{font-size:16px!important}#partnerResources .section--resource-hub{padding:0 16px}#partnerResources .section--resource-hub a,.bwalignl{text-align:left}@media screen and (max-width:1023px){.hero--workload .snowflake-hero-system-media-container{width:100%}}.section--timely-content .snowflake-content-chip,.snowflake-mega-nav-dropdown-footer-wrapper{align-items:center}.section--timely-content .snowflake-content-chip-image{max-width:94px}.section--timely-content .snowflake-content-chip-image__inner{line-height:0}.section--timely-content .snowflake-content-chip-image__image{aspect-ratio:1;height:auto}.section--workload-overview .workload-overview__headline{max-width:280px;margin:0 auto}#industryPartnerSlider .swiper-slide{margin-top:0!important;padding:0 12px}#industryPartnerSlider .snowflake-tabs-navigation-item{margin-left:0!important;margin-right:0!important}#industryPartnerSlider .snowflake-premium-content-banner-background-grad-white .snowflake-premium-content-banner{box-shadow:none}#industryPartnerSlider .logo-slider__slide .aem-container{display:flex;padding:0 8px!important;flex-wrap:wrap;gap:16px!important;justify-content:center}#industryPartnerSlider .logo-slider__slide .aem-container\u003Ediv{width:48%;max-width:200px}#useCaseTabs{padding-top:24px;padding-bottom:24px;padding-right:24px}#useCaseTabs .tab-content.is-active{display:block}#useCaseTabs .vert-tab{border-bottom:1px solid #a0bbcc;padding-bottom:16px}#useCaseTabs .vert-tab p{display:inline-block}#useCaseTabs .vert-tab p:hover{cursor:pointer}#useCaseTabs .vert-tab p,#useCaseTabs .vert-tab.is-active p.not-active{color:#249edc}#useCaseTabs .vert-tab p.is-active,#useCaseTabs .vert-tab.is-active p{color:#000}#industryPlatformSection{background-image:url(/adobe/dynamicmedia/deliver/dm-aid--db074ad5-7122-4c51-87a3-76c3aa466182/double-arrow-bg%403x.png);background-repeat:no-repeat}.snowflake-text p.featured-quote__source{font-weight:900!important;text-transform:uppercase;font-size:16px!important;margin-top:2rem!important}.snowflake-text p.featured-quote__title{margin-top:0!important;font-size:16px!important}.snowflake-case-study-card-logo img{width:auto!important;height:100px!important;transform:translateX(-15%)}.snowflake-quote-item-quote-text{font-weight:600!important}#customerStoryStatsInner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row}#customerStoryStat1,#customerStoryStat2{max-width:240px}#storyHighlights{border-radius:4px;padding:1rem}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line,.summit-pricing-block__tile .black-blue-text-color .snowflake-title-v2-line{color:#000!important}.snowflake-youtube-embedded-wrapper{border-radius:var(--small-border-radius)}#arcticNavItem::before,#offset::before,#open-source::before{color:var(--text-05);font-family:Texta,sans-serif!important}#offset,.sc-architecture-caption{margin-top:16px}.hero--press .snowflake-title-v2-line{text-transform:none!important}@media screen and (min-width:768px){.subpage-timely-content__inner\u003E.snowflake-flexible-column-container-items{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25);padding:var(--spacing-04);border-radius:4px;overflow:hidden}#partnerLogoSquare{padding:0 0 0 48px}.hero--workload .snowflake-container{max-width:1440px;margin:0 auto!important;align-items:center}#industryPartnerSlider.snowflake-flexible-column-container-2-column-40-60\u003E.snowflake-flexible-column-container-items{grid-template-columns:minmax(40%,4fr) minmax(0,6fr)}#industryPartnerSlider .swiper-slide{padding:0 24px}.sc-body{padding:48px}.sc-body\u003E.snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:124px}}.snowflake-button-container.has-icon{display:inline-flex;justify-content:center;align-items:center;text-align:left}.snowflake-button-container.has-icon::before{content:\"\";display:inline-block;width:20px;height:20px;margin-right:12px;background-size:contain;background-repeat:no-repeat;background-position:center}.snowflake-button-container.is-video::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M9 1.28663C13.2523 1.28663 16.7134 4.74768 16.7134 9C16.7134 13.2523 13.2523 16.7134 9 16.7134C4.74768 16.7198 1.28663 13.2588 1.28663 9C1.28663 4.74124 4.74768 1.28663 9 1.28663ZM9 0C4.0336 0 0 4.0336 0 9C0 13.9664 4.0336 18 9 18C13.9728 18 18 13.9664 18 9C18 4.0336 13.9728 0 9 0Z' fill='white'/%3E%3Cpath d='M7.75106 6.18211C7.42941 6.16925 7.16565 6.42658 7.16565 6.74823V11.2772C7.16565 11.7082 7.65457 11.9848 8.02126 11.7597L11.7975 9.4952C12.1578 9.27647 12.1578 8.74252 11.7975 8.52379L8.02126 6.25931C7.93763 6.21428 7.84756 6.18211 7.75106 6.18211Z' fill='white'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-github::before{background-image:url(\"data:image/svg+xml,%3Csvg width='20' height='21' viewBox='0 0 20 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 .651794C4.475 .651794 0 5.12679 0 10.6518C0 15.0768 2.8625 18.8143 6.8375 20.1393C7.3375 20.2268 7.525 19.9268 7.525 19.6643C7.525 19.4268 7.5125 18.6393 7.5125 17.8018C5 18.2643 4.35 17.1893 4.15 16.6268C4.0375 16.3393 3.55 15.4518 3.125 15.2143C2.775 15.0268 2.275 14.5643 3.1125 14.5518C3.9 14.5393 4.4625 15.2768 4.65 15.5768C5.55 17.0893 6.9875 16.6643 7.5625 16.4018C7.65 15.7518 7.9125 15.3143 8.2 15.0643C5.975 14.8143 3.65 13.9518 3.65 10.1268C3.65 9.03929 4.0375 8.13929 4.675 7.43929C4.575 7.18929 4.225 6.16429 4.775 4.78929C4.775 4.78929 5.6125 4.52679 7.525 5.81429C8.325 5.58929 9.175 5.47679 10.025 5.47679C10.875 5.47679 11.725 5.58929 12.525 5.81429C14.4375 4.51429 15.275 4.78929 15.275 4.78929C15.825 6.16429 15.475 7.18929 15.375 7.43929C16.0125 8.13929 16.4 9.02679 16.4 10.1268C16.4 13.9643 14.0625 14.8143 11.8375 15.0643C12.2 15.3768 12.5125 15.9768 12.5125 16.9143C12.5125 18.2518 12.5 19.3268 12.5 19.6643C12.5 19.9268 12.6875 20.2393 13.1875 20.1393C17.1375 18.8143 20 15.0643 20 10.6518C20 5.12679 15.525 .651794 10 .651794Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-quickstart::before{background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='21' viewBox='0 0 15 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M13.8489 2.79368H11.6439V2.38493C11.6439 1.71368 11.1451 .967427 10.4251 .967427H8.94762C8.80887 .359927 8.37387 .299927 7.89762 .299927H7.23012C6.85512 .299927 6.26637 .299927 6.08637 .967427H4.68387C3.94887 .967427 3.35637 1.74368 3.35637 2.38493V2.79368H1.15137C.738867 2.79368 .401367 3.13118 .401367 3.54368V20.2537C.401367 20.6662 .738867 21.0037 1.15137 21.0037H13.8489C14.2614 21.0037 14.5989 20.6662 14.5989 20.2537V3.54368C14.5989 3.13118 14.2614 2.79368 13.8489 2.79368ZM4.29387 2.38493C4.29387 2.18243 4.54137 1.90493 4.68387 1.90493H6.50262C6.76137 1.90493 6.97137 1.69493 6.97137 1.43618C6.97137 1.33868 6.97887 1.27868 6.98637 1.24118C7.05012 1.23368 7.15512 1.23368 7.23387 1.23368H7.90137C7.95012 1.23368 8.00637 1.23368 8.05137 1.23368C8.05512 1.27868 8.05887 1.34243 8.05887 1.43243C8.05887 1.69118 8.26887 1.90118 8.52762 1.90118H10.4289C10.5301 1.90118 10.7101 2.14493 10.7101 2.38118V2.78993H4.29762V2.38118L4.29387 2.38493ZM13.0989 19.4999H1.90137V4.29368H13.0989V19.5037V19.4999Z' fill='%23249EDC'/%3E%3Cpath d='M3.82512 16.0424H11.1751C11.4339 16.0424 11.6439 15.8324 11.6439 15.5736V6.88486C11.6439 6.62611 11.4339 6.41611 11.1751 6.41611H3.82512C3.56637 6.41611 3.35637 6.62611 3.35637 6.88486V15.5736C3.35637 15.8324 3.56637 16.0424 3.82512 16.0424ZM4.29387 15.1049V13.3686H10.7064V15.1049H4.29387ZM10.7101 7.35361V12.4311H4.29762V7.35361H10.7101Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 9.35989H8.83887C9.09762 9.35989 9.30762 9.14989 9.30762 8.89114C9.30762 8.63239 9.09762 8.42239 8.83887 8.42239H6.16512C5.90637 8.42239 5.69637 8.63239 5.69637 8.89114C5.69637 9.14989 5.90637 9.35989 6.16512 9.35989Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 11.3624H8.83887C9.09762 11.3624 9.30762 11.1524 9.30762 10.8937C9.30762 10.6349 9.09762 10.4249 8.83887 10.4249H6.16512C5.90637 10.4249 5.69637 10.6349 5.69637 10.8937C5.69637 11.1524 5.90637 11.3624 6.16512 11.3624Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-download::before{background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='18' viewBox='0 0 16 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M15.2017 17.1637H.798265C.364425 17.1637 0 16.7993 0 16.3655V12.3568C0 11.923 .364425 11.5585 .798265 11.5585C1.2321 11.5585 1.59653 11.923 1.59653 12.3568V15.5498H14.4035V12.3568C14.4035 11.923 14.7679 11.5585 15.2017 11.5585C15.6356 11.5585 16 11.923 16 12.3568V16.3655C16 16.7993 15.6529 17.1637 15.2017 17.1637Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.84381 12.9642 7.73969 12.9468 7.63557 12.8947C7.34056 12.7733 7.14967 12.4783 7.14967 12.1485L7.18437 .938127C7.18437 .504287 7.5488 .139862 7.98264 .139862C8.41648 .139862 8.7809 .504287 8.7809 .938127L8.7462 10.257L12.8416 6.33509C13.154 6.02273 13.6746 6.04008 13.9696 6.35244C14.282 6.66481 14.2646 7.18542 13.9523 7.48043L8.50325 12.7386C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.73969 12.9642 7.54881 12.8947 7.39262 12.7386L2.03037 7.53249C1.718 7.22012 1.70065 6.71687 2.01301 6.40451C2.32538 6.09214 2.82863 6.07479 3.141 6.38715L8.50325 11.5932C8.81562 11.9056 8.83297 12.4088 8.52061 12.7212C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-expand::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.64375 10.9125C6.9375 11.2062 6.93125 11.6812 6.64375 11.9687L2.57502 16H3.79375C4.20625 16 4.54376 16.3375 4.54376 16.75C4.54376 17.1625 4.20625 17.5 3.79375 17.5H.756264C.556264 17.5 .36876 17.4187 .22501 17.2812C.22501 17.2812 .206248 17.25 .193748 17.2375C.143748 17.1812 .100004 17.1125 .0625038 17.0437C.0375038 16.9687 .0187492 16.8937 .0187492 16.8187C.0187492 16.8 .0062561 16.7813 .0062561 16.7625V13.725C.0187561 13.3125 .356257 12.9875 .768757 12.9937C1.16876 13 1.48752 13.325 1.50002 13.725V14.9688L5.5875 10.9187C5.88125 10.6312 6.35 10.6312 6.64375 10.9187V10.9125ZM17.5063 .743732C17.5063 .543732 17.425 .356235 17.2875 .218735C17.2875 .218735 17.2562 .199998 17.2437 .193748C17.1875 .137498 17.1188 .0937347 17.0438 .0624847C16.9688 .0374847 16.8938 .0187492 16.8188 .0187492C16.8 .0187492 16.7813 .00623703 16.7625 .00623703H13.725C13.3125 .00623703 12.975 .343745 12.975 .756245C12.975 1.16874 13.3125 1.50623 13.725 1.50623H14.9688L11.1312 5.37498C10.8437 5.67498 10.8563 6.14999 11.1563 6.43124C11.45 6.71249 11.9063 6.70624 12.1938 6.43124L16.0125 2.575V3.79375C16.0125 4.20625 16.35 4.54372 16.7625 4.54372C17.175 4.54372 17.5125 4.20625 17.5125 3.79375V.756245L17.5063 .743732ZM16.7562 12.9688C16.3437 12.9688 16.0063 13.3063 16.0063 13.7188V14.8937L12.1938 10.925C11.9063 10.625 11.4375 10.6188 11.1375 10.9063C10.8375 11.1938 10.8313 11.6625 11.1188 11.9625L15 16.0062H13.7188C13.3063 16.0062 12.9688 16.3437 12.9688 16.7562C12.9688 17.1687 13.3063 17.5063 13.7188 17.5063H16.7562C16.85 17.5063 16.95 17.4875 17.0375 17.45C17.0875 17.425 17.1313 17.3937 17.175 17.3625C17.2063 17.3437 17.2438 17.325 17.275 17.3C17.3313 17.2375 17.375 17.1687 17.4125 17.1C17.4188 17.0875 17.4375 17.075 17.4438 17.0562C17.45 17.025 17.4563 16.9938 17.4625 16.9625C17.4813 16.9 17.5 16.8375 17.5 16.7687V13.725C17.5 13.3125 17.1687 12.975 16.7562 12.975V12.9688ZM.750008 4.53125C1.16251 4.53125 1.50002 4.19374 1.50002 3.78124V2.5L5.59376 6.43124C5.89376 6.71874 6.36251 6.70626 6.65001 6.41251C6.93751 6.11876 6.92501 5.64375 6.63126 5.35625L2.61251 1.49998H3.7875C4.2 1.49998 4.53751 1.16249 4.53751 .749989C4.53751 .337489 4.2 0 3.7875 0H.743752C.668752 0 .600004 .0187355 .531254 .0437355C.506254 .0499855 .481263 .0437477 .462513 .0562477C.443763 .0687477 .425015 .0812462 .406265 .0937462C.337515 .124996 .275004 .168741 .218754 .224991H.212498C.212498 .224991 .175 .28125 .15625 .3125C.11875 .3625 .0812477 .4125 .0562477 .46875C.0374977 .525 .0249992 .587499 .0187492 .643749C.0124992 .674999 0 .712482 0 .743732V3.78124C0 4.19374 .337508 4.53125 .750008 4.53125Z' fill='white'/%3E%3C/svg%3E%0A\")}@keyframes slow-scroll{100%{transform:translateY(-50%)}}.sc-hero{overflow:hidden;background-color:#212d35;background-repeat:repeat-y;background-image:url(\"data:image/svg+xml,%3Csvg width='389' height='17' viewBox='0 0 389 17' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M.638672 7.80824L.638672 9.2566C.638672 9.52364 .85538 9.74024 1.12262 9.74024H2.57204C2.83928 9.74024 3.05598 9.52364 3.05598 9.2566V7.80824C3.05598 7.54119 2.83928 7.32472 2.57204 7.32472L1.12262 7.32472C.85538 7.32472 .638672 7.54119 .638672 7.80824Z' fill='url(%23paint0_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.9639 7.80824V9.2566C10.9639 9.52364 11.1806 9.74024 11.4478 9.74024L12.8972 9.74024C13.1645 9.74024 13.3812 9.52364 13.3812 9.2566V7.80824C13.3812 7.54119 13.1645 7.32471 12.8972 7.32471L11.4478 7.32471C11.1806 7.32471 10.9639 7.54119 10.9639 7.80824Z' fill='url(%23paint1_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M21.2891 7.80823V9.2566C21.2891 9.52364 21.5058 9.74024 21.773 9.74024L23.2224 9.74024C23.4897 9.74024 23.7064 9.52364 23.7064 9.2566V7.80823C23.7064 7.54119 23.4897 7.32471 23.2224 7.32471L21.773 7.32471C21.5058 7.32471 21.2891 7.54119 21.2891 7.80823Z' fill='url(%23paint2_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M31.6143 7.80823V9.2566C31.6143 9.52364 31.831 9.74024 32.0982 9.74024H33.5476C33.8149 9.74024 34.0316 9.52364 34.0316 9.2566V7.80823C34.0316 7.54119 33.8149 7.32471 33.5476 7.32471L32.0982 7.32471C31.831 7.32471 31.6143 7.54119 31.6143 7.80823Z' fill='url(%23paint3_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M41.9395 7.80823V9.2566C41.9395 9.52364 42.1562 9.74024 42.4234 9.74024H43.8728C44.1401 9.74024 44.3568 9.52364 44.3568 9.2566V7.80823C44.3568 7.54119 44.1401 7.32471 43.8728 7.32471L42.4234 7.32471C42.1562 7.32471 41.9395 7.54119 41.9395 7.80823Z' fill='url(%23paint4_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M52.5076 7.80823V9.2566C52.5076 9.52364 52.7243 9.74024 52.9916 9.74024H54.441C54.7082 9.74024 54.9249 9.52364 54.9249 9.2566V7.80823C54.9249 7.54119 54.7082 7.32471 54.441 7.32471L52.9916 7.32471C52.7243 7.32471 52.5076 7.54119 52.5076 7.80823Z' fill='url(%23paint5_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M62.8331 7.80823V9.2566C62.8331 9.52364 63.0493 9.74024 63.3165 9.74024H64.7664C65.0332 9.74024 65.2504 9.52364 65.2504 9.2566V7.80823C65.2504 7.54119 65.0332 7.32471 64.7664 7.32471L63.3165 7.32471C63.0493 7.32471 62.8331 7.54119 62.8331 7.80823Z' fill='url(%23paint6_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M73.1583 7.80823V9.2566C73.1583 9.52364 73.3745 9.74024 73.6417 9.74024H75.0916C75.3584 9.74024 75.5756 9.52364 75.5756 9.2566V7.80823C75.5756 7.54119 75.3584 7.32471 75.0916 7.32471L73.6417 7.32471C73.3745 7.32471 73.1583 7.54119 73.1583 7.80823Z' fill='url(%23paint7_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M83.4835 7.80823V9.2566C83.4835 9.52364 83.6997 9.74024 83.9669 9.74024H85.4168C85.6836 9.74024 85.9008 9.52364 85.9008 9.2566V7.80823C85.9008 7.54119 85.6836 7.32471 85.4168 7.32471L83.9669 7.32471C83.6997 7.32471 83.4835 7.54119 83.4835 7.80823Z' fill='url(%23paint8_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M93.8087 7.80823V9.2566C93.8087 9.52364 94.0249 9.74024 94.2921 9.74024H95.742C96.0088 9.74024 96.226 9.52364 96.226 9.2566V7.80823C96.226 7.54119 96.0088 7.32471 95.742 7.32471L94.2921 7.32471C94.0249 7.32471 93.8087 7.54119 93.8087 7.80823Z' fill='url(%23paint9_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M104.134 7.80823V9.2566C104.134 9.52364 104.35 9.74024 104.617 9.74024H106.067C106.334 9.74024 106.551 9.52364 106.551 9.2566V7.80823C106.551 7.54119 106.334 7.32471 106.067 7.32471L104.617 7.32471C104.35 7.32471 104.134 7.54119 104.134 7.80823Z' fill='url(%23paint10_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M114.702 7.80823V9.2566C114.702 9.52364 114.918 9.74024 115.185 9.74024L116.635 9.74024C116.902 9.74024 117.119 9.52364 117.119 9.25659V7.80823C117.119 7.54119 116.902 7.32471 116.635 7.32471L115.185 7.32471C114.918 7.32471 114.702 7.54119 114.702 7.80823Z' fill='url(%23paint11_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M125.027 7.80823V9.25659C125.027 9.52364 125.243 9.74024 125.511 9.74024L126.961 9.74024C127.227 9.74024 127.445 9.52364 127.445 9.25659V7.80823C127.445 7.54119 127.227 7.32471 126.961 7.32471L125.511 7.32471C125.243 7.32471 125.027 7.54119 125.027 7.80823Z' fill='url(%23paint12_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M135.352 7.80823V9.25659C135.352 9.52364 135.569 9.74024 135.836 9.74024H137.286C137.553 9.74024 137.77 9.52364 137.77 9.25659V7.80823C137.77 7.54119 137.553 7.32471 137.286 7.32471L135.836 7.32471C135.569 7.32471 135.352 7.54119 135.352 7.80823Z' fill='url(%23paint13_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M145.678 7.80823V9.25659C145.678 9.52364 145.894 9.74024 146.161 9.74024H147.611C147.878 9.74024 148.095 9.52364 148.095 9.25659V7.80823C148.095 7.54119 147.878 7.32471 147.611 7.32471L146.161 7.32471C145.894 7.32471 145.678 7.54119 145.678 7.80823Z' fill='url(%23paint14_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M156.003 7.80823V9.25659C156.003 9.52364 156.219 9.74024 156.486 9.74024H157.936C158.203 9.74024 158.42 9.52364 158.42 9.25659V7.80823C158.42 7.54119 158.203 7.32471 157.936 7.32471L156.486 7.32471C156.219 7.32471 156.003 7.54119 156.003 7.80823Z' fill='url(%23paint15_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M166.328 7.80823V9.25659C166.328 9.52363 166.544 9.74024 166.811 9.74024H168.261C168.528 9.74024 168.745 9.52363 168.745 9.25659V7.80823C168.745 7.54119 168.528 7.32471 168.261 7.32471L166.811 7.32471C166.544 7.32471 166.328 7.54119 166.328 7.80823Z' fill='url(%23paint16_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M176.896 7.80823V9.25659C176.896 9.52363 177.112 9.74023 177.38 9.74023H178.83C179.096 9.74023 179.313 9.52363 179.313 9.25659V7.80823C179.313 7.54119 179.096 7.32471 178.83 7.32471L177.38 7.32471C177.112 7.32471 176.896 7.54119 176.896 7.80823Z' fill='url(%23paint17_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M187.221 7.80823V9.25659C187.221 9.52363 187.438 9.74023 187.705 9.74023H189.155C189.421 9.74023 189.639 9.52363 189.639 9.25659V7.80823C189.639 7.54119 189.421 7.32471 189.155 7.32471L187.705 7.32471C187.438 7.32471 187.221 7.54119 187.221 7.80823Z' fill='url(%23paint18_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M199.639 7.80824V9.2566C199.639 9.52364 199.855 9.74024 200.123 9.74024H201.572C201.839 9.74024 202.056 9.52364 202.056 9.2566V7.80824C202.056 7.54119 201.839 7.32472 201.572 7.32472L200.123 7.32472C199.855 7.32472 199.639 7.54119 199.639 7.80824Z' fill='url(%23paint19_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M209.964 7.80824V9.2566C209.964 9.52364 210.181 9.74024 210.448 9.74024L211.897 9.74024C212.164 9.74024 212.381 9.52364 212.381 9.2566V7.80824C212.381 7.54119 212.164 7.32471 211.897 7.32471L210.448 7.32471C210.181 7.32471 209.964 7.54119 209.964 7.80824Z' fill='url(%23paint20_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M220.289 7.80823V9.2566C220.289 9.52364 220.506 9.74024 220.773 9.74024L222.222 9.74024C222.49 9.74024 222.706 9.52364 222.706 9.2566V7.80823C222.706 7.54119 222.49 7.32471 222.222 7.32471L220.773 7.32471C220.506 7.32471 220.289 7.54119 220.289 7.80823Z' fill='url(%23paint21_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M230.614 7.80823V9.2566C230.614 9.52364 230.831 9.74024 231.098 9.74024H232.548C232.815 9.74024 233.032 9.52364 233.032 9.2566V7.80823C233.032 7.54119 232.815 7.32471 232.548 7.32471L231.098 7.32471C230.831 7.32471 230.614 7.54119 230.614 7.80823Z' fill='url(%23paint22_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M240.939 7.80823V9.2566C240.939 9.52364 241.156 9.74024 241.423 9.74024H242.873C243.14 9.74024 243.357 9.52364 243.357 9.2566V7.80823C243.357 7.54119 243.14 7.32471 242.873 7.32471L241.423 7.32471C241.156 7.32471 240.939 7.54119 240.939 7.80823Z' fill='url(%23paint23_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M251.508 7.80823V9.2566C251.508 9.52364 251.724 9.74024 251.992 9.74024H253.441C253.708 9.74024 253.925 9.52364 253.925 9.2566V7.80823C253.925 7.54119 253.708 7.32471 253.441 7.32471L251.992 7.32471C251.724 7.32471 251.508 7.54119 251.508 7.80823Z' fill='url(%23paint24_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M261.833 7.80823V9.2566C261.833 9.52364 262.049 9.74024 262.317 9.74024H263.766C264.033 9.74024 264.25 9.52364 264.25 9.2566V7.80823C264.25 7.54119 264.033 7.32471 263.766 7.32471L262.317 7.32471C262.049 7.32471 261.833 7.54119 261.833 7.80823Z' fill='url(%23paint25_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M272.158 7.80823V9.2566C272.158 9.52364 272.374 9.74024 272.642 9.74024H274.092C274.358 9.74024 274.576 9.52364 274.576 9.2566L274.576 7.80823C274.576 7.54119 274.358 7.32471 274.092 7.32471L272.642 7.32471C272.374 7.32471 272.158 7.54119 272.158 7.80823Z' fill='url(%23paint26_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M282.483 7.80823V9.2566C282.483 9.52364 282.7 9.74024 282.967 9.74024H284.417C284.684 9.74024 284.901 9.52364 284.901 9.2566V7.80823C284.901 7.54119 284.684 7.32471 284.417 7.32471L282.967 7.32471C282.7 7.32471 282.483 7.54119 282.483 7.80823Z' fill='url(%23paint27_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M292.809 7.80823L292.809 9.2566C292.809 9.52364 293.025 9.74024 293.292 9.74024H294.742C295.009 9.74024 295.226 9.52364 295.226 9.2566V7.80823C295.226 7.54119 295.009 7.32471 294.742 7.32471L293.292 7.32471C293.025 7.32471 292.809 7.54119 292.809 7.80823Z' fill='url(%23paint28_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M303.134 7.80823L303.134 9.2566C303.134 9.52364 303.35 9.74024 303.617 9.74024H305.067C305.334 9.74024 305.551 9.52364 305.551 9.2566V7.80823C305.551 7.54119 305.334 7.32471 305.067 7.32471L303.617 7.32471C303.35 7.32471 303.134 7.54119 303.134 7.80823Z' fill='url(%23paint29_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M313.702 7.80823L313.702 9.2566C313.702 9.52364 313.918 9.74024 314.185 9.74024L315.635 9.74024C315.902 9.74024 316.119 9.52364 316.119 9.25659V7.80823C316.119 7.54119 315.902 7.32471 315.635 7.32471L314.185 7.32471C313.918 7.32471 313.702 7.54119 313.702 7.80823Z' fill='url(%23paint30_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M324.027 7.80823V9.25659C324.027 9.52364 324.243 9.74024 324.511 9.74024L325.961 9.74024C326.227 9.74024 326.445 9.52364 326.445 9.25659V7.80823C326.445 7.54119 326.227 7.32471 325.961 7.32471L324.511 7.32471C324.243 7.32471 324.027 7.54119 324.027 7.80823Z' fill='url(%23paint31_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M334.352 7.80823V9.25659C334.352 9.52364 334.569 9.74024 334.836 9.74024H336.286C336.553 9.74024 336.77 9.52364 336.77 9.25659L336.77 7.80823C336.77 7.54119 336.553 7.32471 336.286 7.32471L334.836 7.32471C334.569 7.32471 334.352 7.54119 334.352 7.80823Z' fill='url(%23paint32_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M344.678 7.80823V9.25659C344.678 9.52364 344.894 9.74024 345.161 9.74024H346.611C346.878 9.74024 347.095 9.52364 347.095 9.25659L347.095 7.80823C347.095 7.54119 346.878 7.32471 346.611 7.32471L345.161 7.32471C344.894 7.32471 344.678 7.54119 344.678 7.80823Z' fill='url(%23paint33_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M355.003 7.80823V9.25659C355.003 9.52364 355.219 9.74024 355.486 9.74024H356.936C357.203 9.74024 357.42 9.52364 357.42 9.25659L357.42 7.80823C357.42 7.54119 357.203 7.32471 356.936 7.32471L355.486 7.32471C355.219 7.32471 355.003 7.54119 355.003 7.80823Z' fill='url(%23paint34_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M365.328 7.80823V9.25659C365.328 9.52363 365.544 9.74024 365.811 9.74024H367.261C367.528 9.74024 367.745 9.52363 367.745 9.25659V7.80823C367.745 7.54119 367.528 7.32471 367.261 7.32471L365.811 7.32471C365.544 7.32471 365.328 7.54119 365.328 7.80823Z' fill='url(%23paint35_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M375.896 7.80823V9.25659C375.896 9.52363 376.112 9.74023 376.38 9.74023H377.83C378.096 9.74023 378.313 9.52363 378.313 9.25659V7.80823C378.313 7.54119 378.096 7.32471 377.829 7.32471L376.38 7.32471C376.112 7.32471 375.896 7.54119 375.896 7.80823Z' fill='url(%23paint36_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M386.221 7.80823V9.25659C386.221 9.52363 386.438 9.74023 386.705 9.74023H388.155C388.421 9.74023 388.639 9.52363 388.639 9.25659V7.80823C388.639 7.54119 388.421 7.32471 388.155 7.32471L386.705 7.32471C386.438 7.32471 386.221 7.54119 386.221 7.80823Z' fill='url(%23paint37_linear_8295_70635)'/%3E%3Cdefs%3E%3ClinearGradient id='paint0_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint1_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint2_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint3_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint4_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint5_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint6_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint7_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint8_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint9_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint10_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint11_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint12_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint13_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint14_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint15_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint16_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint17_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint18_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint19_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint20_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint21_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint22_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint23_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint24_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint25_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint26_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint27_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint28_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint29_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint30_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint31_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint32_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint33_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint34_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint35_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint36_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint37_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3C/defs%3E%3C/svg%3E%0A\")}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:first-child{position:relative;z-index:3}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:absolute;height:100%;width:100%;top:0;left:-24px}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{content:\"\";display:block;z-index:1;position:absolute;top:-64px;left:0;width:150%;height:calc(100% + 160px);background-color:rgb(32 44 53 / .9)}.sc-body__content .heading-3-v2,.sc-hero__headline .heading-1-v2{text-transform:none}.sc-body__content span.snowflake-image-caption{display:block!important;font-style:italic}.sc-body__content .snowflake-text p+ul{margin-top:24px!important;padding-left:16px!important}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#e9eaeb!important;font-size:16px}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification.is-large .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#fff!important;font-size:18px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child{display:flex;justify-content:flex-start;align-items:center;gap:8px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child::before{content:\"\";display:inline-block;width:16px;height:16px;background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='16' viewBox='0 0 16 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M8 0C3.58146 0 0 3.58146 0 8C0 12.4185 3.58146 16 8 16C12.4185 16 16 12.4185 16 8C16 3.58146 12.4185 0 8 0ZM12.7184 5.91984L7.33471 11.3026C7.31293 11.3244 7.31293 11.3454 7.29198 11.3454L7.20653 11.4308C6.94933 11.688 6.54132 11.7525 6.21962 11.6235C6.11238 11.5808 6.00514 11.5163 5.9197 11.4308L5.83425 11.3454C5.83425 11.3454 5.83425 11.3236 5.81246 11.3236L3.28149 8.79347C2.93799 8.44997 2.93799 7.87107 3.28149 7.50664L3.36694 7.42119C3.71044 7.07769 4.28934 7.07769 4.65377 7.42119L6.58401 9.35143L11.3877 4.5477C11.7312 4.2042 12.3101 4.2042 12.6746 4.5477L12.76 4.63315C13.0826 4.99758 13.0828 5.55541 12.7184 5.91984Z' fill='%230E8A16'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-color:#fff;border-radius:100%}.sc-hero__byline{padding-top:8px}.sc-hero__byline p{color:#e2e2e2;margin-top:0!important}.sc-hero pre[class*=language-]{overflow:visible}.snowflake-code-snippet,.snowflake-code-snippet code,.snowflake-code-snippet pre{font-size:16px}.sc-hero__code-snippet:not(pre)\u003Ecode[class*=language-],.sc-hero__code-snippet pre[class*=language-]{background:0 0}.sc-hero__code-snippet{opacity:.8;background-color:transparent!important;position:absolute;top:0;right:0;width:100%;animation:240s linear 1s forwards slow-scroll}.sc-hero__button-container .snowflake-flexible-column-container-items{padding:0 0 24px;margin-top:-8px;margin-left:24px}.sc-sidebar__partner-logo{width:100%;max-width:140px;margin-top:8px}.sc-sidebar__partner-logo .cmp-image__image{border-radius:0}.sc-tag-cluster.snowflake-text ul{list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px;margin:0}.sc-tag-cluster.snowflake-text li{color:#373f41;border-radius:4px;display:inline-block;padding:6px;text-transform:uppercase;letter-spacing:1px;font-size:12px!important;line-height:12px!important;margin:0!important;background-color:#f3f3f3}.sc-body .share-icon svg{height:24px;cursor:pointer}.sc-body .share-icon svg:hover path{fill:var(--ui-02)}.sc-overview__webinar-promo-banner{align-items:center;border:1px solid #ccc;padding:var(--spacing-02)}.sc-overview__webinar-promo-banner .snowflake-content-chip-image{max-width:32px;margin-right:var(--spacing-02);line-height:0}.sc-overview__webinar-promo-banner .snowflake-content-chip-image__image,.summit-speaker-card .snowflake-card-v2-advanced-image__image{aspect-ratio:1}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{font-size:14px;font-family:Lato,sans-serif}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child){font-weight:400}.sc-overview__webinar-promo-banner .snowflake-content-chip-button .snowflake-button-container{font-size:14px!important}.diagram-group__button{position:absolute;bottom:24px;right:24px;background-color:#212c35!important}.section--mountains-bottom,.summit-hp-hero{position:relative}.sc-cert-banner{background-color:#212d35;border-radius:8px;padding:24px;overflow:hidden}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;align-items:center}:root{--text-secondary:#706f6f;--summit-bg-ltblue:#eaf8fd;--summit-bg-blue:#249edc;--summit-border:#d2d1d4;--summit-border-radius:8px;--summit-card-padding:32px;--summit-card-padding-sm:28px}.section--mountains-bottom::after,.section--mountains-bottom::before{content:\"\";display:block;position:absolute;bottom:-1px;max-width:400px;background-size:100% auto;height:100%;width:30%;line-height:0;background-repeat:no-repeat}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center;align-items:center}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;margin:0 8px!important}.button-group .snowflake-button-container{font-family:Texta,sans-serif}.section--summit-bg-ltblue{background-color:var(--summit-bg-ltblue)}.section--summit-bg-blue,.summit-hero-secondary{background-color:var(--summit-bg-blue)}.section--mountains-bottom::before{left:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M401.523 308.761H0V0L181.63 182.431L228.479 135.531L401.523 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom left}.section--mountains-bottom::after{right:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 308.761H401.523V0L219.893 182.431L173.044 135.531L0 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom right}.summit-hp-hero{overflow:hidden}.summit-hero__bg-video{position:absolute;top:50%;left:50%;width:120%;height:100%;opacity:.3;transform:translate(-50%,-50%)}.summit-hero__bg-svg,.summit-prefooter__bg-image,.summit-secondary-hero__bg-image{position:absolute;bottom:0;left:0;width:100%}.summit-hp-promo-banner__headline .heading-4-v2{font-weight:900}.summit-hero-secondary .hero-lottie__left{position:absolute;bottom:0;left:0;width:30%;line-height:0}.summit-timeline__card::after,.summit-timeline__card::before{bottom:0;left:50%;position:absolute;display:block;background-color:var(--ui-01);content:\"\"}.summit-hero-secondary .snowflake-text p{font-size:24px!important;line-height:32px!important;max-width:720px;margin:0 auto}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:center}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;max-width:25%}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid #fff}.summit-timeline__card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding);position:relative;background-color:#fff}.summit-timeline__card::before{width:20px;height:20px;border-radius:100%;transform:translate(-50%,50%)}.summit-timeline__card::after{width:3px;height:50px;transform:translate(-50%,100%)}.summit-timeline-card__icon{width:48px;height:48px}.summit-timeline-card__headline .heading-3-v2{font-size:32px}.faq-group{border:1px solid var(--ui-12);border-radius:4px;background-color:#fff}.faq-group__question{padding:24px}.faq-group__question:hover{color:var(--ui-01);cursor:pointer}.faq-group__question .heading-4-v2,.faq-group__question .heading-5-v2{position:relative;padding-right:64px}.faq-group__question .heading-4-v2::after,.faq-group__question .heading-5-v2::after{content:\"\";display:block;width:32px;height:32px;background-image:url(\"data:image/svg+xml,%3Csvg width='29' height='16' viewBox='0 0 29 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M14.16 14.6807C14.2537 14.7957 14.3719 14.8884 14.506 14.952C14.64 15.0157 14.7866 15.0487 14.935 15.0487C15.0834 15.0487 15.2299 15.0157 15.3639 14.952C15.498 14.8884 15.6162 14.7957 15.71 14.6807V14.6807L28.51 2.00068C29.07 1.43068 29.07 .92068 28.51 .44068C27.95 -.0393204 27.43 -.11932 26.96 .44068L14.94 12.0007L2.99996 .45068C2.90725 .322624 2.7855 .218374 2.6447 .146483C2.50389 .0745926 2.34805 .0371094 2.18996 .0371094C2.03187 .0371094 1.87603 .0745926 1.73522 .146483C1.59442 .218374 1.47267 .322624 1.37996 .45068C.819961 .93068 .819961 1.45068 1.37996 2.01068L14.16 14.6807Z' fill='black'/%3E%3C/svg%3E%0A\");background-size:80% auto;background-repeat:no-repeat;background-position:center;position:absolute;top:-2px;right:0;transition:.3s 150ms}.faq-group__question .heading-5-v2::after{top:-4px}.faq-group__answer{max-height:0;overflow:hidden;width:95%;padding:0 24px;transition:.5s}.faq-group__answer\u003Espan{display:block;padding-bottom:24px}.is-open .faq-group__answer{max-height:600px;transition:1s}.is-open .faq-group__question .heading-4-v2::after,.is-open .faq-group__question .heading-5-v2::after{transform:rotate(180deg);transition:.3s}.summit-agenda{box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);border-radius:8px;background-color:#fff;max-width:980px;margin-left:auto;margin-right:auto;padding:40px;width:90%}.agenda-item{border-radius:8px;background-color:#d4f0fa;padding:16px;border-left:4px solid var(--ui-01);position:relative}.summit-pricing-block__tile.is-past,.summit-pricing-block__tile.is-upcoming{pointer-events:none;border-color:#d2d1d4}p.agenda-item__time{width:25%;font-family:Texta!important;font-size:32px!important;font-weight:900!important;text-transform:uppercase!important;max-width:140px}@media screen and (max-width:991px){#partnerResources .section--resource-hub .snowflake-button-link .snowflake-button-container{font-size:14px!important;line-height:20px!important;margin-top:4px}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items{display:flex;flex-direction:column}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items\u003Ediv{width:100%}.sc-cert-banner__left{text-align:center}.sc-cert-banner__left .solution-center-hero__certification .snowflake-title-v2-line{justify-content:center}.summit-hero__bg-video{width:200%}.summit-leadership-grid .snowflake-flexible-column-container-items{grid-template-columns:repeat(2,1fr)}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:50%!important;max-width:50%!important}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:none!important}.summit-agenda{padding:24px}p.agenda-item__time{font-size:24px!important;width:auto;white-space:nowrap;padding-right:24px}}.agenda-item\u003Espan{display:flex;align-items:center}.summit-add-on-block,.summit-pricing-block{border:1px solid #d2d1d4;border-radius:8px;overflow:hidden;box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);background-color:#fff}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 20px 20px}.summit-pricing-block__tile{padding:24px 20px;border-radius:4px;background:#fff;border:1px solid var(--ui-01);position:relative;transition:background-color .3s}.summit-pricing-block__tile:hover{background-color:var(--ui-01);transition:background-color .3s}.summit-pricing-block__tile.is-past{background-color:#d4f0fa}.summit-pricing-block__tile:hover .black-blue-text-color .snowflake-title-v2-line{color:#fff!important;transition:color .3s}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::after,.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::after,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-pricing-block__tile.is-past .snowflake-content-chip-button,.summit-pricing-block__tile.is-upcoming .snowflake-content-chip-button,.summit-speaker-card .snowflake-card-v2-advanced-tag-indicator{display:none}.summit-pricing-block__tile.is-past .black-blue-text-color .snowflake-title-v2-line{color:#7cc7eb!important}.summit-pricing-block__tile.is-upcoming .black-blue-text-color .snowflake-title-v2-line{color:#8c8c8c!important}.summit-pricing-block__aside{background-color:#d4f0fa;border:1px solid #d2d1d4;border-radius:8px;padding:24px;width:100%}.summit-pricing-block__aside li::marker{color:var(--ui-01)}.summit-pricing-block__aside-headline .heading-5-v2{font-weight:900;margin-bottom:12px}.summit-pricing-block__header{background:#000;padding:24px 40px}.summit-pricing-block__header .heading-4-v2{font-weight:900;letter-spacing:.5px}.bwwidth100,.snowflake-mega-nav-dropdown-footer-content,.summit-pricing-block__tile .black-blue-text-color{width:100%}.summit-pricing-block__tile .heading-5-v2{position:static}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:first-child{text-transform:uppercase;font-weight:900!important;letter-spacing:.25px;font-size:24px!important}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:nth-child(2){margin-top:8px;font-family:Lato,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:16px}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{font-weight:900!important;font-size:40px!important}.snowflake-mega-nav-nav-item\u003Ea:hover .snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title,.summit-pricing-block__tile:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:var(--ui-01)!important}.summit-pricing-block__tile:hover:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:#fff!important}.summit-pricing-block__tile.is-past .heading-5-v2 span.snowflake-title-v2-line:last-child{text-decoration:line-through}.summit-pricing-block__tile .snowflake-content-chip-button{margin-top:0;white-space:nowrap;display:none}.snowflake-card-v2-advanced.no-link{pointer-events:none!important}.snowpro-card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding-sm);display:flex;height:100%}.snowpro-card__headline{margin:24px 0 12px}.snowpro-card__pricing{margin-top:48px}.snowpro-card .snowflake-text .snowpro-card__price{color:var(--ui-01);font-weight:900;font-size:40px!important;font-family:Texta,sans-serif}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid var(--summit-border)}.summit-stat-card{padding:0 40px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:first-child{font-size:64px;line-height:52px;margin-bottom:8px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:last-child{font-size:32px;line-height:30px;margin-bottom:16px}.summit-speaker-card .snowflake-card-v2-advanced-title{margin-bottom:var(--spacing-01)}.summit-add-on-card{padding:24px;border:1px solid #d2d1d4;border-radius:8px}.summit-add-on__subhead{padding-left:40px;padding-right:40px}.partner-card__logo-grid,.partner-card__logo-single{padding:40px}.partner-card__logo-grid .snowflake-image-container .cmp-image__image,.partner-card__logo-single .snowflake-image-container .cmp-image__image{border-radius:0;max-width:240px;margin:0 auto}.partner-card\u003E.container,.partner-card\u003E.container\u003E.aem-container,.partner-card\u003E.container\u003E.cmp-container{height:100%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;gap:24px;align-items:stretch}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap;gap:40px 24px;justify-content:center;align-items:center}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important}.partner-card{border-radius:8px;border:1px solid #d2d1d4;overflow:hidden;height:100%;background-color:#fff}.partner-card__header{padding:16px 24px;border-bottom:1px solid #d2d1d4}.partner-card__header.is-purple{background-color:#7d44cf}.partner-card__header h4{display:flex;flex-direction:row!important;align-items:center;gap:12px}.partner-card__header h4::before{vertical-align:middle;content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='black'/%3E%3C/svg%3E%0A\")}.partner-card__header.is-purple h4::before{background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='white'/%3E%3C/svg%3E%0A\")}.sf-blue-mountains{background-size:90% auto;background-repeat:no-repeat;background-position:center bottom;background-image:url(\"data:image/svg+xml,%3Csvg width='1361' height='410' viewBox='0 0 1361 410' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1360.25 410L1065.53 114.309L976.256 203.875L773.049 0L364.393 410H1360.25Z' fill='%233AA8DF'/%3E%3Cpath d='M274.778 410L137.467 272.238L.15625 410H274.778Z' fill='%233AA8DF'/%3E%3C/svg%3E%0A\")}.bwalignr,.main-pr-body .bwalignr{text-align:right}.bwblockalignl{margin-left:0;margin-right:auto}.bwcellpmargin{margin-top:0;margin-bottom:0}.bwlistdisc{list-style-type:disc}.bwpadb3{padding-bottom:4px}.bwpadb4{padding-bottom:5px}.bwpadl0{padding-left:0}.bwpadl3{padding-left:15px}.bwpadl6{padding-left:30px}.bwpadl9{padding-left:45px}.bwpadl12{padding-left:60px}.bwpadr0{padding-right:0}.bwtablemarginb{margin-bottom:10px}.bwvertalignb{vertical-align:bottom}.bwvertalignt{vertical-align:top}.bwsinglebottom{border-bottom:1pt solid #000}.bwdoublebottom{border-bottom:2.25pt double #000}.bwwidth1{width:1%}.bwwidth2{width:2%}.bwwidth6{width:6%}.bwwidth7{width:7%}.bwwidth8{width:8%}.bwwidth10{width:10%}.bwwidth12{width:12%}.bwwidth32{width:32%}.bwwidth44{width:44%}.bwwidth72{width:72%}.bwwidth97{width:97%}.main-pr-body{font-size:18px;line-height:26px}.main-pr-body img{display:block;width:100%;height:auto!important;border-radius:var(--small-border-radius)}.main-pr-body table{width:100%;display:block}.main-pr-body tbody{background-color:#f7f7f7}.main-pr-body .bwsinglebottom{border-bottom:1pt solid #000!important}.main-pr-body td.bwwidth44{padding-right:40px}.main-pr-body .bw-release-story{font-family:Lato,sans-serif}.main-pr-body .bw-release-story sup,.snowflake-mega-nav-dropdown-header-content-right a{white-space:nowrap}.main-pr-body .bw-release-story\u003E*,.main-pr-body\u003Espan\u003E*{margin-bottom:2rem!important}.snowflake-text.main-pr-body tbody,.snowflake-text.main-pr-body tbody p{font-size:14px!important;line-height:20px!important;width:100%;display:block}.press-body .snowflake-flexible-column-container-items{gap:var(--spacing-08)}.about-snowflake{border:1px solid #ccc;background-color:var(--ui-background-05);padding:24px;border-radius:8px;margin-top:0}.about-snowflake__logo{max-width:140px;margin-top:16px}.hero--press .snowflake-hero-system-inner{max-width:1408px;margin:0 auto!important}#arcticNavItem{flex-direction:column}#arcticNavItem::before{content:\"Featured Open Source Technologies\";display:block;margin-top:48px;margin-bottom:24px;font-size:16px!important;line-height:16px!important;font-weight:800!important;text-transform:uppercase}@media screen and (min-width:768px){.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:relative;height:100%;top:auto;left:auto;width:auto}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{background:linear-gradient(180deg,#202c35 -7.5%,#fff0 51.25%,#202c35 107.69%)}.sc-hero__byline\u003Espan{display:flex;flex-wrap:wrap}.sc-hero__byline p:not(:last-child)::after{content:\"|\";margin:0 12px;opacity:.5}.sc-hero__button-container .snowflake-flexible-column-container-items{position:absolute;bottom:0;padding:0;margin:0 24px 0 0}.sc-hero__button-container .hero-watch-the-demo{padding:12px 16px!important;float:right;margin-bottom:48px;background-color:rgb(35 45 54 / .8)}.summit-overview-stat{padding:0 40px}.summit-timeline{border-bottom:3px solid var(--ui-01);margin-bottom:64px}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 40px 40px}#arcticNavItem::before{font-size:12px!important;margin-bottom:8px;margin-top:16px}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{line-height:20px!important}.snowflake-card .heading-2.snowflake-title-line{font-size:24px!important;line-height:28px!important}}@media screen and (min-width:992px){.hp-hero__eyebrow a{gap:12px;margin-left:0;margin-right:0}.hp-hero__eyebrow a::after{content:\"\";background-image:url(\"data:image/svg+xml,%3Csvg width='6' height='11' viewBox='0 0 6 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M5.49134 5.79438C5.53447 5.75922 5.56923 5.71489 5.5931 5.66463C5.61697 5.61436 5.62935 5.55941 5.62935 5.50376C5.62935 5.44811 5.61697 5.39316 5.5931 5.34289C5.56923 5.29263 5.53447 5.2483 5.49134 5.21314L.736339 .413136C.522589 .203135 .331339 .203135 .151339 .413136C-.0286612 .623135 -.0586612 .818135 .151339 .994386L4.48634 5.50188L.155089 9.97938C.107068 10.0142 .0679743 10.0598 .0410153 10.1126C.0140562 10.1654 0 10.2238 0 10.2831C0 10.3424 .0140562 10.4009 .0410153 10.4537C.0679743 10.5065 .107068 10.5521 .155089 10.5869C.335089 10.7969 .530089 10.7969 .740089 10.5869L5.49134 5.79438Z' fill='black'/%3E%3C/svg%3E%0A\");display:inline-block;width:12px;height:12px;background-repeat:no-repeat;background-size:auto 100%;background-position:left center}.promo-banner--homepage{padding-top:32px}.homepage-banner-offset-container::after{height:50%}#storyHighlights{padding:2rem}.body-display-v2.snowflake-quote-item-quote-text{line-height:28px!important}.snowflake-hero-system-headline .heading-1-v2{line-height:48px;font-size:54px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-content{flex-direction:row;justify-content:space-between;align-items:center;width:100%}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{flex-direction:row}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child)::before{content:\"|\";margin:0 6px}.sc-cert-banner{padding:40px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{margin:0!important;width:50%}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;padding-right:24px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:240px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{width:70%;padding-left:40px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{width:30%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important;display:flex}.summit-pricing-block__tile .snowflake-content-chip-content{display:flex;flex-direction:row;align-items:center;width:calc(100% - 200px)}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{position:absolute;top:50%;transform:translate(0,-50%);right:40px}.press-body\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:sticky;top:120px}.snowflake-mega-nav-navigation-title:hover{color:var(--ui-01)}}@media screen and (min-width:1024px){.about-snowflake{padding:28px}.about-snowflake__logo{max-width:none;padding:0 0 0 48px;margin-bottom:0}.hero--press .snowflake-hero-system-layout-70-30 .snowflake-hero-system-content-container{width:85%}.snowflake-hero-system{padding-bottom:var(--spacing-04);padding-top:var(--spacing-07)}.hero--press .display-2-v2{font-size:64px;line-height:56px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container{flex-direction:row;flex-wrap:nowrap;align-items:center}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:280px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;margin-bottom:0!important}#polarisNavItem{margin-top:40px}.snowflake-mega-nav-nav-item-description{line-height:18px!important}.snowflake-mega-nav-column-items{gap:var(--spacing-01);grid-gap:var(--spacing-01)}.snowflake-mega-nav-navigation-title{text-transform:none}}div[id*=blueIcon] .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01);padding:8px}div[id*=blueIcon]:hover .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01)!important}.snowflake-mega-nav-nav-item-icon__inner{border-radius:4px;background:var(--ui-background-05);padding:6px}.snowflake-mega-nav-nav-item:hover .snowflake-mega-nav-nav-item-icon__inner{background:#fff!important}.snowflake-mega-nav-nav-item-icon.snowflake-image-container{height:40px;width:40px}.snowflake-mega-nav-dropdown-footer-links\u003E.snowflake-button-link\u003E.snowflake-button-container{font-size:16px!important;font-family:Texta!important;font-weight:800!important}.snowflake-mega-nav-dropdown-footer-icon.snowflake-image-container{margin-right:8px;width:40px!important;height:40px!important}#viewAllCapabilities a:hover{background:0 0!important}#platformFooter .snowflake-title-v2 .snowflake-title-v2-line:last-child{font-family:Lato;font-size:14px;font-weight:500}#platformFooter .snowflake-mega-nav-dropdown-footer-links{flex-grow:1;justify-content:flex-end;align-items:center}#platformFooter .snowflake-mega-nav-dropdown-footer-content{flex-direction:row}#offset,#open-source{flex-direction:column;border-top:1px solid #ccc}#offset::before,#open-source::before{content:\" \";display:block;width:100%;font-weight:800!important;font-size:12px!important;line-height:14px;text-transform:uppercase;white-space:nowrap;margin-top:16px;margin-bottom:8px}#open-source::before{content:\"Open Source Technologies\"}.snowflake-mega-nav-dropdown-menu-close-button{margin:var(--spacing-04) 0 var(--spacing-03)}.snowflake-mega-nav-column{gap:var(--spacing-02)!important}.snowflake-mega-nav-nav-item\u003Ea{width:100%;margin-left:-8px;padding:8px;border-radius:4px}.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:var(--ui-background-05)}.snowflake-mega-nav-nav-item-description{margin-top:2px;display:block}#promobanner_overflowBottomDarkBlue::before{content:'';display:block;position:absolute;bottom:0;left:0;width:100%;height:50%;background:#212d35}#promobanner_overflowTopDarkBlue::before{content:'';display:block;position:absolute;top:0;left:0;width:100%;height:50%;background:#212d35}.overview-card\u003Ediv{box-shadow:0 0 14px 0 rgba(0,0,0,.10);background-color:#fff;border-radius:16px;overflow:hidden}.overview-card-text{padding:40px}.overview-card-image img{border-radius:0 !important}.overview-card-text h3,.overview-card-text .heading-3-v2{font-size:18px;line-height:1.1;margin-top:0}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"mega_header":{"additionalClasses":"heap-nav-header","layout":"SIMPLE","id":"container-27595acbc0",":type":"snowflake-site/components/mega-header",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-dee2bc75f7",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-ccd1e26616","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-2da7f622ec",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"additionalClasses":"nav-platform-sidebar","numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-8a0f4f0db3",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-da84c5d91b","additionalClasses":"nav-item__platform-parent is-platform","linkDescription":"Develop AI products, apps and more on a fully managed platform that securely connects businesses globally — across any type or scale of data.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/platform/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The Snowflake Platform"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-3de58af300","additionalClasses":"nav-item nav-item--si is-si","linkDescription":"All your knowledge. One trusted enterprise agent.","flag":"NOW GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-cowork/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoWork"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_836345186":{"id":"nav-item-aa8867036c","additionalClasses":"blue-icon is-analytics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/analytics/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Analytics"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2":{"id":"nav-item-7ff1fe4b35","additionalClasses":"blue-icon is-ai","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/ai/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1314771042":{"id":"nav-item-6ad25a3316","additionalClasses":"blue-icon is-data-eng","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/data-engineering/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Data Engineering"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634":{"id":"nav-item-51cc5458c2","additionalClasses":"blue-icon is-apps-collab","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/applications-and-collaboration/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Applications & Collaboration"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_2013333117":{"id":"nav-item-0c89462cd7","additionalClasses":"blue-icon is-transactions","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/transactions/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Transactions"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646","nav_item","nav_item_copy_copy_2_836345186","nav_item_copy_copy_2","nav_item_copy_copy_2_1314771042","nav_item_copy_144634","nav_item_copy_144634_2013333117"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","navColumnTitle":"Featured Capabilities","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-f4c1d14623",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_212715":{"id":"nav-item-3d914bc5b4","additionalClasses":"is-cortex-code","linkDescription":"Snowflake-native AI coding agent ","flag":"New","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-coco/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoCo"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-74b11c6322","additionalClasses":"is-cortex-ai","linkDescription":"Instant access to industry-leading LLMs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/cortex/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Cortex AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590635":{"id":"nav-item-d548b3821d","additionalClasses":"is-marketplace","linkDescription":"Third-party data sources connected within minutes","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/marketplace/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketplace"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-bfe602e765","additionalClasses":"is-snowpark","linkDescription":"Libraries and code execution environments that run Python and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/snowpark/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowpark"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_983061516":{"id":"nav-item-d44d91e30a","additionalClasses":"is-streamlit","linkDescription":"Framework for transforming Python scripts into web apps","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Streamlit"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_212715","nav_item","nav_item_copy_660590635","nav_item_copy_660590","nav_item_copy_660590_983061516"]},"nav_column_692142673":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-03bbae63d0",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_660590_1739526127":{"id":"nav-item-e19eabc3c3","additionalClasses":"is-postgres","linkDescription":"Fully compatible open source Postgres running on Snowflake","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/postgres/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Postgres"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-9bb4e8f53c","additionalClasses":"is-dcr","linkDescription":"Streamlined model development and MLOps from a centralized UI","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/end-to-end-ml-workflows/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake ML"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_212715":{"id":"nav-item-ff1ecef6e4","additionalClasses":"is-openflow","linkDescription":"Effortless data movement for integrations","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/openflow/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Openflow"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-cf5f03d096","additionalClasses":"is-notebooks","linkDescription":"Interactive dev environment for data and AI teams","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/notebooks/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Notebooks"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-71743f8ff5","propertiesId":"workload-nav-1","additionalClasses":"is-native-apps","linkDescription":"End-to-end, Snowflake-native app creation and distribution","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/native-apps/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Native Apps"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_660590_1739526127","nav_item_copy_185565","nav_item_copy_212715","nav_item_copy_660590","nav_item_258535199"]},"nav_column_782221091":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-10f0017dfd",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-3b83df000a","additionalClasses":"is-light-gray-icon is-horizon-catalog","linkDescription":"Universal AI catalog","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Catalog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_1293798742":{"id":"nav-item-4c1e25a9a3","additionalClasses":"is-snowflake-ml","linkDescription":"Governed context layer that keeps AI, BI and data apps working from one truth","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon-context/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Context"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c":{"id":"nav-item-c0bbb45e2d","additionalClasses":"is-unistore","linkDescription":"Unify transactional and analytical workloads in Snowflake for enhanced simplicity","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/unistore/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Unistore"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1443811525":{"id":"nav-item-64745b042f","additionalClasses":"is-observe","linkDescription":"AI-powered observability for faster production troubleshooting","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/observe/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Observe"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1006104884":{"id":"nav-item-cb1b63b1b4","additionalClasses":"is-observe","linkDescription":"Use any engine on a single governed data copy","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/use-cases/interoperable-lakehouse/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Interoperable Lakehouse"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item_copy_660590_1293798742","nav_item_511717659_c","nav_item_511717659_c_1443811525","nav_item_511717659_c_1006104884"]}},":itemsOrder":["nav_column","nav_column_copy_copy","nav_column_692142673","nav_column_782221091"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Product"},"nav_dropdown_menu_2":{"id":"nav-dropdown-menu-cdad3bc324","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-99772ba2b4",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"navColumnTitle":"INDUSTRIES","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-359816c30a",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_361384_2056203141":{"id":"nav-item-2f459675cd","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"All Industries"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-e1527d199d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/advertising-media-entertainment/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Advertising, Media & Entertainment"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-dced232d56","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/financial-services/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Financial Services"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-42018a968c","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/healthcare-and-life-sciences/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Healthcare & Life Sciences"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1533429516":{"id":"nav-item-fc8f1ca526","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/manufacturing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Manufacturing"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1444458226":{"id":"nav-item-166e7a1dd8","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/public-sector/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Public Sector"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1149488919":{"id":"nav-item-e36114eadb","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Retail & Consumer Goods"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_57417040":{"id":"nav-item-12f93e7792","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Technology"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384674":{"id":"nav-item-0ee828ce92","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/telecom/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Telecom"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384":{"id":"nav-item-a5b2d72ced","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/travel-hospitality/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Travel & Hospitality"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_361384_2056203141","nav_item","nav_item_copy","nav_item_copy_1970515619","nav_item_copy_1533429516","nav_item_copy_1444458226","nav_item_copy_1149488919","nav_item_copy_57417040","nav_item_copy_361384674","nav_item_copy_361384"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"nav_column_copy":{"navColumnTitle":"DEPARTMENTS","numberOfSubColumns":"one-column","minWidth":"160","layout":"SIMPLE","id":"container-6de1b3481e",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-674a5af50d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/finance/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Finance"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-38b9a26f1e","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/information-technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"IT"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-a1da064655","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/marketing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketing"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]},"nav_column_833417450":{"navColumnTitle":"Enablement Solutions","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-a55de86d27",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_107772":{"id":"nav-item-17fc49c350","linkDescription":"Confident migration to a unified platform","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/migrate-to-the-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Migrate to the AI Data Cloud"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_107772/icon.coreimg.svg/1723828484100/nav-icon-cloud.svg","alt":"Cloud icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-493b8fc61f","linkDescription":"Snowflake experts to help you accelerate and achieve business goals","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/services-delivery/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Services Delivery"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_copy/icon.coreimg.svg/1768354429188/nav-icon--migrate.svg","alt":"Migrate icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_107772","nav_item_copy_copy"]},"nav_column_copy_copy":{"navColumnTitle":"PARTNER SOLUTIONS","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-23ddf607a3",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-5ac8f7ab98","linkDescription":"Programs with product, solutions and cloud partners","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Partner Network"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1723828498700/nav-icon--partner-network.svg","alt":"Partner Network icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-dd39aa3cd7","linkDescription":"Partners, apps and solutions for enhanced deployment","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/all-partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Partner Finder"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1726173927645/nav-icon--partner-finder.svg","alt":"Partner Finder icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-56bbb5acfe","linkDescription":"Live and virtual events","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/event-partnership-opportunities/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Event Partnership Opportunities"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy_1970515619/icon.coreimg.svg/1726173935655/nav-icon--events.svg","alt":"Calendar icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-c6282152f5","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-7d5d470358",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-2835f4eb73",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-afd6b512d7","additionalClasses":"nav-item__platform-parent-why-sf","linkDescription":"Collaborate locally and globally to reveal new insights, create previously unforeseen business opportunities, and identify your customers with seamless experiences.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Why Snowflake"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","numberOfSubColumns":"two-columns","maxWidth":"1200","layout":"SIMPLE","id":"container-0d6bf5fe2f",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-a5149c41de","propertiesId":"testID","linkDescription":"Case studies and videos showcasing how global organizations use Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Customers"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1739839279367/nav-icon--partner-network.svg","alt":"Customer icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-6ea7b31f20","propertiesId":"workload-nav-1","linkDescription":"Learn how to connect, share and integrate the data and apps on the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/what-is-data-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The AI Data Cloud Explained"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_258535199/icon.coreimg.svg/1739840490955/nav-icon-cloud.svg","alt":"Cloud icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-617355e342","linkDescription":"Comprehensive security through built-in features, robust cloud infrastructure protection, and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/snowflake-security-hub/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Security Hub"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565/icon.coreimg.svg/1758909528089/user-security-admins-ciso-icon.svg","alt":"User with security lock icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-74f666f74a","additionalClasses":"is-light-gray-icon","linkDescription":"Maximize economic value through minimizing TCO and continuously optimizing price for performance","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/pricing-options/cost-and-performance-optimization/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Cost and Performance Optimization"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1758909542267/nav-icon-cost-optimization-performance.svg","alt":"Cost Optimization icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_903555964":{"id":"nav-item-429084e0b1","linkDescription":"Startups building applications in the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/startup-program/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Startups"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565_903555964/icon.coreimg.svg/1758732224323/launch.svg","alt":"Launch","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_258535199","nav_item_copy_185565","nav_item_copy","nav_item_copy_185565_903555964"]}},":itemsOrder":["nav_column","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Why Snowflake"},"item_1719961362824":{"id":"nav-dropdown-menu-91230f5774","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-29a9f87281",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","minWidth":"124","layout":"SIMPLE","id":"container-e115c791d3",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-ab2ca5abd1","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Blog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_180298689":{"id":"nav-item-fd6a5c870f","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/events/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Events"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_1639361946":{"id":"nav-item-7928b10b52","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/support/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Support"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_680912746":{"id":"nav-item-ccac6c0dca","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/contact/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Contact us"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_180298689","nav_item_1639361946","nav_item_680912746"]},"nav_column_44600420__826130542":{"navColumnTitle":"Learn","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-15662dc205",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-e30ea343ca","linkDescription":"Ebooks, videos, white papers and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Resource Library"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy/icon.coreimg.svg/1736877128196/nav-icon--notebooks.svg","alt":"Notebooks icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-ea00a796ab","linkDescription":"Overview of Snowflake's educational offerings","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/training/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Training"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item/icon.coreimg.svg/1722385094416/nav-icon--training.svg","alt":"Training icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-5094c1720a","linkDescription":"Expert-led discussions and demos across industries and use cases","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Webinars"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_144634_1984107859/icon.coreimg.svg/1759424691990/nav-icon--webinars.svg","alt":"Webinars icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-36e76b1450","linkDescription":"Snowflake's technical industry professional certifications","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/certifications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Certifications"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_1438098918/icon.coreimg.svg/1722382780833/nav-icon--cert.svg","alt":"Certification icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-00c756246b","linkDescription":"Weekly product demos showcasing key features and live Q&A ","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/demo/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Live Demos"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_143809/icon.coreimg.svg/1759424359543/nav-icon--live-demo.svg","alt":"Live Demo icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-64af8c942c","linkDescription":"Training courses for all levels, on-demand or instructor-led","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://learn.snowflake.com/en/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowflake University"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890638/icon.coreimg.svg/1722382769808/nav-icon--education.svg","alt":"Education icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-2534a06b89","linkDescription":"Instructor-led virtual workshops for exploring key Snowflake features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/virtual-hands-on-lab/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Hands-On Labs"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_189945/icon.coreimg.svg/1759388182903/nav-icon--labs.svg","alt":"Hands-on Labs icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890":{"id":"nav-item-d14e2c2318","linkDescription":"Academic papers written by Snowflake researchers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/publications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Research Publications"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890/icon.coreimg.svg/1756326371387/copy.svg","alt":"Copy","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890_930852828":{"id":"nav-item-cc9f486c3a","linkDescription":"Informative articles about AI and data topics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/fundamentals/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Fundamentals"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890_930852828/icon.coreimg.svg/1756853637155/data-sheet.svg","alt":"Document with list","lazyEnabled":true,"height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item","nav_item_copy_144634_1984107859","nav_item_copy_1438098918","nav_item_copy_143809","nav_item_copy_333890638","nav_item_copy_189945","nav_item_copy_333890","nav_item_copy_333890_930852828"]}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"]},"nav_promo_section":{"id":"nav-promo-section-e2a005153b","experience_fragment_1":{"id":"experiencefragment-7fe4ef5377","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master1/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-571e52f203",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-59ff9b08ca","openInNewWindow":true,"layout":"horizontal","headline":"The Modern Marketing Data Stack 5th Edition","description":"AI agents are changing the marketing stack. See how to govern the shift. ","linkTitle":"Learn more","linkUrl":"/en/the-modern-marketing-data-stack-report/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b3030d24-fd50-45e6-bfe6-9520d3eb46d8/web-inside-the-mmds-5th-960x540.png?quality=85&preferwebp=true","alt":"MMDS report 5th edition","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf"},"experience_fragment_2":{"id":"experiencefragment-4369e9f25a","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-2/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-7f1f76c760",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-4ad410942f","openInNewWindow":true,"layout":"horizontal","headline":"The ROI of Gen AI and Agents 2026","description":"Discover how 92% of early adopters are achieving positive ROI with gen AI.","linkTitle":"Learn More","linkUrl":"/en/lp/radical-roi-generative-ai/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0c15edae-1a97-4739-8b16-c7f3941a6d9e/web-roi-of-gen-ai-and-agents-2026-r02-960x540.png?quality=85&preferwebp=true","alt":"roi of gen ai and agents","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf"},"experience_fragment_3":{"id":"experiencefragment-2a9d1e08ef","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-3/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-b260f10f3f",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-f1bd3914ce","openInNewWindow":true,"layout":"horizontal","headline":"Startup 2026: AI Agents Mean Business","description":"Venture leaders weigh in on agentic AI. ","linkTitle":"Learn more","linkUrl":"/en/lp/building-startup-ai-age/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a320b404-dca1-4477-b033-c79708538657/web-startup-2026-960x540.png?quality=85&preferwebp=true","alt":"alt","lazyEnabled":true,"height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf"},":type":"snowflake-site/components/nav/nav-promo-section"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Resources"},"item_1719963657751":{"id":"nav-dropdown-menu-d1956c11b6","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-cff6aa48b5",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy_copy":{"navColumnTitle":"Build","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-bb9db9478a",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-dd336ce95f","propertiesId":"testID","linkDescription":"Overview of the dev resources you need to build and scale","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Developers"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1731362494574/nav-icon--devs.svg","alt":"Developers icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-30a55e47cf","linkDescription":"Reference architectures, use cases and best practices","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/guides/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Developer Guides"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy_1855651246/icon.coreimg.svg/1761677891705/nav-icon--solution-center.svg","alt":"Solution Center icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-0dbb419a93","additionalClasses":"is-light-gray-icon","linkDescription":"The latest software versions, drivers, libraries and relevant docs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/downloads/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Downloads"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1731362660050/nav-icon-download.svg","alt":"Download icon","lazyEnabled":true,"height":"28","width":"28",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246","nav_item_copy"]},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Learn","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-e60908e4b4",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-9cc257a494","propertiesId":"testID","linkDescription":"Reference docs, guides, tutorials and announcements","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://docs.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Documentation"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item/icon.coreimg.svg/1731361950527/nav-icon--docs.svg","alt":"Docs icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-c06c508093","additionalClasses":"is-light-gray-icon","linkDescription":"Key projects Snowflake engineers maintain and support","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/open-source/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Open Source"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy/icon.coreimg.svg/1731365437016/nav-icon-open-source.svg","alt":"Open Source icon","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-475fbd3b96","additionalClasses":"is-light-gray-icon","linkDescription":"Online and in-person classes and workshops to upskill on Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/northstar/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Builder Education"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy_copy/icon.coreimg.svg/1731362475640/nav-icon--northstar.svg","alt":"Northstar logo","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_copy"]},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-c2498f88bf",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-dfc8400814","propertiesId":"testID","linkDescription":"Snowflake’s technical leaders on what, why and how they build features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/engineering-blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Engineering Blog"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item/icon.coreimg.svg/1757101368571/nav-icon--developer-center.svg","alt":"Developers icon","lazyEnabled":true,"height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-781a8602c9","linkDescription":"Tips, tricks and discussion with fellow Snowflake developers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://community.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Community"},"icon":{"id":"icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item_copy_1855651246/icon.coreimg.svg/1731362644348/nav-icon--partner-network.svg","alt":"Partner Network icon","lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246"]}},":itemsOrder":["nav_column_copy_copy","nav_column_copy_copy_1367930678","nav_column_copy_copy_1101894776"]},"nav_promo_section":{"id":"nav-promo-section-2afecc6d03","experience_fragment_1":{"id":"experiencefragment-b500b20064","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-5/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-c9e91c193d",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-07d5bf0bf6","openInNewWindow":false,"layout":"horizontal","headline":"Get started with your first Snowflake Notebook","description":"Write and execute code, visualize results, and tell the story of your analysis all in one place.","linkTitle":"Learn More","linkUrl":"/en/developers/solutions-center/getting-started-with-your-first-snowflake-notebook-project/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dc7e334a-c38b-4283-b1de-fcf829952eef/nav-promo-first-notebook.jpg?quality=85&preferwebp=true","alt":"alt","lazyEnabled":true,"height":"210","width":"415",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf"},"experience_fragment_2":{"id":"experiencefragment-7592f2f542","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-7a10429f08",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-3ed1e6e3f4","openInNewWindow":true,"layout":"horizontal","headline":"Northstar Builder Workshops","description":"Join other developers as you roll up your sleeves and explore the possibilities of Snowflake.","linkTitle":"Learn More","linkUrl":"/en/nav-promos/northstar-builders-workshop/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14341ced-bc5e-4a29-9762-b7857f6cadfc/nav-promo-northstar.jpg?quality=85&preferwebp=true","alt":"Snowflake Northstar logo","lazyEnabled":true,"height":"700","width":"1440",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf"},":type":"snowflake-site/components/nav/nav-promo-section"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Developers"},"item_1718247180324":{"id":"nav-dropdown-menu-de6e9c0160","enableDropdown":false,"link_url":"/en/pricing-options/",":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Pricing"}},":itemsOrder":["item_1719963657751_c_663444255","nav_dropdown_menu_2","item_1719963657751_c","item_1719961362824","item_1719963657751","item_1718247180324"]},"languagenavigation":{"id":"language-navigation-79d0deb440","languageNavItems":[{"title":"English","path":"/en/developers/guides/dcdf-incremental-processing/","locale":"en","active":true},{"title":"日本語","path":"/ja/","locale":"ja","active":false},{"title":"한국어","path":"/ko/","locale":"ko","active":false},{"title":"中文（简体）","path":"/zh_cn/","locale":"zh-cn","active":false},{"title":"Português","path":"/pt_br/","locale":"pt-br","active":false},{"title":"Deutsch","path":"/de/","locale":"de","active":false},{"title":"Français","path":"/fr/","locale":"fr","active":false},{"title":"Español","path":"/es/","locale":"es","active":false},{"title":"Italiano","path":"/it/","locale":"it","active":false}],":type":"snowflake-site/components/nav/language-navigation"},"button_1177328691":{"id":"button-57257ba977","heapButtonClasses":["mega-nav__sign-in"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://app.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-link snowflake-button-black snowflake-button-compact","text":"Sign in"},"button":{"id":"button-c902760b70","heapButtonClasses":["contact_nav","heap-nav-contact"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"/en/contact-sales/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-secondary snowflake-button-blue snowflake-button-compact","text":"CONTACT SALES"},"button_288358396":{"id":"button-15ae0e0c62","heapButtonClasses":["start_for_free_nav","heap-nav-start-for-free"],"showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://signup.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","appliedCssClassNames":"snowflake-button-primary snowflake-button-blue snowflake-button-compact","text":"start for free"}},":itemsOrder":["nav_mega","languagenavigation","button_1177328691","button","button_288358396"],"appliedCssClassNames":"snowflake-header-container white"}},":itemsOrder":["markup_editor","mega_header"]},"image":{":type":"nt:unstructured"},"cq:targetMetadata":{"cq:targetStatus":"OUT_OF_SYNC","cq:exportTime":1781280015540,"cq:targetOfferId":860250,":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:targetMetadata"],"classNames":"aem-xf"},"markup_editor_1950346551":{"id":"markup-editor-9022c7ec58","title":" ","cssContent":".snowflake-markdown-table code[class*=language-],.snowflake-markdown-table code[class*=language-],.snowflake-markdown .snowflake-text code[class*=language-],.snowflake-markdown .snowflake-text pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-31a9b0de5c","quickstartHeroTitle":{"lines":["Getting Started with DCDF Data Architecture Incremental Processing & Logical Partitions"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"melindawebster","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Transformation","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/transformation","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-bc06c2a599","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/dcdf-incremental-processing"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Getting Started with DCDF Data Architecture Incremental Processing & Logical Partitions","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/dcdf-incremental-processing","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"isDeveloperGuidesPage":false,"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/dcdf-incremental-processing",":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""}},"flexible_column_cont":{"id":"flexible-column-container-31e3d59ef0","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-c3b3940ba0",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-52ec7edd0a","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThe topics of covered in this guide were originally presented in Episode 2 of Snowflake's Data Cloud Deployment Framework (DCDF) webinar series. \u003Ca href=\"/webinar/for-customers/applying-architectural-patterns-to-solve-business-questions-2023-01-11/\"\u003EDCDF Webinar Episode 2\u003C/a\u003E focused on the ELT implementation patterns to operationalize data loading, centralize the management of data transformations and restructure the data for optimal reporting and analysis.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, we will focus on the actual SQL code templates for ingesting, transforming, and restructuring data into the presentation layer using incremental processing and logical partition definitions.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECaveat:\u003C/strong\u003E This DCDF Data Architecture quickstart and template scripts are for illustrative purposes only. These scripts can be run in any Snowflake account in order to reinforce the concepts and patterns presented in DCDF Webinar Episode 2. They can be customized for your own business use cases.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake account.  Existing or a free \u003Ca href=\"https://trial.snowflake.com/\"\u003ETrial Account\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EACCOUNTADMIN role (Note: Free Trial accounts provide this automatically)\u003C/li\u003E\u003Cli\u003EWorking knowledge of Snowflake database objects and the Snowflake Web UI/Snowsight.\u003C/li\u003E\u003Cli\u003EFamiliarity with Snowflake and Snowflake objects.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\"\u003ESnowSQL installed\u003C/a\u003E and configured to your Snowflake account.\u003C/li\u003E\u003Cli\u003EFamiliarity and understanding of SQL syntax\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, we will build on the topics discussed in the webinar by loading and executing the SQL code used in the presentation.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDCDF Data Architecture\u003C/li\u003E\u003Cli\u003EData Ingestion\u003C/li\u003E\u003Cli\u003EData Transformation\u003C/li\u003E\u003Cli\u003EIncremental processing and Logical Partitions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Account or \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ETrial Account\u003C/a\u003E, any edition will do as the scripts will run on any edition.\u003C/li\u003E\u003Cli\u003ELogin to Snowflake account that has ACCOUNTADMIN role access (Note: Free Trial accounts provide this automatically)\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowsql-install-config.html\"\u003ESnowSQL installed\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ESnowsight or Classic Ui will be used in these examples.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n","\u003Cp\u003EAn extensible ELT data pipeline, using logical partitions, that employs repeatable patterns for ingestion, transformation and consumable data assets.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDCDF Data Architecture\u003C/h2\u003E\n","\u003Cp\u003ELet's review the DCDF Data Architecture processing layers and the purpose of each layer.  This was discussed in detail in the \u003Ca href=\"/webinar/for-customers/data-cloud-deployment-framework-series/\"\u003EDCDF Webinar Series Episode 1\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/dcdf_architecture_review.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ERaw Layer\u003C/h3\u003E\n","\u003Cp\u003EThe Raw Layer represents the first processing layer within Snowflake.  It facilitates data ingestion into Snowflake and will manage the data as it exists in the source system, with no applied transformations. The following are attributes of the Raw Layer:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOne database per data source.\u003C/li\u003E\u003Cli\u003EHash generation for surrogate keys.\u003C/li\u003E\u003Cli\u003EReplication of data as it exists in the source system along with the creation of a surrogate key or control column for downstream transformations.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EIntegration Layer\u003C/h3\u003E\n","\u003Cp\u003EThe Integration Layer is used to centralize all business rules applied to the data. This layer performs the transformation, application of business rules, and materialization of data from the Raw Layer into one central location.  The following are attributes of the Integration Layer:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECentralized business rules.\u003C/li\u003E\u003Cli\u003EIntermediate results persisted as tables.\u003C/li\u003E\u003Cli\u003EUnits of work in one place with reusable components.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPresentation Layer\u003C/h3\u003E\n","\u003Cp\u003EThe Presentation Layer performs the organization of data from the raw and integration layers into various purpose-built solutions for reporting and analytics. The following are attributes of the Presentation Layer:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EConforming Dimensions\u003C/li\u003E\u003Cli\u003EAtomic Aggregate Fact Tables\u003C/li\u003E\u003Cli\u003EFlat data sets for data science model building\u003C/li\u003E\u003Cli\u003EViews for governing exposure of data for consumption, analytics and sharing.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E Business rules should not be implemented within the Presentation Layer. Using permanent tables for incrementally updated tables is recommended.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECommon Database\u003C/h3\u003E\n","\u003Cp\u003EThe Common Database is a single database with one or more schemas to manage objects that span the breadth of the data architecture layers. The following are attributes of the Common Database:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUser Defined Functions\u003C/li\u003E\u003Cli\u003EFile Formats\u003C/li\u003E\u003Cli\u003EExternal and Internal Stages\u003C/li\u003E\u003Cli\u003EAudit Tables\u003C/li\u003E\u003Cli\u003ECommon dimensions such as Date, Zip Code, State, Country, etc.\u003C/li\u003E\u003Cli\u003ECommon table data that is utilized across the DCDF layers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWorkspace Databases\u003C/h3\u003E\n","\u003Cp\u003EA Workspace is a sandbox environment where individual teams can persist data for their own development and testing. These workspaces can be a database and related virtual warehouse for each department or team within a business entity. One example can be a Data Science team that clones data from the Presentation Layer into their workspace and run models on that data to determine actionable insights. The following are attributes of a Workspace Database:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPersist intermediate results\u003C/li\u003E\u003Cli\u003ENon-production datasets for testing purposes\u003C/li\u003E\u003Cli\u003ECompute isolation to mitigate contention and facilitate cost governance.\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EIncremental Processing Concepts\u003C/h3\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"The best way to process big data, is to turn it into small data"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EThree steps for Incremental Processing\u003C/h4\u003E\n","\u003Ch5\u003EStep 1 - Define Logical Partitions\u003C/h5\u003E\n","\u003Cp\u003EThe volume of data being processed from the source system will drive the need to create logical partitions. Logical partitions are commonly defined as logical periods of time, or time series data, such as day, week, or month based on a business event represented in the data.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn this quickstart order date will be used as the logical partition. The order date will define how to identify new or changed data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch5\u003EStep 2 - Identify Impacted Partitions\u003C/h5\u003E\n","\u003Cp\u003ENext we want to identify the impacted logical partitions that are represented in the delta feed from the sources system. As the delta data is ingested into our staging tables in the raw layer, the impacted logical partitions (orderdate) can be identified.\u003C/p\u003E\n","\u003Ch5\u003EStep 3 - Incrementally Process those Partitions\u003C/h5\u003E\n","\u003Cp\u003EWe will utilize the logical partitions identified in Step 2, to incrementally process the partitions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/overview_diagram.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn our example above we implement the aforementioned three steps.\u003C/p\u003E\n","\u003Ch5\u003EDefine Logical Partitions\u003C/h5\u003E\n\u003Cul\u003E\u003Cli\u003EWe will use order date from the LINE_ITEM table.\u003C/li\u003E\u003Cli\u003EThis identifies the business event in our data that represents lifecycle changes over time for a given period.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch5\u003EIdentify Impacted Partitions\u003C/h5\u003E\n\u003Cul\u003E\u003Cli\u003EWe will query the LINE_ITEM_STG table in the Raw Layer for distinct order dates.\u003C/li\u003E\u003Cli\u003EWe will insert these dates into the Common Database table DELTA_DATE because this table will be utilized across the different layers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch5\u003EIncrementally Process those Partitions\u003C/h5\u003E\n\u003Cul\u003E\u003Cli\u003EWe will utilize the impacted partitions and process those dates of data through the raw, integration and presentation layers.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ELet's see how this works!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELab Overview\u003C/h2\u003E\n","\u003Cp\u003EBelow is an overview diagram of what we will be building.  Each step builds upon what was produced in the prior step.  We will only build the tables in this diagram.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIncluded in the code repository are additional table scripts that can be used to build out a dimensional model using these same concepts.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/overview_diagram.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELab Structure\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExplain Code Snippets\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWe will work through the code for each DCDF data architecture layer, explaining what is happening in each code snippet.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExecute Code and Verify Results\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn these sections, you will execute the code in your environment.\u003C/li\u003E\u003Cli\u003EWe will validate that the data was loaded using our example tables line_items, orders, part and partsupp.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELab summary\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EWe are loading data for 17 days(logical partition) between 1998-06-18 and 1998-07-02 representing three distinct weeks.\u003C/li\u003E\u003Cli\u003EGenerate CSV files for logical partition range from Snowflake Sample tables.\u003C/li\u003E\u003Cli\u003ESave this date range in a separate common delta table.\u003C/li\u003E\u003Cli\u003EFor Raw data layer, use delta table to load data in Line Item History and Line Item table.\u003C/li\u003E\u003Cli\u003EGenerate surrogate key and hash value to ensure only unique data is merged in Raw table.\u003C/li\u003E\u003Cli\u003EUse same Delta table to load data in subsequent layers(Integration and Presentation)\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EDCDF Data Architecture Layers\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERaw Layer\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn this layer, we will build the tables in the Raw Layer.\u003C/li\u003E\u003Cli\u003ETransient Staging tables used for most recent loaded data and having a suffix of _stg.\u003C/li\u003E\u003Cli\u003EPermanent History tables to track the history of changes and having a suffix of _hist.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIntegration Layer\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn this layer, we will identify a unit of work to derive the margin (retail price minus cost) at the line item level.\u003C/li\u003E\u003Cli\u003EWe will step through the code and extract these intermediate results by applying incremental processing.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPresentation Layer\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn this layer, we will create the ORDER_LINE_FACT table as well as the PART_DM dimension table as part of a dimensional model that can be used for consumption.\u003C/li\u003E\u003Cli\u003EWe will walk through how to incrementally process the new data coming in.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECommon database\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIn this layer, we will create the DW_DELTA_DATE table that will be used for incremental processing.\u003C/li\u003E\u003Cli\u003EA table function named DW_DELTA_DATE_RANGE_F will be created in the Common database and utilized for the logical partitions. This will take place in the Quickstart Setup section.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuickstart Setup\u003C/h2\u003E\n","\u003Ch3\u003EClone Repository for Quickstart\u003C/h3\u003E\n","\u003Cp\u003EThe sample code templates provided will be used to demonstrate incremental processing and logical partitions.  This code is written using SQL Scripting.  The code is tool ignostic and can be easily implemented into your tool set.\u003C/p\u003E\n","\u003Cp\u003ETo clone the repository for this Quickstart, visit the \u003Ca href=\"https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/dcdf_incremental_processing\"\u003EDCDF Incremental Processing associated GitHub Repository\u003C/a\u003E. Click on the green "Code" icon near the top of the page to obtain the "HTTPS" link needed to clone the repository. For more information on cloning a git repository please refer to the \u003Ca href=\"https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository\"\u003EGitHub Docs\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/git_repo_url.png\" alt=\"assets/git_repo_url.png\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENOTE:\u003C/strong\u003E This sample code contains additional tables that we will not go through as part of the quickstart.  They are just a continuation of the patterns we will be discussing to build out a purpose built dimensional model sample.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ESample Code Information\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EThese code templates are written using SQL Scripting.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPurpose of these scripts is to illustrate incremental processing and logical partitions.\u003C/li\u003E\u003Cli\u003EThese scripts are tool agnostic.\u003C/li\u003E\u003Cli\u003EThese templates can be easily modified to your environment and implemented in your toolset.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENaming Conventions of scripts\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENames of the folders are prefixed with a number to identify the order they will run as part of the data pipeline.\u003C/li\u003E\u003Cli\u003ETable Creation Scripts - Files with postfix of _tbl.sql\u003C/li\u003E\u003Cli\u003EELT Process Scripts - Files with postfix of _ld.sql\u003C/li\u003E\u003Cli\u003Eddl_orch.sql is the parent script that will execute all the database, schema, and table creation scripts.\u003C/li\u003E\u003Cli\u003Edml_orch.sql is the parent script that will execute the data pipeline.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E\n","\u003Ch3\u003ESample Data Set\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ELogin to your account and verify that you have access to the SNOWFLAKE_SAMPLE_DATA database.  See screenshot below.\u003C/li\u003E\u003Cli\u003ESNOWFLAKE_SAMPLE_DATA data share is created by default in newer accounts.  If you are not seeing this data share, you might need to create the data share in your account.  \u003Ca href=\"https://docs.snowflake.com/en/user-guide/sample-data-using.html\"\u003EUsing Sample Data\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/sample_data_set.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreating Example Databases, Schemas, Tables and Warehouse\u003C/h3\u003E\n","\u003Cp\u003ELet's create the databases, tables and warehouse using the default names.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EMake sure you are in the folder on your laptop where you cloned the sample code.  You want to be at the top level where you see the sub-folders of 000_admin, 100_raw, etc.\u003C/li\u003E\u003Cli\u003ERun Snowsql from the command line.  This will create all the databases, schemas, tables and a warehouse that are needed for this lab.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esnowsql -a <account_name> -u <username> -r sysadmin -D l_env=dev -f ddl_orch.sql -o output_file=ddl_orch.out\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EEnd of the output should show success like this screenshot.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/snowsql_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EExample Line Items\u003C/h3\u003E\n","\u003Cp\u003EAs part of the quickstart, we will monitor specific line item records.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ELogin to your Snowflake account and open a worksheet.\u003C/li\u003E\u003Cli\u003ECopy and paste this query into a worksheet.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Sample Order\nselect\n              row_number() over(order by uniform( 1, 60, random() ) ) as seq_no\n             ,l.l_orderkey\n             ,o.o_orderdate\n             ,l.l_partkey\n             ,l.l_suppkey\n             ,l.l_linenumber\n             ,l.l_quantity\n             ,l.l_extendedprice\n             ,l.l_discount\n             ,l.l_tax\n             ,l.l_returnflag\n             ,l.l_linestatus\n             ,l.l_shipdate\n             ,l.l_commitdate\n             ,l.l_receiptdate\n             ,l.l_shipinstruct\n             ,l.l_shipmode\n             ,l.l_comment\n        from\n            snowflake_sample_data.tpch_sf1000.orders o\n            join snowflake_sample_data.tpch_sf1000.lineitem l\n              on l.l_orderkey = o.o_orderkey\n        where\n                o.o_orderdate >= to_date('7/1/1998','mm/dd/yyyy')\n            and o.o_orderdate  < to_date('7/2/1998','mm/dd/yyyy')\n            and l_orderkey = 5722076550\n            and l_partkey in ( 105237594, 128236374);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EOutput should look like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/sample_initial_query.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EData Acquistion\u003C/h3\u003E\n","\u003Cp\u003EDuring this step we will acquiring the data from the SNOWFLAKE_SAMPLE_DATA to load in the next step. We will use the SNOWFLAKE_SAMPLE_DATA data set for tables lineitem, orders, part, and partsupp to generate the data files to load into our raw layer.\u003C/p\u003E\n","\u003Ch4\u003EStep 1 - Explain code snippets\u003C/h4\u003E\n","\u003Ch5\u003ELINE_ITEM_ACQ.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EUsing the Snowsight UI, select Worksheets from the left hand menu.\u003C/li\u003E\u003Cli\u003EClick the ellipsis next to the blue +Worksheets button.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 100_acquisition/line_item_acq.sql.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/snowsight_load_from_file.png\" alt=\"img\"\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/load_line_item_acq.png\" alt=\"img\"\u003E\u003C/li\u003E\u003Cli\u003EIn the first few lines of the script we are setting the context for this script. The defaults for database are DEV_WEBINAR_ORDERS_RL_DB and TPCH for the schema. This will be refered to as the Raw Layer going forward.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ENext we are setting the date range for the data we want to acquire by setting the l_start_dt and l_end_dt variables. For the purposes of this lab, we are using a 17 day date range.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Set variables for this sample data for the time frame to acquire\nset l_start_dt = dateadd( day, -16, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\nset l_end_dt   = dateadd( day,   1, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EThe \u003Cem\u003E"copy into"\u003C/em\u003E statement is where we are copying (Unloading) data, in CSV format, from the database SNOWFLAKE_SAMPLE_DATA into an Internal Table Stage.  As part of this \u003Cem\u003E"copy into"\u003C/em\u003E statement we are modifying the data to show changes in l_line_status over time.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- run this 2 or 3 times to produce overlapping files with new and modified records.\ncopy into\n    @~/line_item\nfrom\n(\n    with l_line_item as\n    (\n        select\n              row_number() over(order by uniform( 1, 60, random() ) ) as seq_no\n             ,l.l_orderkey\n             ,o.o_orderdate\n             ,l.l_partkey\n             ,l.l_suppkey\n             ,l.l_linenumber\n             ,l.l_quantity\n             ,l.l_extendedprice\n             ,l.l_discount\n             ,l.l_tax\n             ,l.l_returnflag\n             ,l.l_linestatus\n             ,l.l_shipdate\n             ,l.l_commitdate\n             ,l.l_receiptdate\n             ,l.l_shipinstruct\n             ,l.l_shipmode\n             ,l.l_comment\n        from\n            snowflake_sample_data.tpch_sf1000.orders o\n            join sample_data.tpch_sf1000.lineitem l\n              on l.l_orderkey = o.o_orderkey\n        where\n                o.o_orderdate >= $l_start_dt\n            and o.o_orderdate  < $l_end_dt\n    )\n    select\n         l.l_orderkey\n        ,l.o_orderdate\n        ,l.l_partkey\n        ,l.l_suppkey\n        ,l.l_linenumber\n        ,l.l_quantity\n        ,l.l_extendedprice\n        ,l.l_discount\n        ,l.l_tax\n        ,l.l_returnflag\n        -- simulate modified data by randomly changing the status\n        ,case uniform( 1, 100, random() )\n            when  1 then 'A'\n            when  5 then 'B'\n            when 20 then 'C'\n            when 30 then 'D'\n            when 40 then 'E'\n            else l.l_linestatus\n         end                            as l_linestatus\n        ,l.l_shipdate\n        ,l.l_commitdate\n        ,l.l_receiptdate\n        ,l.l_shipinstruct\n        ,l.l_shipmode\n        ,l.l_comment\n        ,current_timestamp()            as last_modified_dt -- generating a last modified timestamp as part of data acquisition.\n    from\n        l_line_item l\n    order by\n        l.l_orderkey\n)\nfile_format      = ( type=csv field_optionally_enclosed_by = '"' )\noverwrite        = false\nsingle           = false\ninclude_query_id = true\nmax_file_size    = 16000000\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2 - Execute the code and Verify results\u003C/h4\u003E\n","\u003Cp\u003EIn this step we will unload data for the LINE_ITEM, PART and ORDERS tables.\u003C/p\u003E\n","\u003Ch5\u003ELINE_ITEM_ACQ.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIf you haven't done so already, click the ellipsis next to the blue +Worksheets button.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 100_acquisition/line_item_acq.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of your script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EHighlight the code to set the variables for l_start_dt, l_end_dt and run them.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Set variables for this sample data for the time frame to acquire\nset l_start_dt = dateadd( day, -16, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\nset l_end_dt   = dateadd( day,   1, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ELet's verify those variables have been set. Run the following statement in your worksheet.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect $l_start_dt, $l_end_dt;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_start_end_dates.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003E\n","\u003Cp\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThis might take around 2-3 minutes on a small warehouse.  If you want to increase the size of your warehouse, it will run faster. The output should be similar to this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_copy_output.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify the number of files created. Paste this SQL into your worksheet and run it.  Output should be similar to this.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Elist @~/line_item;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_list_files.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch5\u003EPART_ACQ.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EClick the ellipsis next to the blue +Worksheets button.\u003C/li\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 100_acquisition/part_acq.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of your script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  This might take a few minutes.  The output should be similar to this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_part_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch5\u003EORDERS_ACQ.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EClick the ellipsis next to the blue +Worksheets button.\u003C/li\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 100_acquisition/orders_acq.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of your script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  This might take a few minutes.  The output should be similar to this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_orders_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch5\u003EPARTSUPP_ACQ.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EClick the ellipsis next to the blue +Worksheets button.\u003C/li\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 100_acquisition/partsupp_acq.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of your script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  This might take a few minutes.  The output should be similar to this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_partsupp_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ERaw Layer\u003C/h2\u003E\n","\u003Ch3\u003EStaging the data\u003C/h3\u003E\n","\u003Cp\u003EIn this section, we will take the acquired data from the Internal Table Stage mentioned in the previous section and load it into the staging tables in the Raw layer.  We will load LINE_ITEM_STG, ORDER_STG, PART_STG, and PARTSUPP_STG tables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_load_stg_diagram.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis diagram illustrates the first steps of loading the new data files into the raw layer tables.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIncremental Processing Step 1\u003C/strong\u003E is to define the logical partitions.  In the line item data we have identified the orderdate as the logical partition.\u003C/li\u003E\u003Cli\u003ENew data files from the Online Orders data source are placed into cloud storage.\u003C/li\u003E\u003Cli\u003EA Truncate/Reload pattern is used here to load the LINE_ITEM_STG table.\u003C/li\u003E\u003Cli\u003EFirst we truncate the prior data, and then load the new data into the LINE_ITEM_STG table.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 1 - Explain code snippets\u003C/h4\u003E\n","\u003Ch5\u003ELINE_ITEM_STG_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/line_item_stg_ld.sql\u003C/li\u003E\u003Cli\u003EIn the code, after setting the context, the next step is to truncate the line_item_stg table to remove any old ddata from the previous run.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Etruncate table line_item_stg;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EBelow in the \u003Cem\u003E"copy into"\u003C/em\u003E statement, the data from the files produced in the acquisition steps will be loaded in one statement.  This is a bulk load.\u003C/li\u003E\u003Cli\u003EThe purge parameter is set to true so that the files will be purged from the Internal Table Stage once they have been loaded.  This saves on storage usage and cost since these files are no longer needed.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- perform bulk load\ncopy into\n    line_item_stg\nfrom\n    (\n    select\n         s.$1                                            -- l_orderkey\n        ,s.$2                                            -- o_orderdate\n        ,s.$3                                            -- l_partkey\n        ,s.$4                                            -- l_suppkey\n        ,s.$5                                            -- l_linenumber\n        ,s.$6                                            -- l_quantity\n        ,s.$7                                            -- l_extendedprice\n        ,s.$8                                            -- l_discount\n        ,s.$9                                            -- l_tax\n        ,s.$10                                           -- l_returnflag\n        ,s.$11                                           -- l_linestatus\n        ,s.$12                                           -- l_shipdate\n        ,s.$13                                           -- l_commitdate\n        ,s.$14                                           -- l_receiptdate\n        ,s.$15                                           -- l_shipinstruct\n        ,s.$16                                           -- l_shipmode\n        ,s.$17                                           -- l_comment\n        ,s.$18                                           -- last_modified_dt\n        ,metadata$filename                               -- dw_file_name\n        ,metadata$file_row_number                        -- dw_file_row_no\n        ,current_timestamp()                             -- dw_load_ts\n    from\n        @~ s\n    )\npurge         = true\npattern       = '.*line_item/data.*\\.csv\\.gz'\nfile_format   = ( type=csv field_optionally_enclosed_by = '"' )\non_error      = skip_file\n--validation_mode = return_all_errors\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Execute code and Verify Results\u003C/h3\u003E\n","\u003Cp\u003EIn this step we will load 3 _stg tables: LINE_ITEM_STG, ORDERS_STG, PART_STG and PARTSUPP_STG.\u003C/p\u003E\n","\u003Ch4\u003ELINE_ITEM_STG_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EFirst, we will load the Line Item data into the LINE_ITEM_STG table.\u003C/li\u003E\u003Cli\u003EMake sure you have 200_raw/line_item_stg_ld.sql script open in Snowsight.\u003C/li\u003E\u003Cli\u003ESetting the context of our session, highlight these SQL statements in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EHighlight the truncate command in the script and run it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Etruncate table line_item_stg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003E\n","\u003Cp\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  On a small warehouse this will take approximately 1 minute to load the files. The results should look like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_stg_load_complete.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify the data was loaded.  Highlight the following SQL statement in your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect \n    *\nfrom \n    table(information_schema.copy_history(table_name=>'LINE_ITEM_STG', start_time=> dateadd(hours, -1, current_timestamp())))\nwhere\n    status = 'Loaded'\norder by\n    last_load_time desc\n;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003E\n","\u003Cp\u003EThe results will look similar to this.  The file name might differ.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_copy_history_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the lines that we are monitoring are loaded into the LINE_ITEM_STG table. Highlight this SQL statement in your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item_stg \nwhere l_orderkey = 5722076550\nand l_partkey in ( 105237594, 128236374); -- 2 lines\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_verify_stg_ld.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch5\u003EPART_STG_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003ENext, we will load the Part data into the PART_STG table.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 200_raw/part_stg_ld.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of our script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EHighlight the truncate command in the script and run it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Etruncate table part_stg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_part_stg_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch5\u003EORDERS_STG_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EFinally, we will load the Orders data into the ORDERS_STG table.\u003C/li\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 200_raw/orders_stg_ld.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of our script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EHighlight the truncate command in the script and run it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Etruncate table orders_stg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_orders_stg_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch5\u003EPARTSUPP_STG_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EFinally, we will load the Orders data into the PARTSUPP_STG table.\u003C/li\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and load the 200_raw/partsupp_stg_ld.sql.\u003C/li\u003E\u003Cli\u003ESetting the context of our script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EHighlight the truncate command in the script and run it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Etruncate table partsupp_stg;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003ESet your cursor on the \u003Cem\u003E"copy into"\u003C/em\u003E command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_partsupp_stg_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EIdentify Impacted Partitions\u003C/h3\u003E\n","\u003Cp\u003EIn this section we will identify the impacted partitions that were loaded into the staging tables in the Raw Layer and persist those identified partitions in a table for use in subsequent steps.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_impacted_partitions.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn this diagram illustrates the concept of identifying the impacted partitions, persisting them to be utilized in all the layers of the data architecture to incrementally process the data.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIncremental Processing Step 2\u003C/strong\u003E is to identify the impacted partitions.\u003C/li\u003E\u003Cli\u003EIn this diagram, we select all the distinct orderdates from the LINE_ITEM_STG table that are impacted by the new data that was loaded.\u003C/li\u003E\u003Cli\u003EThen we persist those dates into a table called \u003Cem\u003E"dw_delta_date"\u003C/em\u003E so that we can utilize those impacted partitions to do our incremental processing at each layer (Raw, Integration and Presentation).\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 1 - Explain code snippets\u003C/h4\u003E\n","\u003Ch5\u003EDW_DELTA_DATE_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/dw_delta_date_ld.sql\u003C/li\u003E\u003Cli\u003EAfter setting the context there is an \u003Cem\u003E"insert"\u003C/em\u003E statement.  As part of the \u003Cem\u003E"insert"\u003C/em\u003E statement, there is a \u003Ca href=\"https://docs.snowflake.com/en/user-guide/queries-cte.html\"\u003ECTE (Common Table Expression)\u003C/a\u003E identified by the \u003Cem\u003E"with"\u003C/em\u003E statement inside the \u003Cem\u003E"insert"\u003C/em\u003E statement. This \u003Cem\u003E"select"\u003C/em\u003E identifies all the orderdates that were impacted with the load into the _STG table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Einsert overwrite into dw_delta_date\nwith l_delta_date as\n(\n    select distinct\n        o_orderdate as event_dt\n    from\n        dev_webinar_orders_rl_db.tpch.line_item_stg \n)\nselect\n     event_dt\n    ,current_timestamp()            as dw_load_ts\nfrom\n    l_delta_date\norder by\n    1\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EDW_DELTA_DATE_RANGE_F.SQL (Additional Information)\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EAs part of the objects we created back in the Getting Started Section, we created a table function called DW_DELTA_DATE_RANGE_F. This table function will take a type of time period such as day, week, month, quarter and year as a parameter, and return rows with a start_date and end_date of that period.  We want the end_dt to be one greater than the actual max when returned from the function.  This is so in our code for incremental processing we can use less than that end_date, to catch all data up through the end of that date.\u003C/li\u003E\u003Cli\u003ETo review the code in Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 000_admin/dw_delta_date_range_f.sql\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse schema   &{l_common_schema};;\n\ncreate or replace function dw_delta_date_range_f\n(\n    p_period_type_cd   varchar\n)\nreturns table( start_dt timestamp_ltz, end_dt timestamp_ltz )\nas\n$$\n    select\n         start_dt\n        ,end_dt\n    from\n        (\n        select\n             case lower( p_period_type_cd )\n                 when 'all'     then current_date()\n                 when 'day'     then date_trunc( day, event_dt )\n                 when 'week'    then date_trunc( week, event_dt )\n                 when 'month'   then date_trunc( month, event_dt )\n                 when 'quarter' then date_trunc( quarter, event_dt )\n                 when 'year'    then date_trunc( year, event_dt )\n                 else current_date()\n             end                as partition_dt\n            ,min( event_dt ) as start_dt\n            ,dateadd( day, 1, max( event_dt ) ) as end_dt  -- To provide end date \n        from\n            dw_delta_date\n        group by\n            1\n        )\n    order by\n        1\n$$\n;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Execute code and Verify Results\u003C/h3\u003E\n","\u003Ch4\u003EDW_DELTA_DATE_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ESetting the context of our script.  Highlight these in your worksheet, and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse database dev_webinar_common_db;\nuse schema util;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ESet the cursor on the \u003Cem\u003E"insert overwrite"\u003C/em\u003E statement and run it. The output should look like the following.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ETo verify that the order dates for 17 day window were loaded into the DW_DELTA_DATE table, highlight this query into your worksheet and run it.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_common_db.util.dw_delta_date\norder by event_dt;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_load_verify.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EDW_DELTA_DATE_RANGE_F.SQL (Query the table function)\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EVerify the table function DW_DELTA_DATE_RANGE_F returns results as well. Highlight the following query your worksheet and run it.  This will return 3 rows representing 3 weeks of data.  We will utilize this table function in future scripts.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect start_dt, end_dt \nFROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) \norder by 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_table_function_output.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EIncrementally Process\u003C/h3\u003E\n","\u003Cp\u003EIn this section we will incrementally process the data and load it into the persistent tables in the Raw layer by utilizing the impacted partitions that were identified in the prior step.  We will load LINE_ITEM, LINE_ITEM_HIST, PART, ORDERS, and PARTSUPP tables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_incremental_processing.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn this diagram we are illustrating the concept of "looping" through the impacted partitions and processing only that data impacted into the persistent tables in the raw layer.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EIncremental Processing Step 3\u003C/strong\u003E is to utilize the impacted partitions to process the data through each of the layers.\u003C/li\u003E\u003Cli\u003EHere we are using the order dates stored in the DW_DELTA_DATE table to identify the dates that need to be processed into the LINE_ITEM_HIST and LINE_ITEM tables.\u003C/li\u003E\u003Cli\u003EIn the LINE_ITEM_HIST table we are capturing the life cycle of the rows as they change. This is for audit purposes or tracking.\u003C/li\u003E\u003Cli\u003EIn the LINE_ITEM table, we are peristing the current state of that line_item row.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 1 - Explain code snippets\u003C/h3\u003E\n","\u003Ch4\u003ELINE_ITEM_HIST_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/line_item_hist_ld.sql\u003C/li\u003E\u003Cli\u003EAfter we set the context, there is the \u003Cem\u003E"execute immediate"\u003C/em\u003E statement.  Since we are using \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index.html\"\u003ESQL Scripting\u003C/a\u003E this statement is used to create an anonymous block.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eexecute immediate $$\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EIn the declaration section, we are defining 2 variables (l_start_dt, and l_end_dt) that we will use to process the logical partition start and end dates.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edeclare\n  l_start_dt date;\n  l_end_dt   date;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EThen we are declaring the cursor which holds the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a week.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edeclare\n  ...\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EThe \u003Cem\u003E"for"\u003C/em\u003E loop is where the script incrementally processes the data by looping through a week at a time.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ebegin\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n    ...\n  end for;\n\nend;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EScroll down to the \u003Cem\u003E"insert"\u003C/em\u003E statement.\u003C/li\u003E\u003Cli\u003EInside the \u003Cem\u003E"with"\u003C/em\u003E statement the script is creating a CTE named l_stg.\u003C/li\u003E\u003Cli\u003EThe surrogate key and hash diff columns (_shk) being created in this CTE will be used to assist in identifying the rows that have changed.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Einsert into line_item_hist\nwith l_stg as\n    (\n        --\n        -- Driving CTE to identify all records in the logical partition to be processed.\n        select\n            -- generate hash key and hash diff to streamline processing\n             sha1_binary( concat( s.l_orderkey, '|', s.l_linenumber ) )  as dw_line_item_shk\n            --\n            -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n            -- actual meaningful change in the data\n            ,sha1_binary( concat( s.l_orderkey\n                                         ,'|', coalesce( to_char( s.o_orderdate, 'yyyymmdd' ), '~' )\n                                         ,'|', s.l_linenumber\n                                         ,'|', coalesce( to_char( s.l_partkey ), '~' )\n                                         ,'|', coalesce( to_char( s.l_suppkey ), '~' )\n                                         ,'|', coalesce( to_char( s.l_quantity ), '~' )\n                                         ,'|', coalesce( to_char( s.l_extendedprice ), '~' )\n                                         ,'|', coalesce( to_char( s.l_discount ), '~' )\n                                         ,'|', coalesce( to_char( s.l_tax ), '~' )\n                                         ,'|', coalesce( to_char( s.l_returnflag ), '~' )\n                                         ,'|', coalesce( to_char( s.l_linestatus ), '~' )\n                                         ,'|', coalesce( to_char( s.l_shipdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( to_char( s.l_commitdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( to_char( s.l_receiptdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( s.l_shipinstruct, '~' )\n                                         ,'|', coalesce( s.l_shipmode, '~' )\n                                         ,'|', coalesce( s.l_comment, '~' )\n                                )\n        \n                        )               as dw_hash_diff\n            ,s.*\n        from\n            line_item_stg s\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"8\"\u003E\u003Cli\u003EIn the CTE, the \u003Cem\u003E"where"\u003C/em\u003E clause is filtering the data from the LINE_ITEM_STG table to select only the rows that have an orderdate that is in the logical \u003Cem\u003E"week"\u003C/em\u003E partiton.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Einsert into line_item_hist\nwith l_stg as\n    (\n        --\n        -- Driving CTE to identify all records in the logical partition to be processed.\n        select\n            ...\n        from\n            line_item_stg s\n        where\n                s.o_orderdate >= :l_start_dt\n            and s.o_orderdate  < :l_end_dt\n    )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003EThe next CTE named l_deduped will go through and dedupe the records in the l_stg CTE, using the hash_diff to identify duplicate rows.  This eliminates duplicates from getting loaded into the permanent Raw layer LINE_ITEM_HIST table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E,l_deduped as\n    (\n        --\n        -- Dedupe the records from the staging table.\n        -- This assumes that there may be late arriving or duplicate data that were loaded\n        -- Need to identify the most recent record and use that to update the Current state table.\n        -- as there is no reason to process each individual change in the record, the last one would have the most recent updates\n        select\n            *\n        from\n            l_stg\n        qualify\n            row_number() over( partition by dw_hash_diff order by last_modified_dt desc, dw_file_row_no )  = 1\n    )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"10\"\u003E\u003Cli\u003EThe final outside \u003Cem\u003E"select"\u003C/em\u003E statement will select data from the LINE_ITEM_HIST table using the same logical \u003Cem\u003E"week"\u003C/em\u003E partition range, and compare it to the deduped staging records to identify what needs to be inserted into the LINE_ITEM_HIST table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E    select\n        ...\n    from\n        l_deduped s\n    where\n        s.dw_hash_diff not in\n        (\n            -- Select only the rows in that logical partition from the final table.\n            select dw_hash_diff from line_item_hist \n            where\n                    o_orderdate >= :l_start_dt\n                and o_orderdate  < :l_end_dt\n        )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"11\"\u003E\u003Cli\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E To optimize query performance in Snowflake, and ensure proper clustering of data in each micropartition, the \u003Cem\u003E"order by"\u003C/em\u003E clause sorts the rows by orderdate as they are inserted into the table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E    order by\n        o_orderdate  -- physically sort rows by a logical partitioning date\n    ;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ELINE_ITEM_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/line_item_ld.sql\u003C/li\u003E\u003Cli\u003EThe script is very similar to line_item_hist_ld.sql except this is a merge pattern. The script has the same anonymous block, variable declarations, and cursor definition using the DW_DELTA_DATE_RANGE_F table function to loop through the logical partitions, as well as the same \u003Cem\u003E"for"\u003C/em\u003E loop.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eexecute immediate $$\n\ndeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n    ...\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EThe script also has the same l_stg CTE to identify the LINE_ITEM_STG records within that week of logical partitions. It is also doing the same surrogate key and hash diff derivations.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E       with l_stg as\n        (\n            --\n            -- Driving CTE to identify all records in the logical partition to be processed\n            --\n            select\n                -- generate hash key and hash diff to streamline processing\n                 sha1_binary( concat( s.l_orderkey, '|', s.l_linenumber ) )  as dw_line_item_shk\n                --\n                -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n                -- actual meaningful change in the data\n                --\n                ,sha1_binary( concat( s.l_orderkey\n                                     ,'|', coalesce( to_char( s.o_orderdate, 'yyyymmdd' ), '~' )\n                                     ,'|', s.l_linenumber\n                                     ,'|', coalesce( to_char( s.l_partkey ), '~' )\n                                     ,'|', coalesce( to_char( s.l_suppkey ), '~' )\n                                     ,'|', coalesce( to_char( s.l_quantity ), '~' )\n                                     ,'|', coalesce( to_char( s.l_extendedprice ), '~' )\n                                     ,'|', coalesce( to_char( s.l_discount ), '~' )\n                                     ,'|', coalesce( to_char( s.l_tax ), '~' )\n                                     ,'|', coalesce( to_char( s.l_returnflag ), '~' )\n                                     ,'|', coalesce( to_char( s.l_linestatus ), '~' )\n                                     ,'|', coalesce( to_char( s.l_shipdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( to_char( s.l_commitdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( to_char( s.l_receiptdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( s.l_shipinstruct, '~' )\n                                     ,'|', coalesce( s.l_shipmode, '~' )\n                                     ,'|', coalesce( s.l_comment, '~' )\n                                    )\n            \n                            )               as dw_hash_diff\n                ,s.*\n            from\n                line_item_stg s\n            where\n                    s.o_orderdate >= :l_start_dt\n                and s.o_orderdate  < :l_end_dt\n        )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EThe script has the same dedupe logic.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E,l_deduped as\n    (\n        --\n        -- Dedupe the records from the staging table.\n        -- This assumes that there may be late arriving or duplicate data that were loaded\n        -- Need to identify the most recent record and use that to update the Current state table.\n        -- as there is no reason to process each individual change in the record, the last one would have the most recent updates\n        select\n            *\n        from\n            l_stg\n        qualify\n            row_number() over( partition by dw_hash_diff order by last_modified_dt desc, dw_file_row_no )  = 1\n    )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003EHowever...the script does have an additional CTE l_tgt. This CTE is important for partition pruning efficiencies because it is selecting only those rows from the LINE_ITEM table that are in the logical partition range we are processing.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E,l_tgt as\n        (\n            --\n            -- Select the records in the logical partition from the current table. \n            -- Its own CTE, for partition pruning efficiencies\n            select *\n            from line_item\n            where\n                    o_orderdate >= :l_start_dt\n                and o_orderdate  < :l_end_dt\n        )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ENow let's look at the \u003Cem\u003E"merge"\u003C/em\u003E statement.  In the \u003Cem\u003E"select"\u003C/em\u003E statement below, the l_deduped CTE and l_tgt CTE are joined together with a left join to identify the rows that are in the LINE_ITEM_STG table that might not be in the LINE_ITEM table; or where the hash_diff is different and the modified date is after what already exists in the table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Merge Pattern \n    --\n    merge into line_item tgt using\n    (\n      ...\n        select\n             current_timestamp()        as dw_version_ts\n            ,s.*\n        from\n            l_deduped s\n            left join l_tgt t on\n                t.dw_line_item_shk = s.dw_line_item_shk\n        where\n            -- source row does not exist in target table\n            t.dw_line_item_shk is null\n            -- or source row is more recent and differs from target table\n            or (\n                    t.last_modified_dt  < s.last_modified_dt\n                and t.dw_hash_diff     != s.dw_hash_diff\n               )\n        order by\n            s.o_orderdate  -- physically sort rows by logical partitioning date\n    ) src\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Please note the \u003Cem\u003E"on"\u003C/em\u003E clause of the \u003Cem\u003E"merge"\u003C/em\u003E statement.  The logical partition dates are used to prevent a full table scan of the LINE_ITEM table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Merge Pattern \n    --\n    merge into line_item tgt using\n    (\n      ...\n    on\n    (\n            tgt.dw_line_item_shk = src.dw_line_item_shk\n        and tgt.o_orderdate     >= :l_start_dt\n        and tgt.o_orderdate      < :l_end_dt\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EStep 2 - Execute code and Verify Results\u003C/h3\u003E\n","\u003Ch4\u003ELINE_ITEM_HIST_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/line_item_hist_ld.sql. Set the context of our script. Highlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPut your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command at the top of the script and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the LINE_ITEM_HISTORY table. Highlight the following query in your worksheet. If you have run these acquisition and load scripts multiple times you may see history changes in this table.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you are using the Snowflake Extension for VS Code to run the scripts, the SHA1_BINARY fields will not be displayed properly. To confirm that the SHA1_BINARY was created properly, run the following query in Snowflake.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item_hist \nwhere l_orderkey = 5722076550 \nand l_partkey in ( 105237594, 128236374)\norder by 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_hist_output.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ELINE_ITEM_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the the 200_raw/line_item_ld.sql file.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPut your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the LINE_ITEM table. Highlight the following query in your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item \nwhere l_orderkey = 5722076550 \nand l_partkey in ( 105237594, 128236374)\norder by 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_hist_output.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EPART_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENow we want to load the Part data into the PART table.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the worksheet 200_raw/part_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in the worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPut your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify the PART table was loaded.  Highlight the following query and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom dev_webinar_orders_rl_db.tpch.part\nwhere p_partkey in ( 105237594, 128236374);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_part_verify.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EORDER_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENow we want to load the Order data into the ORDERS table.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the 200_raw/order_ld.sql file.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in the worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPut your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify the ORDERS table was loaded with the order data.  Highlight the following query and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_orders_rl_db.tpch.orders \nwhere o_orderkey = 5722076550;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_order_verify.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EPARTSUPP_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENow we want to load the partsupp data into the PARTSUPP table.\u003C/li\u003E\u003Cli\u003ESelect \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the 200_raw/partsupp_ld.sql file.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in the worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPut your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EVerify the PARTSUPP table was loaded with the partsupp data.  Highlight the following query and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect * \nfrom dev_webinar_orders_rl_db.tpch.partsupp\nwhere ps_partkey in ( 105237594, 128236374);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_partsupp_verify.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EIntegration Layer\u003C/h3\u003E\n","\u003Cp\u003EIn this step we will incrementally process an isolated unit of work, deriving certain business rules from the impacted partitions that we previously identified.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/integration_incremental_process.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThis diagram illustrates the steps for incremental processing units of work in the integration layer of the DCDF data architecture.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA unit of work has been identified where the line item margin needs to be derived/calculated for each line item.\u003C/li\u003E\u003Cli\u003EThis is an isolated unit of work with business rules that should be incrementally processed each time new or changed line items are processed.\u003C/li\u003E\u003Cli\u003EExtensible, ease of maintenance for that unit of work.  As rules change to derive the margin, then they can be added/changed here in one place.\u003C/li\u003E\u003Cli\u003EThe impacted logical partitions have been identified and will be incrementally processed.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 1 - Explain code snippets\u003C/h4\u003E\n","\u003Ch5\u003ELINE_ITEM_MARGIN_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 310_derivation/line_item_margin_ld.sql\u003C/li\u003E\u003Cli\u003EThis script is also using the anonymous block in SQL Scripting.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eexecute immediate $$\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EAgain, the cursor is being declared using the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a given week. We are going to loop through the weeks and process a week at a time. This is the incremental processing.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EJust like the line_item_ld.sql file in the previous section, the merge processing pattern is being utilized here to select data by using the logical partitions. \u003Cstrong\u003EImportant:\u003C/strong\u003E Another note is the \u003Cem\u003E"on"\u003C/em\u003E clause of the \u003Cem\u003E"merge"\u003C/em\u003E statement.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Emerge into line_item_margin t using\n    (\n        with l_src as\n        (\n            -- \n            -- Driving CTE to identify all the records in the logical partition to be process\n            --\n            select\n                 s.dw_line_item_shk\n                ,s.o_orderdate\n                ,s.l_extendedprice - (s.l_quantity * p.ps_supplycost ) as margin_amt\n                ,s.last_modified_dt\n            from\n                dev_webinar_orders_rl_db.tpch.line_item s\n                join dev_webinar_orders_rl_db.tpch.partsupp p\n                  on ( p.ps_partkey = s.l_partkey\n                       and p.ps_suppkey = s.l_suppkey )\n            where\n                    s.o_orderdate >= :l_start_dt\n                and s.o_orderdate  < :l_end_dt\n        )\n        ,l_tgt as\n        (\n            -- \n            -- Select the records in the logical partition from the current table.\n            -- Its own CTE, for partition pruning efficiencies\n            select *\n            from line_item_margin\n            where\n                    o_orderdate >= :l_start_dt\n                and o_orderdate  < :l_end_dt\n        )\n        select\n             current_timestamp()        as dw_update_ts\n            ,s.*\n        from\n            l_src s\n            left join l_tgt t on\n                t.dw_line_item_shk = s.dw_line_item_shk\n        where\n            -- source row does not exist in target table\n            t.dw_line_item_shk is null\n            -- or source row is more recent and differs from target table\n            or (\n                    t.last_modified_dt  < s.last_modified_dt\n                and t.margin_amt       != s.margin_amt\n               )\n        order by\n            s.o_orderdate\n    ) s\n    on\n    (\n        t.dw_line_item_shk = s.dw_line_item_shk\n        and t.o_orderdate >= :l_start_dt \n        and t.o_orderdate  < :l_end_dt\n    )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2 - Execute code and Verify Results\u003C/h4\u003E\n","\u003Ch5\u003ELINE_ITEM_MARGIN_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the line_item_margin_ld.sql file.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_il_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace your cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the LINE_ITEM_MARGIN table. Highlight the following SQL query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Integration\nselect m.*\nfrom dev_webinar_il_db.main.line_item_margin m\n    join dev_webinar_orders_rl_db.tpch.line_item l\nwhere l.l_orderkey = 5722076550 \nand l.l_partkey in ( 105237594, 128236374)\nand m.dw_line_item_shk = l.dw_line_item_shk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/integration_line_item_margin_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch3\u003EPresentation Layer\u003C/h3\u003E\n","\u003Cp\u003EIn this step we will incrementally process the data that was loaded in the previous section, and re-organizing the data for consumption from the Presentation layer, utilizing the identified impacted partitions.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_incremental_process.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIn this diagram illustrates the steps for incremental processing for the Presentation layer of the DCDF data architecture.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOrder_line_fact is a purpose built solution for consumption and analytics.\u003C/li\u003E\u003Cli\u003EDimensional model as an example to provide a solution around the orders lines and consumption at that atomic level of data.\u003C/li\u003E\u003Cli\u003EThe impacted logical partitions have been identified and will be incrementally processed in the presentation layer.\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 1 - Explain code snippets\u003C/h4\u003E\n","\u003Ch5\u003EORDER_LINE_FACT_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 410_fact_atomic/order_line_fact_ld.sql\u003C/li\u003E\u003Cli\u003EThis script uses the anonymous block in SQL Scripting.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eexecute immediate $$\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EAgain, the cursor is being declared using the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a given week. We are going to loop through the weeks and process a week at a time. This is the incremental processing.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Edeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EWithin the \u003Cem\u003E"for"\u003C/em\u003E loop is a delete/insert processing pattern. The \u003Cem\u003E"delete"\u003C/em\u003E statement will delete from the ORDER_LINE_FACT table any rows within the logical partition. This logical partition is the week of order dates in our example. Then the \u003Cem\u003E"insert"\u003C/em\u003E will insert all the rows for that logical partition into the ORDER_LINE_FACT table.\u003C/li\u003E\u003Cli\u003EWe first apply deletes and then inserts in order to handle situations such as late arriving data. For instance if the supplier wasn't available when the order line item was first ordered, that value would be some default value. Then as the status of the line item changes, and the supplier was updated, the original defaulted row would need to be removed.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Delete the records using the logical partition \n     -- Very efficient when all the rows are in the same micropartitions.  Mirrors a truncate table in other database platforms.\n     delete from order_line_fact\n     where orderdate >= :l_start_dt\n       and orderdate <  :l_end_dt;\n \n     -- Insert the logical partitioned records into the table\n     -- Inserts data from same order date into the same micropartitions\n     -- Enables efficient querying of the data for consumption\n     insert into order_line_fact\n     select\n         li.dw_line_item_shk\n        ,o.o_orderdate\n        ,o.dw_order_shk\n        ,p.dw_part_shk\n        ,s.dw_supplier_shk\n        ,li.l_quantity      as quantity\n        ,li.l_extendedprice as extendedprice\n        ,li.l_discount      as discount\n        ,li.l_tax           as tax\n        ,li.l_returnflag    as returnflag\n        ,li.l_linestatus    as linestatus\n        ,li.l_shipdate\n        ,li.l_commitdate\n        ,li.l_receiptdate\n        ,lim.margin_amt\n        ,current_timestamp() as dw_load_ts\n     from\n         webinar_rl_db.tpch.line_item li\n         --\n         join webinar_rl_db.tpch.orders o\n           on o.o_orderkey = li.l_orderkey\n         --\n         join webinar_il_db.main.line_item_margin lim\n           on lim.dw_line_item_shk = li.dw_line_item_shk\n         --\n         -- Left outer join in case the part record is late arriving\n         --\n         left outer join webinar_rl_db.tpch.part p\n           on p.p_partkey = li.l_partkey\n         --\n         -- left outer join in case the supplier record is late arriving\n         --\n         left outer join webinar_rl_db.tpch.supplier s\n           on s.s_suppkey = li.l_suppkey\n     where \n             li.o_orderdate >= :l_start_dt\n         and li.o_orderdate <  :l_end_dt\n     order by o.o_orderdate;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003ELet's look at a dimension table load to illlustrate a different processing pattern \u003Cem\u003E"insert overwrite"\u003C/em\u003E.\u003C/li\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 400_dimension/part_dm_ld.sql\u003C/li\u003E\u003Cli\u003EThis script is also using the anonymous block in SQL Scripting.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eexecute immediate $$\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"9\"\u003E\u003Cli\u003EThis script uses an \u003Cem\u003E"insert overwrite"\u003C/em\u003E pattern where the code will essentially be doing a truncate/insert. The advantage of doing \u003Cem\u003E"insert overwrite"\u003C/em\u003E, versus a truncate/insert, is that the table will never be empty. With a truncate/insert pattern there could be a time period in which the table is empty if users were to query the data.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Einsert overwrite into part_dm\n   select\n       p.dw_part_shk\n      ,p.p_partkey\n      ,p.p_name as part_name\n      ,p.p_mfgr as mfgr\n      ,p.p_brand as brand\n      ,p.p_type as type\n      ,p.p_size as size\n      ,p.p_container as container\n      ,p.p_retailprice as retail_price\n      ,p.p_comment as comment\n      ,d.first_orderdate\n      ,p.last_modified_dt\n      ,p.dw_load_ts\n      ,p.dw_update_ts\n   from\n       dev_webinar_orders_rl_db.tpch.part p\n       left join dev_webinar_il_db.main.part_first_order_dt d\n         on d.dw_part_shk = p.dw_part_shk;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003EStep 2 - Execute code and Verify Results\u003C/h4\u003E\n","\u003Ch5\u003EORDER_LINE_FACT_LD.SQL\u003C/h5\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the worksheet for the 410_fact_atomic/order_line_fact_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\n3. Place the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003ELet's verify that the data was loaded into the ORDER_LINE_FACT table. Highlight the following query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect olf.*\nfrom dev_webinar_pl_db.main.order_line_fact olf\n  join dev_webinar_orders_rl_db.tpch.line_item l\n    on l.dw_line_item_shk = olf.dw_line_item_shk\nwhere l.l_orderkey = 5722076550 \nand l.l_partkey in ( 105237594, 128236374);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_order_line_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EPART_DM_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the worksheet for the 400_dimension/part_dm_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the PART_DM table. Highlight the following query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom dev_webinar_pl_db.main.part_dm p\nwhere p_partkey in ( 105237594, 128236374);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_part_dm_output.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBONUS -Slowly Changing Dimension\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ESlowly changing dimension type 2 changes add a new row in the dimension with the updated attribute values. This requires generalizing the primary key of the dimension beyond the natural or durable key because there will potentially be multiple rows describing each member. When a new row is created for a dimension member, a new primary surrogate key is assigned and used as a foreign key in all fact tables from the moment of the update until a subsequent change creates a new dimension key and updated dimension row. -  Kimball Group.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this section we will go through incremental processing of a Type 2, slowly changing dimension; customer.   We will go through running each layer in the DCDF for this customer data.\u003C/p\u003E\n","\u003Ch3\u003EStep 1 - Acquistion\u003C/h3\u003E\n","\u003Ch4\u003ECUSTOMER_ACQ.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight,  \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 100_acquisition/customer_acq.sql.\u003C/li\u003E\u003Cli\u003EIn the first few lines of the script we are setting the context for this script.\u003C/li\u003E\u003Cli\u003EHere you will see a \u003Cem\u003E"union all"\u003C/em\u003E statement where we are creating some modified data for a specific customer to illustrate changes over time to the data.\u003C/li\u003E\u003Cli\u003EThe \u003Cem\u003E"copy into"\u003C/em\u003E statement is where we are copying (Unloading) the data from the SNOWFLAKE_SAMPLE_DATA into CSV formatted files into an internal table stage.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_acq_results.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 2 - Raw Layer\u003C/h3\u003E\n","\u003Ch4\u003ECUSTOMER_STG_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 100_acquisition/customer_stg_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"truncate table "\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_ld_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the CUSTOMER_STG table.  Highlight the following query at the bottom of your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom dev_webinar_orders_rl_db.tpch.customer_stg\nwhere c_custkey in (50459048);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_verify_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EDW_DELTA_DATE_LD_BONUS.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 200_raw/dw_delta_date_ld_bonus.sql.\u003C/li\u003E\u003Cli\u003EThis script has been updated to identify logical partitions in the CUSTOMER_STG table as well as the LINE_ITEM_STG.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EPlace the cursor on the \u003Cem\u003E"insert overwrite"\u003C/em\u003E statement and run it.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_dw_delta_date_bonus.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003ECUSTOMER_HIST_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 100_acquisition/customer_hist_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EScroll down to the dw_customer_shk surrogate key generation in the \u003Cem\u003E"with"\u003C/em\u003E statement.  We are generating a surrogate key for each combination of the c_custkey and the change_date.  We want to use this in the presentation layer to match an order to the correct version of the customer at the time of the order.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E    select\n        -- generate hash key and hash diff to streamline processing\n        sha1_binary( s.c_custkey || to_char( s.change_date, 'yyyymmdd' ) )  as dw_customer_shk\n        --\n        -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n        -- actual meaningful change in the data\n        ,sha1_binary( concat( s.c_custkey || to_char( s.change_date, 'yyyymmdd' )\n                                     ,'|', coalesce( to_char( s.change_date, 'yyyymmdd'), '~' )\n                                     ,'|', coalesce( s.c_name, '~' )\n                                     ,'|', coalesce( s.c_address, '~' )\n                                     ,'|', coalesce( to_char( s.c_nationkey ), '~' )\n                                     ,'|', coalesce( s.c_phone, '~' )\n                                     ,'|', coalesce( to_char( s.c_acctbal ), '~' )\n                                     ,'|', coalesce( s.c_mktsegment, '~' )\n                                     ,'|', coalesce( s.c_comment, '~' )\n                            )\n     \n                    )               as dw_hash_diff\n        ,s.*\n    from\n        customer_stg s\n    where\n            s.change_date >= :l_start_dt\n        and s.change_date  < :l_end_dt\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.  Here we are loading the CUSTOMER_HIST table using the same repeatable pattern as we did with the LINE_ITEM_HIST table.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_ld_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the CUSTOMER_HIST table.  Highlight the following query at the bottom of your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom dev_webinar_orders_rl_db.tpch.customer_hist\nwhere c_custkey in (50459048)\norder by 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_hist_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EStep 3 - Integration Layer\u003C/h3\u003E\n","\u003Cp\u003EThere are no steps here for the integration layer as there aren't any business rules being defined for customer.\u003C/p\u003E\n","\u003Ch3\u003EStep 4 - Presentation Layer\u003C/h3\u003E\n","\u003Ch4\u003ECUSTOMER_DM.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 400_dimension/customer_dm_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.  Here we are loading the CUSTOMER_DM table.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's veriy that the data was loaded into the CUSTOMER_DM table.  Highlight the query below in your worksheet and run it.  There should be two rows, where the values of the c_acctbal are different.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect *\nfrom dev_webinar_pl_db.main.customer_dm\nwhere c_custkey in (50459048)\norder by 5;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_customer_dm_results.png\" alt=\"img\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EORDER_LINE_FACT_BONUS_LD.SQL\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn Snowsight, \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E, select the 410_fact_atomic/order_line_fact_bonus_ld.sql.\u003C/li\u003E\u003Cli\u003EHighlight the following SQL statements in your worksheet and run them to set the context.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Euse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPlace the cursor on the \u003Cem\u003E"execute immediate"\u003C/em\u003E command and run it.  Here we are loading the ORDER_LINE_FACT_BONUS table.\u003Cbr\u003E\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ELet's verify that the data was loaded into the ORDER_LINE_FACT_BONUS table and that we can see the one order with multiple line_items and the c_acctbal column is 1,293. Highlight the query below in your worksheet and run it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Eselect \n     c.c_custkey\n    ,c.c_name\n    ,c.c_acctbal\n    ,olf.*\nfrom dev_webinar_pl_db.main.customer_dm c\n   join dev_webinar_pl_db.main.order_line_fact_bonus olf\n     on olf.dw_customer_shk = c.dw_customer_shk\nwhere c.c_custkey in (50459048);\norder by olf.orderdate;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_order_line_fact_bonus.png\" alt=\"img\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECleanup\u003C/h2\u003E\n","\u003Cp\u003EThis step is to cleanup and drop all the objects we created as part of this quickstart.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ESelect to \u003Cem\u003E"create worksheet from SQL file"\u003C/em\u003E and open the worksheet for the 000_admin/cleanup.sql.\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Cleanup all the objects we created\n\nuse role sysadmin;\n\ndrop database dev_webinar_orders_rl_db;\ndrop database dev_webinar_il_db;\ndrop database dev_webinar_pl_db;\ndrop database dev_webinar_common_db;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ERun all the SQL statements to drop all the objects that were created.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png\" alt=\"img\"\u003E\u003C/li\u003E\u003C/ol\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E\n","\u003Cp\u003EThis tutorial was designed as a hands-on introduction to the Data Cloud Deployment Framework (DCDF) data architecture incremental processing and logical partitions.\u003C/p\u003E\n","\u003Cp\u003EWe encourage you to continue learning about the Data Cloud Deployment Framework, by watching the \u003Ca href=\"/webinar/for-customers/data-cloud-deployment-framework-series/\"\u003EData Cloud Deployment Framework Series Webinars\u003C/a\u003E either on-demand on register for upcoming episodes.\u003C/p\u003E\n","\u003Cp\u003EAlso the github repo contains more scripts than what was covered in this lab.  It's a full, working template model taking source data from the Raw layer, through the Integration layer, and finally to the Presentation layer dimension model, ready for consumption. Please take the time to go through each one of these scripts and slowly work through the examples. Feel free to use these as code templates to be implemented in your own environments and accounts for your data processing.\u003C/p\u003E\n","\u003Cp\u003EDuring this quickstart, our hope is that you noticed the repeatable patterns in these scripts which can facilitate an Agile Development Process.\u003C/p\u003E\n","\u003Ch3\u003EWhat we have covered\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA data pipeline utilizing incremental processing and logical partition definitions\u003C/li\u003E\u003Cli\u003EIntroduced the concepts of the Raw, Integration and Presentation layers and how each utilizes incremental processing and logical partition definitions.\u003C/li\u003E\u003Cli\u003EWalked through code examples used for different processing patterns such as truncate/reload, insert overwrite, merge and delete/insert.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPotential Next Steps\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECreate a stored procedure for the LINE_ITEM_MARGIN business rules logic\u003C/li\u003E\u003Cli\u003ECreate streams on the LINE_ITEM raw layer table.\u003C/li\u003E\u003Cli\u003ECreate a Snowflake Task to execute the LINE_ITEM_MARGIN stored procedure when there is data in the LINE_ITEM stream.\u003C/li\u003E\u003C/ul\u003E"],"description":"Process data incrementally in Snowflake with DCDF Data Architecture and Dynamic Tables for efficient, cost-effective data pipelines.","title":"Getting Started with DCDF Data Architecture Incremental Processing & Logical Partitions","isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview \n\nThe topics of covered in this guide were originally presented in Episode 2 of Snowflake's Data Cloud Deployment Framework (DCDF) webinar series. [DCDF Webinar Episode 2](/webinar/for-customers/applying-architectural-patterns-to-solve-business-questions-2023-01-11/) focused on the ELT implementation patterns to operationalize data loading, centralize the management of data transformations and restructure the data for optimal reporting and analysis.\n\nIn this quickstart, we will focus on the actual SQL code templates for ingesting, transforming, and restructuring data into the presentation layer using incremental processing and logical partition definitions.\n\n\u003E \n\u003E\n\u003E **Caveat:** This DCDF Data Architecture quickstart and template scripts are for illustrative purposes only. These scripts can be run in any Snowflake account in order to reinforce the concepts and patterns presented in DCDF Webinar Episode 2. They can be customized for your own business use cases.\n\n### Prerequisites\n- A Snowflake account.  Existing or a free [Trial Account](https://trial.snowflake.com/).\n- ACCOUNTADMIN role (Note: Free Trial accounts provide this automatically)\n- Working knowledge of Snowflake database objects and the Snowflake Web UI/Snowsight.\n- Familiarity with Snowflake and Snowflake objects.\n- [SnowSQL installed](https://docs.snowflake.com/en/user-guide/snowsql-install-config.html) and configured to your Snowflake account.\n- Familiarity and understanding of SQL syntax\n\n### What You Will Learn \nIn this quickstart, we will build on the topics discussed in the webinar by loading and executing the SQL code used in the presentation.\n\n- DCDF Data Architecture\n- Data Ingestion\n- Data Transformation\n- Incremental processing and Logical Partitions\n\n### What You Will Need \n- A Snowflake Account or [Trial Account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides), any edition will do as the scripts will run on any edition.\n- Login to Snowflake account that has ACCOUNTADMIN role access (Note: Free Trial accounts provide this automatically)\n- [SnowSQL installed](https://docs.snowflake.com/en/user-guide/snowsql-install-config.html)\n- Snowsight or Classic Ui will be used in these examples.  \n\n### What You Will Build \nAn extensible ELT data pipeline, using logical partitions, that employs repeatable patterns for ingestion, transformation and consumable data assets.\n\n\n\u003C!-- ------------------------ --\u003E\n## DCDF Data Architecture\n\nLet's review the DCDF Data Architecture processing layers and the purpose of each layer.  This was discussed in detail in the [DCDF Webinar Series Episode 1](/webinar/for-customers/data-cloud-deployment-framework-series/).\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/dcdf_architecture_review.png)\n\n### Raw Layer\nThe Raw Layer represents the first processing layer within Snowflake.  It facilitates data ingestion into Snowflake and will manage the data as it exists in the source system, with no applied transformations. The following are attributes of the Raw Layer:\n- One database per data source.  \n- Hash generation for surrogate keys.\n- Replication of data as it exists in the source system along with the creation of a surrogate key or control column for downstream transformations.\n\n### Integration Layer\nThe Integration Layer is used to centralize all business rules applied to the data. This layer performs the transformation, application of business rules, and materialization of data from the Raw Layer into one central location.  The following are attributes of the Integration Layer:\n- Centralized business rules.\n- Intermediate results persisted as tables.\n- Units of work in one place with reusable components.\n\n### Presentation Layer\nThe Presentation Layer performs the organization of data from the raw and integration layers into various purpose-built solutions for reporting and analytics. The following are attributes of the Presentation Layer:\n- Conforming Dimensions\n- Atomic Aggregate Fact Tables\n- Flat data sets for data science model building\n- Views for governing exposure of data for consumption, analytics and sharing.  \n\n\u003E \n\u003E\n\u003E **Note** Business rules should not be implemented within the Presentation Layer. Using permanent tables for incrementally updated tables is recommended.\n\n### Common Database\nThe Common Database is a single database with one or more schemas to manage objects that span the breadth of the data architecture layers. The following are attributes of the Common Database:\n- User Defined Functions\n- File Formats\n- External and Internal Stages\n- Audit Tables\n- Common dimensions such as Date, Zip Code, State, Country, etc.\n- Common table data that is utilized across the DCDF layers.\n\n### Workspace Databases\nA Workspace is a sandbox environment where individual teams can persist data for their own development and testing. These workspaces can be a database and related virtual warehouse for each department or team within a business entity. One example can be a Data Science team that clones data from the Presentation Layer into their workspace and run models on that data to determine actionable insights. The following are attributes of a Workspace Database:\n\n- Persist intermediate results\n- Non-production datasets for testing purposes\n- Compute isolation to mitigate contention and facilitate cost governance.\n\n\u003C!-- ------------------------ --\u003E\n### Incremental Processing Concepts\n\u003E \n\u003E\n\u003E\"The best way to process big data, is to turn it into small data\"\n\n#### Three steps for Incremental Processing\n##### Step 1 - Define Logical Partitions\nThe volume of data being processed from the source system will drive the need to create logical partitions. Logical partitions are commonly defined as logical periods of time, or time series data, such as day, week, or month based on a business event represented in the data.\n\n\u003E \n\u003E\n\u003E In this quickstart order date will be used as the logical partition. The order date will define how to identify new or changed data.\n\n##### Step 2 - Identify Impacted Partitions\nNext we want to identify the impacted logical partitions that are represented in the delta feed from the sources system. As the delta data is ingested into our staging tables in the raw layer, the impacted logical partitions (orderdate) can be identified.  \n \n##### Step 3 - Incrementally Process those Partitions\nWe will utilize the logical partitions identified in Step 2, to incrementally process the partitions. \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/overview_diagram.png)\n\nIn our example above we implement the aforementioned three steps.\n##### Define Logical Partitions\n- We will use order date from the LINE_ITEM table.  \n- This identifies the business event in our data that represents lifecycle changes over time for a given period.\n\n##### Identify Impacted Partitions\n- We will query the LINE_ITEM_STG table in the Raw Layer for distinct order dates.  \n- We will insert these dates into the Common Database table DELTA_DATE because this table will be utilized across the different layers.\n\n##### Incrementally Process those Partitions\n- We will utilize the impacted partitions and process those dates of data through the raw, integration and presentation layers.  \n\nLet's see how this works!\n\n\u003C!-- ------------------------ --\u003E\n## Lab Overview\n\nBelow is an overview diagram of what we will be building.  Each step builds upon what was produced in the prior step.  We will only build the tables in this diagram.  \n\n\u003E \n\u003E\n\u003E Included in the code repository are additional table scripts that can be used to build out a dimensional model using these same concepts.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/overview_diagram.png)\n\n### Lab Structure\n- **Explain Code Snippets**\n    - We will work through the code for each DCDF data architecture layer, explaining what is happening in each code snippet.\n\n- **Execute Code and Verify Results**\n    - In these sections, you will execute the code in your environment.\n    - We will validate that the data was loaded using our example tables line_items, orders, part and partsupp.  \n\n### Lab summary\n- We are loading data for 17 days(logical partition) between 1998-06-18 and 1998-07-02 representing three distinct weeks.\n- Generate CSV files for logical partition range from Snowflake Sample tables.\n- Save this date range in a separate common delta table.\n- For Raw data layer, use delta table to load data in Line Item History and Line Item table.\n- Generate surrogate key and hash value to ensure only unique data is merged in Raw table.\n- Use same Delta table to load data in subsequent layers(Integration and Presentation)\n\n### DCDF Data Architecture Layers\n\n- **Raw Layer**\n    - In this layer, we will build the tables in the Raw Layer.\n    - Transient Staging tables used for most recent loaded data and having a suffix of _stg. \n    - Permanent History tables to track the history of changes and having a suffix of _hist.\n\n- **Integration Layer**\n    - In this layer, we will identify a unit of work to derive the margin (retail price minus cost) at the line item level. \n    - We will step through the code and extract these intermediate results by applying incremental processing.\n\n- **Presentation Layer**\n    - In this layer, we will create the ORDER_LINE_FACT table as well as the PART_DM dimension table as part of a dimensional model that can be used for consumption.\n    - We will walk through how to incrementally process the new data coming in.\n\n- **Common database**\n    - In this layer, we will create the DW_DELTA_DATE table that will be used for incremental processing.  \n    - A table function named DW_DELTA_DATE_RANGE_F will be created in the Common database and utilized for the logical partitions. This will take place in the Quickstart Setup section. \n\n\n\u003C!-- ------------------------ --\u003E\n## Quickstart Setup\n\n### Clone Repository for Quickstart\nThe sample code templates provided will be used to demonstrate incremental processing and logical partitions.  This code is written using SQL Scripting.  The code is tool ignostic and can be easily implemented into your tool set.\n\nTo clone the repository for this Quickstart, visit the [DCDF Incremental Processing associated GitHub Repository](https://github.com/Snowflake-Labs/sf-samples/tree/main/samples/dcdf_incremental_processing). Click on the green \"Code\" icon near the top of the page to obtain the \"HTTPS\" link needed to clone the repository. For more information on cloning a git repository please refer to the [GitHub Docs](https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository).\n\n![assets/git_repo_url.png](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/git_repo_url.png)\n\n\u003E \n\u003E\n\u003E **NOTE:** This sample code contains additional tables that we will not go through as part of the quickstart.  They are just a continuation of the patterns we will be discussing to build out a purpose built dimensional model sample.\n\n### Sample Code Information\n- These code templates are written using SQL Scripting.\n  - Purpose of these scripts is to illustrate incremental processing and logical partitions.\n  - These scripts are tool agnostic.\n  - These templates can be easily modified to your environment and implemented in your toolset.\n\n- Naming Conventions of scripts\n  - Names of the folders are prefixed with a number to identify the order they will run as part of the data pipeline.\n  - Table Creation Scripts - Files with postfix of _tbl.sql\n  - ELT Process Scripts - Files with postfix of _ld.sql\n  - ddl_orch.sql is the parent script that will execute all the database, schema, and table creation scripts.\n  - dml_orch.sql is the parent script that will execute the data pipeline.\n\n\u003C!-- ------------------------ --\u003E\n## Snowflake Setup\n\n### Sample Data Set\n- Login to your account and verify that you have access to the SNOWFLAKE_SAMPLE_DATA database.  See screenshot below.\n- SNOWFLAKE_SAMPLE_DATA data share is created by default in newer accounts.  If you are not seeing this data share, you might need to create the data share in your account.  [Using Sample Data](https://docs.snowflake.com/en/user-guide/sample-data-using.html)\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/sample_data_set.png)\n\n### Creating Example Databases, Schemas, Tables and Warehouse\nLet's create the databases, tables and warehouse using the default names.\n- Make sure you are in the folder on your laptop where you cloned the sample code.  You want to be at the top level where you see the sub-folders of 000_admin, 100_raw, etc.\n- Run Snowsql from the command line.  This will create all the databases, schemas, tables and a warehouse that are needed for this lab.  \n``` sql\nsnowsql -a \u003Caccount_name\u003E -u \u003Cusername\u003E -r sysadmin -D l_env=dev -f ddl_orch.sql -o output_file=ddl_orch.out\n```\n- End of the output should show success like this screenshot.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/snowsql_success.png)\n\n### Example Line Items\nAs part of the quickstart, we will monitor specific line item records.\n1. Login to your Snowflake account and open a worksheet. \n2. Copy and paste this query into a worksheet.\n``` sql\n-- Sample Order\nselect\n              row_number() over(order by uniform( 1, 60, random() ) ) as seq_no\n             ,l.l_orderkey\n             ,o.o_orderdate\n             ,l.l_partkey\n             ,l.l_suppkey\n             ,l.l_linenumber\n             ,l.l_quantity\n             ,l.l_extendedprice\n             ,l.l_discount\n             ,l.l_tax\n             ,l.l_returnflag\n             ,l.l_linestatus\n             ,l.l_shipdate\n             ,l.l_commitdate\n             ,l.l_receiptdate\n             ,l.l_shipinstruct\n             ,l.l_shipmode\n             ,l.l_comment\n        from\n            snowflake_sample_data.tpch_sf1000.orders o\n            join snowflake_sample_data.tpch_sf1000.lineitem l\n              on l.l_orderkey = o.o_orderkey\n        where\n                o.o_orderdate \u003E= to_date('7/1/1998','mm/dd/yyyy')\n            and o.o_orderdate  \u003C to_date('7/2/1998','mm/dd/yyyy')\n            and l_orderkey = 5722076550\n            and l_partkey in ( 105237594, 128236374);\n```\n3. Output should look like this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/sample_initial_query.png)\n\n\u003C!-- ------------------------ --\u003E\n### Data Acquistion\n\nDuring this step we will acquiring the data from the SNOWFLAKE_SAMPLE_DATA to load in the next step. We will use the SNOWFLAKE_SAMPLE_DATA data set for tables lineitem, orders, part, and partsupp to generate the data files to load into our raw layer.  \n\n#### Step 1 - Explain code snippets\n##### LINE_ITEM_ACQ.SQL\n1. Using the Snowsight UI, select Worksheets from the left hand menu.\n2. Click the ellipsis next to the blue +Worksheets button.\n3. Select *\"create worksheet from SQL file\"* and load the 100_acquisition/line_item_acq.sql.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/snowsight_load_from_file.png)\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/load_line_item_acq.png)\n4. In the first few lines of the script we are setting the context for this script. The defaults for database are DEV_WEBINAR_ORDERS_RL_DB and TPCH for the schema. This will be refered to as the Raw Layer going forward.\n``` sql\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\n```\n5. Next we are setting the date range for the data we want to acquire by setting the l_start_dt and l_end_dt variables. For the purposes of this lab, we are using a 17 day date range.  \n``` sql\n-- Set variables for this sample data for the time frame to acquire\nset l_start_dt = dateadd( day, -16, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\nset l_end_dt   = dateadd( day,   1, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\n```\n6. The *\"copy into\"* statement is where we are copying (Unloading) data, in CSV format, from the database SNOWFLAKE_SAMPLE_DATA into an Internal Table Stage.  As part of this *\"copy into\"* statement we are modifying the data to show changes in l_line_status over time.  \n\n``` sql\n-- run this 2 or 3 times to produce overlapping files with new and modified records.\ncopy into\n    @~/line_item\nfrom\n(\n    with l_line_item as\n    (\n        select\n              row_number() over(order by uniform( 1, 60, random() ) ) as seq_no\n             ,l.l_orderkey\n             ,o.o_orderdate\n             ,l.l_partkey\n             ,l.l_suppkey\n             ,l.l_linenumber\n             ,l.l_quantity\n             ,l.l_extendedprice\n             ,l.l_discount\n             ,l.l_tax\n             ,l.l_returnflag\n             ,l.l_linestatus\n             ,l.l_shipdate\n             ,l.l_commitdate\n             ,l.l_receiptdate\n             ,l.l_shipinstruct\n             ,l.l_shipmode\n             ,l.l_comment\n        from\n            snowflake_sample_data.tpch_sf1000.orders o\n            join sample_data.tpch_sf1000.lineitem l\n              on l.l_orderkey = o.o_orderkey\n        where\n                o.o_orderdate \u003E= $l_start_dt\n            and o.o_orderdate  \u003C $l_end_dt\n    )\n    select\n         l.l_orderkey\n        ,l.o_orderdate\n        ,l.l_partkey\n        ,l.l_suppkey\n        ,l.l_linenumber\n        ,l.l_quantity\n        ,l.l_extendedprice\n        ,l.l_discount\n        ,l.l_tax\n        ,l.l_returnflag\n        -- simulate modified data by randomly changing the status\n        ,case uniform( 1, 100, random() )\n            when  1 then 'A'\n            when  5 then 'B'\n            when 20 then 'C'\n            when 30 then 'D'\n            when 40 then 'E'\n            else l.l_linestatus\n         end                            as l_linestatus\n        ,l.l_shipdate\n        ,l.l_commitdate\n        ,l.l_receiptdate\n        ,l.l_shipinstruct\n        ,l.l_shipmode\n        ,l.l_comment\n        ,current_timestamp()            as last_modified_dt -- generating a last modified timestamp as part of data acquisition.\n    from\n        l_line_item l\n    order by\n        l.l_orderkey\n)\nfile_format      = ( type=csv field_optionally_enclosed_by = '\"' )\noverwrite        = false\nsingle           = false\ninclude_query_id = true\nmax_file_size    = 16000000\n;\n```\n\n#### Step 2 - Execute the code and Verify results\nIn this step we will unload data for the LINE_ITEM, PART and ORDERS tables.\n\n##### LINE_ITEM_ACQ.SQL\n\n1. If you haven't done so already, click the ellipsis next to the blue +Worksheets button.\n2. Select *\"create worksheet from SQL file\"* and load the 100_acquisition/line_item_acq.sql.\n3. Setting the context of your script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\n\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Highlight the code to set the variables for l_start_dt, l_end_dt and run them.\n``` sql\n-- Set variables for this sample data for the time frame to acquire\nset l_start_dt = dateadd( day, -16, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\nset l_end_dt   = dateadd( day,   1, to_date( '1998-07-02', 'yyyy-mm-dd' ) );\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n5. Let's verify those variables have been set. Run the following statement in your worksheet.\n``` sql\nselect $l_start_dt, $l_end_dt;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_start_end_dates.png)\n\n6. Set your cursor on the *\"copy into\"* command and run it.\n7. This might take around 2-3 minutes on a small warehouse.  If you want to increase the size of your warehouse, it will run faster. The output should be similar to this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_copy_output.png)\n\n8. Let's verify the number of files created. Paste this SQL into your worksheet and run it.  Output should be similar to this.\n``` sql\nlist @~/line_item;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_list_files.png)\n\n##### PART_ACQ.SQL\n1. Click the ellipsis next to the blue +Worksheets button.\n2. Select to *\"create worksheet from SQL file\"* and load the 100_acquisition/part_acq.sql.\n3. Setting the context of your script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Set your cursor on the *\"copy into\"* command and run it.  This might take a few minutes.  The output should be similar to this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_part_results.png)\n\n##### ORDERS_ACQ.SQL\n1. Click the ellipsis next to the blue +Worksheets button.\n2. Select to *\"create worksheet from SQL file\"* and load the 100_acquisition/orders_acq.sql.\n3. Setting the context of your script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Set your cursor on the *\"copy into\"* command and run it.  This might take a few minutes.  The output should be similar to this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_orders_results.png)\n\n##### PARTSUPP_ACQ.SQL\n1. Click the ellipsis next to the blue +Worksheets button.\n2. Select to *\"create worksheet from SQL file\"* and load the 100_acquisition/partsupp_acq.sql.\n3. Setting the context of your script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse database DEV_WEBINAR_ORDERS_RL_DB;\nuse schema   TPCH;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Set your cursor on the *\"copy into\"* command and run it.  This might take a few minutes.  The output should be similar to this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/acq_partsupp_results.png)\n\n\u003C!-- ------------------------ --\u003E\n## Raw Layer \n### Staging the data\n\nIn this section, we will take the acquired data from the Internal Table Stage mentioned in the previous section and load it into the staging tables in the Raw layer.  We will load LINE_ITEM_STG, ORDER_STG, PART_STG, and PARTSUPP_STG tables.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_load_stg_diagram.png)\n\u003E \n\u003E\n\u003EThis diagram illustrates the first steps of loading the new data files into the raw layer tables.\n\u003E\n\u003E - **Incremental Processing Step 1** is to define the logical partitions.  In the line item data we have identified the orderdate as the logical partition.\n\u003E - New data files from the Online Orders data source are placed into cloud storage.\n\u003E - A Truncate/Reload pattern is used here to load the LINE_ITEM_STG table.\n\u003E - First we truncate the prior data, and then load the new data into the LINE_ITEM_STG table.\n \n#### Step 1 - Explain code snippets\n\n##### LINE_ITEM_STG_LD.SQL\n\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 200_raw/line_item_stg_ld.sql\n2. In the code, after setting the context, the next step is to truncate the line_item_stg table to remove any old ddata from the previous run.\n``` sql\ntruncate table line_item_stg;\n```\n3. Below in the *\"copy into\"* statement, the data from the files produced in the acquisition steps will be loaded in one statement.  This is a bulk load.  \n4. The purge parameter is set to true so that the files will be purged from the Internal Table Stage once they have been loaded.  This saves on storage usage and cost since these files are no longer needed.\n``` sql\n-- perform bulk load\ncopy into\n    line_item_stg\nfrom\n    (\n    select\n         s.$1                                            -- l_orderkey\n        ,s.$2                                            -- o_orderdate\n        ,s.$3                                            -- l_partkey\n        ,s.$4                                            -- l_suppkey\n        ,s.$5                                            -- l_linenumber\n        ,s.$6                                            -- l_quantity\n        ,s.$7                                            -- l_extendedprice\n        ,s.$8                                            -- l_discount\n        ,s.$9                                            -- l_tax\n        ,s.$10                                           -- l_returnflag\n        ,s.$11                                           -- l_linestatus\n        ,s.$12                                           -- l_shipdate\n        ,s.$13                                           -- l_commitdate\n        ,s.$14                                           -- l_receiptdate\n        ,s.$15                                           -- l_shipinstruct\n        ,s.$16                                           -- l_shipmode\n        ,s.$17                                           -- l_comment\n        ,s.$18                                           -- last_modified_dt\n        ,metadata$filename                               -- dw_file_name\n        ,metadata$file_row_number                        -- dw_file_row_no\n        ,current_timestamp()                             -- dw_load_ts\n    from\n        @~ s\n    )\npurge         = true\npattern       = '.*line_item/data.*\\.csv\\.gz'\nfile_format   = ( type=csv field_optionally_enclosed_by = '\"' )\non_error      = skip_file\n--validation_mode = return_all_errors\n;\n```\n\n### Step 2 - Execute code and Verify Results\nIn this step we will load 3 _stg tables: LINE_ITEM_STG, ORDERS_STG, PART_STG and PARTSUPP_STG.\n\n#### LINE_ITEM_STG_LD.SQL\n1. First, we will load the Line Item data into the LINE_ITEM_STG table.\n2. Make sure you have 200_raw/line_item_stg_ld.sql script open in Snowsight.  \n3. Setting the context of our session, highlight these SQL statements in your worksheet, and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Highlight the truncate command in the script and run it.\n``` sql\ntruncate table line_item_stg;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n5. Set your cursor on the *\"copy into\"* command and run it.  On a small warehouse this will take approximately 1 minute to load the files. The results should look like this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_stg_load_complete.png)\n\n6. Let's verify the data was loaded.  Highlight the following SQL statement in your worksheet and run it.  \n``` sql\nselect \n    *\nfrom \n    table(information_schema.copy_history(table_name=\u003E'LINE_ITEM_STG', start_time=\u003E dateadd(hours, -1, current_timestamp())))\nwhere\n    status = 'Loaded'\norder by\n    last_load_time desc\n;\n```\n7.  The results will look similar to this.  The file name might differ.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_copy_history_results.png)\n\n8. Let's verify that the lines that we are monitoring are loaded into the LINE_ITEM_STG table. Highlight this SQL statement in your worksheet and run it.\n``` sql\nselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item_stg \nwhere l_orderkey = 5722076550\nand l_partkey in ( 105237594, 128236374); -- 2 lines\n```\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_verify_stg_ld.png)\n\n##### PART_STG_LD.SQL\n\n1. Next, we will load the Part data into the PART_STG table. \n2. Select *\"create worksheet from SQL file\"* and load the 200_raw/part_stg_ld.sql.  \n3. Setting the context of our script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Highlight the truncate command in the script and run it.\n``` sql\ntruncate table part_stg;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n5. Set your cursor on the *\"copy into\"* command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_part_stg_results.png)\n\n##### ORDERS_STG_LD.SQL\n\n1. Finally, we will load the Orders data into the ORDERS_STG table. \n2. Select to *\"create worksheet from SQL file\"* and load the 200_raw/orders_stg_ld.sql.  \n3. Setting the context of our script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Highlight the truncate command in the script and run it.\n``` sql\ntruncate table orders_stg;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n5. Set your cursor on the *\"copy into\"* command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_orders_stg_results.png)\n\n##### PARTSUPP_STG_LD.SQL\n\n1. Finally, we will load the Orders data into the PARTSUPP_STG table. \n2. Select to *\"create worksheet from SQL file\"* and load the 200_raw/partsupp_stg_ld.sql.  \n3. Setting the context of our script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Highlight the truncate command in the script and run it.\n``` sql\ntruncate table partsupp_stg;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n5. Set your cursor on the *\"copy into\"* command and run it.  On a small warehouse this will take approximately 2 minutes to load the files. The results should look like this.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_partsupp_stg_results.png)\n\n\u003C!-- ------------------------ --\u003E\n### Identify Impacted Partitions\n\nIn this section we will identify the impacted partitions that were loaded into the staging tables in the Raw Layer and persist those identified partitions in a table for use in subsequent steps.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_impacted_partitions.png)\n\n\n\u003E\n\u003EIn this diagram illustrates the concept of identifying the impacted partitions, persisting them to be utilized in all the layers of the data architecture to incrementally process the data.\n\u003E\n\u003E - **Incremental Processing Step 2** is to identify the impacted partitions.  \n\u003E - In this diagram, we select all the distinct orderdates from the LINE_ITEM_STG table that are impacted by the new data that was loaded.  \n\u003E - Then we persist those dates into a table called *\"dw_delta_date\"* so that we can utilize those impacted partitions to do our incremental processing at each layer (Raw, Integration and Presentation).\n\n#### Step 1 - Explain code snippets\n\n##### DW_DELTA_DATE_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 200_raw/dw_delta_date_ld.sql\n2. After setting the context there is an *\"insert\"* statement.  As part of the *\"insert\"* statement, there is a [CTE (Common Table Expression)](https://docs.snowflake.com/en/user-guide/queries-cte.html) identified by the *\"with\"* statement inside the *\"insert\"* statement. This *\"select\"* identifies all the orderdates that were impacted with the load into the _STG table.\n``` sql\ninsert overwrite into dw_delta_date\nwith l_delta_date as\n(\n    select distinct\n        o_orderdate as event_dt\n    from\n        dev_webinar_orders_rl_db.tpch.line_item_stg \n)\nselect\n     event_dt\n    ,current_timestamp()            as dw_load_ts\nfrom\n    l_delta_date\norder by\n    1\n;\n```\n\n#### DW_DELTA_DATE_RANGE_F.SQL (Additional Information)\n\n1. As part of the objects we created back in the Getting Started Section, we created a table function called DW_DELTA_DATE_RANGE_F. This table function will take a type of time period such as day, week, month, quarter and year as a parameter, and return rows with a start_date and end_date of that period.  We want the end_dt to be one greater than the actual max when returned from the function.  This is so in our code for incremental processing we can use less than that end_date, to catch all data up through the end of that date.\n2. To review the code in Snowsight, *\"create worksheet from SQL file\"*, select the 000_admin/dw_delta_date_range_f.sql\n``` sql\nuse schema   &{l_common_schema};;\n\ncreate or replace function dw_delta_date_range_f\n(\n    p_period_type_cd   varchar\n)\nreturns table( start_dt timestamp_ltz, end_dt timestamp_ltz )\nas\n$$\n    select\n         start_dt\n        ,end_dt\n    from\n        (\n        select\n             case lower( p_period_type_cd )\n                 when 'all'     then current_date()\n                 when 'day'     then date_trunc( day, event_dt )\n                 when 'week'    then date_trunc( week, event_dt )\n                 when 'month'   then date_trunc( month, event_dt )\n                 when 'quarter' then date_trunc( quarter, event_dt )\n                 when 'year'    then date_trunc( year, event_dt )\n                 else current_date()\n             end                as partition_dt\n            ,min( event_dt ) as start_dt\n            ,dateadd( day, 1, max( event_dt ) ) as end_dt  -- To provide end date \n        from\n            dw_delta_date\n        group by\n            1\n        )\n    order by\n        1\n$$\n;\n```\n\n### Step 2 - Execute code and Verify Results\n\n#### DW_DELTA_DATE_LD.SQL\n\n1. Setting the context of our script.  Highlight these in your worksheet, and run them to set the context.\n``` sql\nuse database dev_webinar_common_db;\nuse schema util;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Set the cursor on the *\"insert overwrite\"* statement and run it. The output should look like the following.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_results.png)\n\n3. To verify that the order dates for 17 day window were loaded into the DW_DELTA_DATE table, highlight this query into your worksheet and run it.\n``` sql\nselect * \nfrom dev_webinar_common_db.util.dw_delta_date\norder by event_dt;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_load_verify.png)\n\n#### DW_DELTA_DATE_RANGE_F.SQL (Query the table function)\n\n1. Verify the table function DW_DELTA_DATE_RANGE_F returns results as well. Highlight the following query your worksheet and run it.  This will return 3 rows representing 3 weeks of data.  We will utilize this table function in future scripts.\n``` sql\nselect start_dt, end_dt \nFROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) \norder by 1;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/delta_date_table_function_output.png)\n\n\n\u003C!-- ------------------------ --\u003E\n### Incrementally Process\n\nIn this section we will incrementally process the data and load it into the persistent tables in the Raw layer by utilizing the impacted partitions that were identified in the prior step.  We will load LINE_ITEM, LINE_ITEM_HIST, PART, ORDERS, and PARTSUPP tables.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_incremental_processing.png)\n\n\u003E \n\u003EIn this diagram we are illustrating the concept of \"looping\" through the impacted partitions and processing only that data impacted into the persistent tables in the raw layer.\n\u003E\n\u003E -  **Incremental Processing Step 3** is to utilize the impacted partitions to process the data through each of the layers.\n\u003E - Here we are using the order dates stored in the DW_DELTA_DATE table to identify the dates that need to be processed into the LINE_ITEM_HIST and LINE_ITEM tables.  \n\u003E - In the LINE_ITEM_HIST table we are capturing the life cycle of the rows as they change. This is for audit purposes or tracking.\n\u003E - In the LINE_ITEM table, we are peristing the current state of that line_item row.\n\n### Step 1 - Explain code snippets\n\n#### LINE_ITEM_HIST_LD.SQL\n\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 200_raw/line_item_hist_ld.sql\n2. After we set the context, there is the *\"execute immediate\"* statement.  Since we are using [SQL Scripting](https://docs.snowflake.com/en/developer-guide/snowflake-scripting/index.html) this statement is used to create an anonymous block.\n``` sql\nexecute immediate $$\n```\n3. In the declaration section, we are defining 2 variables (l_start_dt, and l_end_dt) that we will use to process the logical partition start and end dates.  \n``` sql\ndeclare\n  l_start_dt date;\n  l_end_dt   date;\n```\n4. Then we are declaring the cursor which holds the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a week.  \n``` sql\ndeclare\n  ...\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n```\n5.  The *\"for\"* loop is where the script incrementally processes the data by looping through a week at a time.\n``` sql\nbegin\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n    ...\n  end for;\n\nend;\n```\n6. Scroll down to the *\"insert\"* statement.  \n7. Inside the *\"with\"* statement the script is creating a CTE named l_stg.\n8. The surrogate key and hash diff columns (_shk) being created in this CTE will be used to assist in identifying the rows that have changed.\n``` sql\ninsert into line_item_hist\nwith l_stg as\n    (\n        --\n        -- Driving CTE to identify all records in the logical partition to be processed.\n        select\n            -- generate hash key and hash diff to streamline processing\n             sha1_binary( concat( s.l_orderkey, '|', s.l_linenumber ) )  as dw_line_item_shk\n            --\n            -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n            -- actual meaningful change in the data\n            ,sha1_binary( concat( s.l_orderkey\n                                         ,'|', coalesce( to_char( s.o_orderdate, 'yyyymmdd' ), '~' )\n                                         ,'|', s.l_linenumber\n                                         ,'|', coalesce( to_char( s.l_partkey ), '~' )\n                                         ,'|', coalesce( to_char( s.l_suppkey ), '~' )\n                                         ,'|', coalesce( to_char( s.l_quantity ), '~' )\n                                         ,'|', coalesce( to_char( s.l_extendedprice ), '~' )\n                                         ,'|', coalesce( to_char( s.l_discount ), '~' )\n                                         ,'|', coalesce( to_char( s.l_tax ), '~' )\n                                         ,'|', coalesce( to_char( s.l_returnflag ), '~' )\n                                         ,'|', coalesce( to_char( s.l_linestatus ), '~' )\n                                         ,'|', coalesce( to_char( s.l_shipdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( to_char( s.l_commitdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( to_char( s.l_receiptdate, 'yyyymmdd' ), '~' )\n                                         ,'|', coalesce( s.l_shipinstruct, '~' )\n                                         ,'|', coalesce( s.l_shipmode, '~' )\n                                         ,'|', coalesce( s.l_comment, '~' )\n                                )\n        \n                        )               as dw_hash_diff\n            ,s.*\n        from\n            line_item_stg s\n```\n\n8. In the CTE, the *\"where\"* clause is filtering the data from the LINE_ITEM_STG table to select only the rows that have an orderdate that is in the logical *\"week\"* partiton.\n``` sql\ninsert into line_item_hist\nwith l_stg as\n    (\n        --\n        -- Driving CTE to identify all records in the logical partition to be processed.\n        select\n            ...\n        from\n            line_item_stg s\n        where\n                s.o_orderdate \u003E= :l_start_dt\n            and s.o_orderdate  \u003C :l_end_dt\n    )\n```\n\n9. The next CTE named l_deduped will go through and dedupe the records in the l_stg CTE, using the hash_diff to identify duplicate rows.  This eliminates duplicates from getting loaded into the permanent Raw layer LINE_ITEM_HIST table.\n``` sql\n,l_deduped as\n    (\n        --\n        -- Dedupe the records from the staging table.\n        -- This assumes that there may be late arriving or duplicate data that were loaded\n        -- Need to identify the most recent record and use that to update the Current state table.\n        -- as there is no reason to process each individual change in the record, the last one would have the most recent updates\n        select\n            *\n        from\n            l_stg\n        qualify\n            row_number() over( partition by dw_hash_diff order by last_modified_dt desc, dw_file_row_no )  = 1\n    )\n```\n\n10. The final outside *\"select\"* statement will select data from the LINE_ITEM_HIST table using the same logical *\"week\"* partition range, and compare it to the deduped staging records to identify what needs to be inserted into the LINE_ITEM_HIST table.\n``` sql\n    select\n        ...\n    from\n        l_deduped s\n    where\n        s.dw_hash_diff not in\n        (\n            -- Select only the rows in that logical partition from the final table.\n            select dw_hash_diff from line_item_hist \n            where\n                    o_orderdate \u003E= :l_start_dt\n                and o_orderdate  \u003C :l_end_dt\n        )\n```\n\n11. **Important:** To optimize query performance in Snowflake, and ensure proper clustering of data in each micropartition, the *\"order by\"* clause sorts the rows by orderdate as they are inserted into the table.\n``` sql\n    order by\n        o_orderdate  -- physically sort rows by a logical partitioning date\n    ;\n```\n#### LINE_ITEM_LD.SQL\n\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 200_raw/line_item_ld.sql\n2. The script is very similar to line_item_hist_ld.sql except this is a merge pattern. The script has the same anonymous block, variable declarations, and cursor definition using the DW_DELTA_DATE_RANGE_F table function to loop through the logical partitions, as well as the same *\"for\"* loop.\n``` sql\nexecute immediate $$\n\ndeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n    ...\n```\n\n3. The script also has the same l_stg CTE to identify the LINE_ITEM_STG records within that week of logical partitions. It is also doing the same surrogate key and hash diff derivations.\n``` sql\n       with l_stg as\n        (\n            --\n            -- Driving CTE to identify all records in the logical partition to be processed\n            --\n            select\n                -- generate hash key and hash diff to streamline processing\n                 sha1_binary( concat( s.l_orderkey, '|', s.l_linenumber ) )  as dw_line_item_shk\n                --\n                -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n                -- actual meaningful change in the data\n                --\n                ,sha1_binary( concat( s.l_orderkey\n                                     ,'|', coalesce( to_char( s.o_orderdate, 'yyyymmdd' ), '~' )\n                                     ,'|', s.l_linenumber\n                                     ,'|', coalesce( to_char( s.l_partkey ), '~' )\n                                     ,'|', coalesce( to_char( s.l_suppkey ), '~' )\n                                     ,'|', coalesce( to_char( s.l_quantity ), '~' )\n                                     ,'|', coalesce( to_char( s.l_extendedprice ), '~' )\n                                     ,'|', coalesce( to_char( s.l_discount ), '~' )\n                                     ,'|', coalesce( to_char( s.l_tax ), '~' )\n                                     ,'|', coalesce( to_char( s.l_returnflag ), '~' )\n                                     ,'|', coalesce( to_char( s.l_linestatus ), '~' )\n                                     ,'|', coalesce( to_char( s.l_shipdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( to_char( s.l_commitdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( to_char( s.l_receiptdate, 'yyyymmdd' ), '~' )\n                                     ,'|', coalesce( s.l_shipinstruct, '~' )\n                                     ,'|', coalesce( s.l_shipmode, '~' )\n                                     ,'|', coalesce( s.l_comment, '~' )\n                                    )\n            \n                            )               as dw_hash_diff\n                ,s.*\n            from\n                line_item_stg s\n            where\n                    s.o_orderdate \u003E= :l_start_dt\n                and s.o_orderdate  \u003C :l_end_dt\n        )\n```\n\n4. The script has the same dedupe logic.\n``` sql\n,l_deduped as\n    (\n        --\n        -- Dedupe the records from the staging table.\n        -- This assumes that there may be late arriving or duplicate data that were loaded\n        -- Need to identify the most recent record and use that to update the Current state table.\n        -- as there is no reason to process each individual change in the record, the last one would have the most recent updates\n        select\n            *\n        from\n            l_stg\n        qualify\n            row_number() over( partition by dw_hash_diff order by last_modified_dt desc, dw_file_row_no )  = 1\n    )\n```\n5. However...the script does have an additional CTE l_tgt. This CTE is important for partition pruning efficiencies because it is selecting only those rows from the LINE_ITEM table that are in the logical partition range we are processing.  \n``` sql\n,l_tgt as\n        (\n            --\n            -- Select the records in the logical partition from the current table. \n            -- Its own CTE, for partition pruning efficiencies\n            select *\n            from line_item\n            where\n                    o_orderdate \u003E= :l_start_dt\n                and o_orderdate  \u003C :l_end_dt\n        )\n```\n6. Now let's look at the *\"merge\"* statement.  In the *\"select\"* statement below, the l_deduped CTE and l_tgt CTE are joined together with a left join to identify the rows that are in the LINE_ITEM_STG table that might not be in the LINE_ITEM table; or where the hash_diff is different and the modified date is after what already exists in the table.  \n``` sql\n-- Merge Pattern \n    --\n    merge into line_item tgt using\n    (\n      ...\n        select\n             current_timestamp()        as dw_version_ts\n            ,s.*\n        from\n            l_deduped s\n            left join l_tgt t on\n                t.dw_line_item_shk = s.dw_line_item_shk\n        where\n            -- source row does not exist in target table\n            t.dw_line_item_shk is null\n            -- or source row is more recent and differs from target table\n            or (\n                    t.last_modified_dt  \u003C s.last_modified_dt\n                and t.dw_hash_diff     != s.dw_hash_diff\n               )\n        order by\n            s.o_orderdate  -- physically sort rows by logical partitioning date\n    ) src\n```\n7. **Important:** Please note the *\"on\"* clause of the *\"merge\"* statement.  The logical partition dates are used to prevent a full table scan of the LINE_ITEM table.\n``` sql\n-- Merge Pattern \n    --\n    merge into line_item tgt using\n    (\n      ...\n    on\n    (\n            tgt.dw_line_item_shk = src.dw_line_item_shk\n        and tgt.o_orderdate     \u003E= :l_start_dt\n        and tgt.o_orderdate      \u003C :l_end_dt\n    )\n```\n### Step 2 - Execute code and Verify Results\n\n#### LINE_ITEM_HIST_LD.SQL\n1. Select *\"create worksheet from SQL file\"*, select the 200_raw/line_item_hist_ld.sql. Set the context of our script. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Put your cursor on the *\"execute immediate\"* command at the top of the script and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n3. Let's verify that the data was loaded into the LINE_ITEM_HISTORY table. Highlight the following query in your worksheet. If you have run these acquisition and load scripts multiple times you may see history changes in this table.\n\n\u003E \n\u003E If you are using the Snowflake Extension for VS Code to run the scripts, the SHA1_BINARY fields will not be displayed properly. To confirm that the SHA1_BINARY was created properly, run the following query in Snowflake. \n\n``` sql\nselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item_hist \nwhere l_orderkey = 5722076550 \nand l_partkey in ( 105237594, 128236374)\norder by 1;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_hist_output.png)\n\n#### LINE_ITEM_LD.SQL\n\n1. Select *\"create worksheet from SQL file\"* and open the the 200_raw/line_item_ld.sql file.  \n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Put your cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n3. Let's verify that the data was loaded into the LINE_ITEM table. Highlight the following query in your worksheet and run it.\n``` sql\nselect * \nfrom dev_webinar_orders_rl_db.tpch.line_item \nwhere l_orderkey = 5722076550 \nand l_partkey in ( 105237594, 128236374)\norder by 1;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_line_item_hist_output.png)\n\n#### PART_LD.SQL\n1. Now we want to load the Part data into the PART table.  \n2. Select *\"create worksheet from SQL file\"* and open the worksheet 200_raw/part_ld.sql.  \n3. Highlight the following SQL statements in the worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Put your cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n3. Verify the PART table was loaded.  Highlight the following query and run it.\n``` sql\nselect *\nfrom dev_webinar_orders_rl_db.tpch.part\nwhere p_partkey in ( 105237594, 128236374);\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_part_verify.png)\n\n#### ORDER_LD.SQL\n1. Now we want to load the Order data into the ORDERS table.  \n2. Select *\"create worksheet from SQL file\"* and open the 200_raw/order_ld.sql file.  \n3. Highlight the following SQL statements in the worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Put your cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n3. Verify the ORDERS table was loaded with the order data.  Highlight the following query and run it.\n``` sql\nselect * \nfrom dev_webinar_orders_rl_db.tpch.orders \nwhere o_orderkey = 5722076550;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_order_verify.png)\n\n#### PARTSUPP_LD.SQL\n1. Now we want to load the partsupp data into the PARTSUPP table.  \n2. Select *\"create worksheet from SQL file\"* and open the 200_raw/partsupp_ld.sql file.  \n3. Highlight the following SQL statements in the worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_orders_rl_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n2. Put your cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n3. Verify the PARTSUPP table was loaded with the partsupp data.  Highlight the following query and run it.\n``` sql\nselect * \nfrom dev_webinar_orders_rl_db.tpch.partsupp\nwhere ps_partkey in ( 105237594, 128236374);\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_partsupp_verify.png)\n\n\u003C!-- ------------------------ --\u003E\n### Integration Layer\n\nIn this step we will incrementally process an isolated unit of work, deriving certain business rules from the impacted partitions that we previously identified. \n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/integration_incremental_process.png)\n\u003E \n\u003E\n\u003EThis diagram illustrates the steps for incremental processing units of work in the integration layer of the DCDF data architecture.\n\u003E\n\u003E - A unit of work has been identified where the line item margin needs to be derived/calculated for each line item.\n\u003E - This is an isolated unit of work with business rules that should be incrementally processed each time new or changed line items are processed.  \n\u003E - Extensible, ease of maintenance for that unit of work.  As rules change to derive the margin, then they can be added/changed here in one place.\n\u003E - The impacted logical partitions have been identified and will be incrementally processed.  \n\n#### Step 1 - Explain code snippets\n##### LINE_ITEM_MARGIN_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 310_derivation/line_item_margin_ld.sql\n2. This script is also using the anonymous block in SQL Scripting. \n``` sql\nexecute immediate $$\n```\n3. Again, the cursor is being declared using the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a given week. We are going to loop through the weeks and process a week at a time. This is the incremental processing.\n\n``` sql\ndeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n```\n4. Just like the line_item_ld.sql file in the previous section, the merge processing pattern is being utilized here to select data by using the logical partitions. **Important:** Another note is the *\"on\"* clause of the *\"merge\"* statement.\n``` sql\nmerge into line_item_margin t using\n    (\n        with l_src as\n        (\n            -- \n            -- Driving CTE to identify all the records in the logical partition to be process\n            --\n            select\n                 s.dw_line_item_shk\n                ,s.o_orderdate\n                ,s.l_extendedprice - (s.l_quantity * p.ps_supplycost ) as margin_amt\n                ,s.last_modified_dt\n            from\n                dev_webinar_orders_rl_db.tpch.line_item s\n                join dev_webinar_orders_rl_db.tpch.partsupp p\n                  on ( p.ps_partkey = s.l_partkey\n                       and p.ps_suppkey = s.l_suppkey )\n            where\n                    s.o_orderdate \u003E= :l_start_dt\n                and s.o_orderdate  \u003C :l_end_dt\n        )\n        ,l_tgt as\n        (\n            -- \n            -- Select the records in the logical partition from the current table.\n            -- Its own CTE, for partition pruning efficiencies\n            select *\n            from line_item_margin\n            where\n                    o_orderdate \u003E= :l_start_dt\n                and o_orderdate  \u003C :l_end_dt\n        )\n        select\n             current_timestamp()        as dw_update_ts\n            ,s.*\n        from\n            l_src s\n            left join l_tgt t on\n                t.dw_line_item_shk = s.dw_line_item_shk\n        where\n            -- source row does not exist in target table\n            t.dw_line_item_shk is null\n            -- or source row is more recent and differs from target table\n            or (\n                    t.last_modified_dt  \u003C s.last_modified_dt\n                and t.margin_amt       != s.margin_amt\n               )\n        order by\n            s.o_orderdate\n    ) s\n    on\n    (\n        t.dw_line_item_shk = s.dw_line_item_shk\n        and t.o_orderdate \u003E= :l_start_dt \n        and t.o_orderdate  \u003C :l_end_dt\n    )\n```\n\n#### Step 2 - Execute code and Verify Results\n##### LINE_ITEM_MARGIN_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"* and open the line_item_margin_ld.sql file. \n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_il_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n3. Place your cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n4. Let's verify that the data was loaded into the LINE_ITEM_MARGIN table. Highlight the following SQL query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table.\n``` sql\n-- Integration\nselect m.*\nfrom dev_webinar_il_db.main.line_item_margin m\n    join dev_webinar_orders_rl_db.tpch.line_item l\nwhere l.l_orderkey = 5722076550 \nand l.l_partkey in ( 105237594, 128236374)\nand m.dw_line_item_shk = l.dw_line_item_shk;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/integration_line_item_margin_results.png)\n\u003C!-- ------------------------ --\u003E\n### Presentation Layer\n\nIn this step we will incrementally process the data that was loaded in the previous section, and re-organizing the data for consumption from the Presentation layer, utilizing the identified impacted partitions.\n\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_incremental_process.png)\n\u003E \n\u003E\n\u003EIn this diagram illustrates the steps for incremental processing for the Presentation layer of the DCDF data architecture.\n\u003E\n\u003E - Order_line_fact is a purpose built solution for consumption and analytics.\n\u003E - Dimensional model as an example to provide a solution around the orders lines and consumption at that atomic level of data.\n\u003E - The impacted logical partitions have been identified and will be incrementally processed in the presentation layer.\n\n#### Step 1 - Explain code snippets\n##### ORDER_LINE_FACT_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 410_fact_atomic/order_line_fact_ld.sql\n2. This script uses the anonymous block in SQL Scripting. \n``` sql\nexecute immediate $$\n```\n3. Again, the cursor is being declared using the result of the DW_DELTA_DATE_RANGE_F table function that returns a start and end date for a given week. We are going to loop through the weeks and process a week at a time. This is the incremental processing.\n``` sql\ndeclare\n  l_start_dt date;\n  l_end_dt   date;\n  -- Grab the dates for the logical partitions to process\n  c1 cursor for select start_dt, end_dt FROM table(dev_webinar_common_db.util.dw_delta_date_range_f('week')) order by 1;\n\nbegin\n\n  --\n  -- Loop through the dates to incrementally process based on the logical partition definition.\n  -- In this example, the logical partitions are by week.\n  --\n  for record in c1 do\n    l_start_dt := record.start_dt;\n    l_end_dt   := record.end_dt;\n```\n4.  Within the *\"for\"* loop is a delete/insert processing pattern. The *\"delete\"* statement will delete from the ORDER_LINE_FACT table any rows within the logical partition. This logical partition is the week of order dates in our example. Then the *\"insert\"* will insert all the rows for that logical partition into the ORDER_LINE_FACT table.\n5. We first apply deletes and then inserts in order to handle situations such as late arriving data. For instance if the supplier wasn't available when the order line item was first ordered, that value would be some default value. Then as the status of the line item changes, and the supplier was updated, the original defaulted row would need to be removed.\n``` sql\n-- Delete the records using the logical partition \n     -- Very efficient when all the rows are in the same micropartitions.  Mirrors a truncate table in other database platforms.\n     delete from order_line_fact\n     where orderdate \u003E= :l_start_dt\n       and orderdate \u003C  :l_end_dt;\n \n     -- Insert the logical partitioned records into the table\n     -- Inserts data from same order date into the same micropartitions\n     -- Enables efficient querying of the data for consumption\n     insert into order_line_fact\n     select\n         li.dw_line_item_shk\n        ,o.o_orderdate\n        ,o.dw_order_shk\n        ,p.dw_part_shk\n        ,s.dw_supplier_shk\n        ,li.l_quantity      as quantity\n        ,li.l_extendedprice as extendedprice\n        ,li.l_discount      as discount\n        ,li.l_tax           as tax\n        ,li.l_returnflag    as returnflag\n        ,li.l_linestatus    as linestatus\n        ,li.l_shipdate\n        ,li.l_commitdate\n        ,li.l_receiptdate\n        ,lim.margin_amt\n        ,current_timestamp() as dw_load_ts\n     from\n         webinar_rl_db.tpch.line_item li\n         --\n         join webinar_rl_db.tpch.orders o\n           on o.o_orderkey = li.l_orderkey\n         --\n         join webinar_il_db.main.line_item_margin lim\n           on lim.dw_line_item_shk = li.dw_line_item_shk\n         --\n         -- Left outer join in case the part record is late arriving\n         --\n         left outer join webinar_rl_db.tpch.part p\n           on p.p_partkey = li.l_partkey\n         --\n         -- left outer join in case the supplier record is late arriving\n         --\n         left outer join webinar_rl_db.tpch.supplier s\n           on s.s_suppkey = li.l_suppkey\n     where \n             li.o_orderdate \u003E= :l_start_dt\n         and li.o_orderdate \u003C  :l_end_dt\n     order by o.o_orderdate;\n```\n6. Let's look at a dimension table load to illlustrate a different processing pattern *\"insert overwrite\"*.\n7. In Snowsight, *\"create worksheet from SQL file\"*, select the 400_dimension/part_dm_ld.sql\n8. This script is also using the anonymous block in SQL Scripting. \n``` sql\nexecute immediate $$\n```\n9. This script uses an *\"insert overwrite\"* pattern where the code will essentially be doing a truncate/insert. The advantage of doing *\"insert overwrite\"*, versus a truncate/insert, is that the table will never be empty. With a truncate/insert pattern there could be a time period in which the table is empty if users were to query the data.\n\n``` sql\ninsert overwrite into part_dm\n   select\n       p.dw_part_shk\n      ,p.p_partkey\n      ,p.p_name as part_name\n      ,p.p_mfgr as mfgr\n      ,p.p_brand as brand\n      ,p.p_type as type\n      ,p.p_size as size\n      ,p.p_container as container\n      ,p.p_retailprice as retail_price\n      ,p.p_comment as comment\n      ,d.first_orderdate\n      ,p.last_modified_dt\n      ,p.dw_load_ts\n      ,p.dw_update_ts\n   from\n       dev_webinar_orders_rl_db.tpch.part p\n       left join dev_webinar_il_db.main.part_first_order_dt d\n         on d.dw_part_shk = p.dw_part_shk;\n```\n\n#### Step 2 - Execute code and Verify Results\n##### ORDER_LINE_FACT_LD.SQL\n\n1. In Snowsight, *\"create worksheet from SQL file\"* and open the worksheet for the 410_fact_atomic/order_line_fact_ld.sql.\n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n3. Place the cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n4. Let's verify that the data was loaded into the ORDER_LINE_FACT table. Highlight the following query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table. \n``` sql\nselect olf.*\nfrom dev_webinar_pl_db.main.order_line_fact olf\n  join dev_webinar_orders_rl_db.tpch.line_item l\n    on l.dw_line_item_shk = olf.dw_line_item_shk\nwhere l.l_orderkey = 5722076550 \nand l.l_partkey in ( 105237594, 128236374);\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_order_line_results.png)\n\n#### PART_DM_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"* and open the worksheet for the 400_dimension/part_dm_ld.sql. \n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n```\n3. Place the cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/anonymous_block_success.png)\n\n4. Let's verify that the data was loaded into the PART_DM table. Highlight the following query in your worksheet. If you have run these load scripts multiple times you may see history changes in this table. \n``` sql\nselect *\nfrom dev_webinar_pl_db.main.part_dm p\nwhere p_partkey in ( 105237594, 128236374);\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_part_dm_output.png)\n\n\n\u003C!-- ------------------------ --\u003E\n## BONUS -Slowly Changing Dimension\n\n\u003E \n\u003ESlowly changing dimension type 2 changes add a new row in the dimension with the updated attribute values. This requires generalizing the primary key of the dimension beyond the natural or durable key because there will potentially be multiple rows describing each member. When a new row is created for a dimension member, a new primary surrogate key is assigned and used as a foreign key in all fact tables from the moment of the update until a subsequent change creates a new dimension key and updated dimension row. -  Kimball Group.\n\nIn this section we will go through incremental processing of a Type 2, slowly changing dimension; customer.   We will go through running each layer in the DCDF for this customer data.  \n\n### Step 1 - Acquistion\n#### CUSTOMER_ACQ.SQL\n1. In Snowsight,  *\"create worksheet from SQL file\"*, select the 100_acquisition/customer_acq.sql.\n2. In the first few lines of the script we are setting the context for this script.\n3. Here you will see a *\"union all\"* statement where we are creating some modified data for a specific customer to illustrate changes over time to the data.\n4. The *\"copy into\"* statement is where we are copying (Unloading) the data from the SNOWFLAKE_SAMPLE_DATA into CSV formatted files into an internal table stage.  \n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_acq_results.png)\n\n### Step 2 - Raw Layer\n#### CUSTOMER_STG_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 100_acquisition/customer_stg_ld.sql.\n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n3. Place the cursor on the *\"truncate table \"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Place the cursor on the *\"execute immediate\"* command and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_ld_results.png)\n\n5. Let's verify that the data was loaded into the CUSTOMER_STG table.  Highlight the following query at the bottom of your worksheet and run it.\n``` sql\nselect *\nfrom dev_webinar_orders_rl_db.tpch.customer_stg\nwhere c_custkey in (50459048);\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_verify_results.png)\n\n#### DW_DELTA_DATE_LD_BONUS.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 200_raw/dw_delta_date_ld_bonus.sql.  \n2. This script has been updated to identify logical partitions in the CUSTOMER_STG table as well as the LINE_ITEM_STG.\n3. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Place the cursor on the *\"insert overwrite\"* statement and run it.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_dw_delta_date_bonus.png)\n\n#### CUSTOMER_HIST_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 100_acquisition/customer_hist_ld.sql.\n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_rl_orders_db;\nuse schema   tpch;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n3. Scroll down to the dw_customer_shk surrogate key generation in the *\"with\"* statement.  We are generating a surrogate key for each combination of the c_custkey and the change_date.  We want to use this in the presentation layer to match an order to the correct version of the customer at the time of the order.\n``` sql\n    select\n        -- generate hash key and hash diff to streamline processing\n        sha1_binary( s.c_custkey || to_char( s.change_date, 'yyyymmdd' ) )  as dw_customer_shk\n        --\n        -- note that last_modified_dt is not included in the hash diff since it only represents recency of the record versus an \n        -- actual meaningful change in the data\n        ,sha1_binary( concat( s.c_custkey || to_char( s.change_date, 'yyyymmdd' )\n                                     ,'|', coalesce( to_char( s.change_date, 'yyyymmdd'), '~' )\n                                     ,'|', coalesce( s.c_name, '~' )\n                                     ,'|', coalesce( s.c_address, '~' )\n                                     ,'|', coalesce( to_char( s.c_nationkey ), '~' )\n                                     ,'|', coalesce( s.c_phone, '~' )\n                                     ,'|', coalesce( to_char( s.c_acctbal ), '~' )\n                                     ,'|', coalesce( s.c_mktsegment, '~' )\n                                     ,'|', coalesce( s.c_comment, '~' )\n                            )\n     \n                    )               as dw_hash_diff\n        ,s.*\n    from\n        customer_stg s\n    where\n            s.change_date \u003E= :l_start_dt\n        and s.change_date  \u003C :l_end_dt\n```\n4. Place the cursor on the *\"execute immediate\"* command and run it.  Here we are loading the CUSTOMER_HIST table using the same repeatable pattern as we did with the LINE_ITEM_HIST table.  \n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_stg_ld_results.png)\n\n5. Let's verify that the data was loaded into the CUSTOMER_HIST table.  Highlight the following query at the bottom of your worksheet and run it.   \n``` sql\nselect *\nfrom dev_webinar_orders_rl_db.tpch.customer_hist\nwhere c_custkey in (50459048)\norder by 5;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/raw_layer_customer_hist_results.png)\n\n### Step 3 - Integration Layer\nThere are no steps here for the integration layer as there aren't any business rules being defined for customer.\n\n### Step 4 - Presentation Layer\n#### CUSTOMER_DM.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 400_dimension/customer_dm_ld.sql.\n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n3. Place the cursor on the *\"execute immediate\"* command and run it.  Here we are loading the CUSTOMER_DM table.  \n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Let's veriy that the data was loaded into the CUSTOMER_DM table.  Highlight the query below in your worksheet and run it.  There should be two rows, where the values of the c_acctbal are different.\n``` sql\nselect *\nfrom dev_webinar_pl_db.main.customer_dm\nwhere c_custkey in (50459048)\norder by 5;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_customer_dm_results.png)\n\n#### ORDER_LINE_FACT_BONUS_LD.SQL\n1. In Snowsight, *\"create worksheet from SQL file\"*, select the 410_fact_atomic/order_line_fact_bonus_ld.sql.\n2. Highlight the following SQL statements in your worksheet and run them to set the context.\n``` sql\nuse role     sysadmin;\nuse database dev_webinar_pl_db;\nuse schema   main;\nuse warehouse dev_webinar_wh;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n3. Place the cursor on the *\"execute immediate\"* command and run it.  Here we are loading the ORDER_LINE_FACT_BONUS table.  \n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n4. Let's verify that the data was loaded into the ORDER_LINE_FACT_BONUS table and that we can see the one order with multiple line_items and the c_acctbal column is 1,293. Highlight the query below in your worksheet and run it.\n``` sql\nselect \n     c.c_custkey\n    ,c.c_name\n    ,c.c_acctbal\n    ,olf.*\nfrom dev_webinar_pl_db.main.customer_dm c\n   join dev_webinar_pl_db.main.order_line_fact_bonus olf\n     on olf.dw_customer_shk = c.dw_customer_shk\nwhere c.c_custkey in (50459048);\norder by olf.orderdate;\n```\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/presentation_order_line_fact_bonus.png)\n\n\u003C!-- ------------------------ --\u003E\n## Cleanup\n\nThis step is to cleanup and drop all the objects we created as part of this quickstart.\n\n1. Select to *\"create worksheet from SQL file\"* and open the worksheet for the 000_admin/cleanup.sql.  \n``` sql\n-- Cleanup all the objects we created\n\nuse role sysadmin;\n\ndrop database dev_webinar_orders_rl_db;\ndrop database dev_webinar_il_db;\ndrop database dev_webinar_pl_db;\ndrop database dev_webinar_common_db;\n```\n2. Run all the SQL statements to drop all the objects that were created.\n![img](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/dcdf-incremental-processing/Statement_executed_successfully.png)\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion & Next Steps\n\nThis tutorial was designed as a hands-on introduction to the Data Cloud Deployment Framework (DCDF) data architecture incremental processing and logical partitions.\n\nWe encourage you to continue learning about the Data Cloud Deployment Framework, by watching the [Data Cloud Deployment Framework Series Webinars](/webinar/for-customers/data-cloud-deployment-framework-series/) either on-demand on register for upcoming episodes.  \n\nAlso the github repo contains more scripts than what was covered in this lab.  It's a full, working template model taking source data from the Raw layer, through the Integration layer, and finally to the Presentation layer dimension model, ready for consumption. Please take the time to go through each one of these scripts and slowly work through the examples. Feel free to use these as code templates to be implemented in your own environments and accounts for your data processing.\n\nDuring this quickstart, our hope is that you noticed the repeatable patterns in these scripts which can facilitate an Agile Development Process.\n\n### What we have covered\n- A data pipeline utilizing incremental processing and logical partition definitions\n- Introduced the concepts of the Raw, Integration and Presentation layers and how each utilizes incremental processing and logical partition definitions.\n- Walked through code examples used for different processing patterns such as truncate/reload, insert overwrite, merge and delete/insert.\n\n### Potential Next Steps\n- Create a stored procedure for the LINE_ITEM_MARGIN business rules logic\n- Create streams on the LINE_ITEM raw layer table. \n- Create a Snowflake Task to execute the LINE_ITEM_MARGIN stored procedure when there is data in the LINE_ITEM stream.\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-a322a98d8f","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-d5e87e90ca",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-72de82789c","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-b11d0d1000","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-e3bd29fbbb",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-95c23ee334",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-66042eec90","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-e7b9e4b6ac","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/dcdf-incremental-processing",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EDCDF Data Architecture\u003C/h2\u003E","\u003Ch2\u003ELab Overview\u003C/h2\u003E","\u003Ch2\u003EQuickstart Setup\u003C/h2\u003E","\u003Ch2\u003ESnowflake Setup\u003C/h2\u003E","\u003Ch2\u003ERaw Layer\u003C/h2\u003E","\u003Ch2\u003EBONUS -Slowly Changing Dimension\u003C/h2\u003E","\u003Ch2\u003ECleanup\u003C/h2\u003E","\u003Ch2\u003EConclusion & Next Steps\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-d6e72f42bb","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/dcdf-incremental-processing",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"markup_editor":{"id":"markup-editor-80c2faaf23","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:1fr 0}.qs-disclaimer-text p \u003E span{font-size:15px !important}@media (min-width:768px){#quickstart-template-main-flexible-container{padding:24px 32px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:48px}}@media (max-width:767px){#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:0}}@media (min-width:1024px){#quickstart-template-main-flexible-container{padding:0 92px 48px 92px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:117px}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["quickstart_hero","flexible_column_cont","markup_editor"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"layout":"SIMPLE","id":"container-2d6cc3417e",":type":"snowflake-site/components/modal/modal-container",":items":{},":itemsOrder":[]},"experiencefragment-footer":{"id":"experiencefragment-7e91bd2ff1","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"additionalClasses":"sf-footer","layout":"SIMPLE","id":"container-0f60e7e7d2",":type":"snowflake-site/components/container",":items":{"container_copy":{"additionalClasses":"sf-footer__inner","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-cb80bbed72",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-6ce81a3f89","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"sf-footer-grid","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f1b175f2ac",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_1622723482":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-621375444b",":type":"snowflake-site/components/container",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-b1592e2321",":type":"snowflake-site/components/container",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-c2e84e2b91",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-fe157771c8","additionalClasses":"sf-footer__newsletter-title","text":"\u003Cp\u003E\u003Cb\u003ESubscribe to our monthly newsletter\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003EStay up to date on Snowflake’s latest products, expert insights and resources—right in your inbox!\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-04"},"marketo_v2":{"id":"marketo-v2-cb11f2d7e4","marketoForm":{"successUrl":null,"edit":false,"formId":"45871","hidden":null,"script":null,"values":null},"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com","marketoConfigured":true,"formConfigured":true,":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-71aafc9eef",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-2baaac08c4","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-632e072be4","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"container_copy_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-c6db5e0a4a",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-b2de676377","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003E\u003Ca href=\"/en/solutions/industries/\"\u003EIndustries\u003C/a\u003E\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/advertising-media-entertainment/\"\u003EAdvertising, Media & Entertainment\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/financial-services/\"\u003EFinancial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/healthcare-and-life-sciences/\"\u003EHealthcare & Life Sciences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/manufacturing/\"\u003EManufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/public-sector/\"\u003EPublic Sector\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/retail-consumer-goods/\"\u003ERetail & Consumer Goods\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/telecom/\"\u003ETelecom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/solutions/industries/technology/\"\u003ETechnology\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-279da3699d",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-1a5126d92b","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ECompany\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/about-snowflake/\"\u003EAbout Snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/leadership-and-board/\"\u003ELeadership & Board\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://careers.snowflake.com/us/en\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECareers\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://investors.snowflake.com/overview/default.aspx\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EInvestor Relations\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://trust.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETrust Center\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/brand-guidelines/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBrand Guidelines\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/contact/\"\u003EContact\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/news/\"\u003ENewsroom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/esg/\"\u003EEnvironmental, Social & Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/snowflake-ventures/\"\u003ESnowflake Ventures\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/end-data-disparity/\"\u003EEnd Data Disparity\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/summit/\"\u003ESnowflake Summit 26\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-d9c7d7278e",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-429d591f03","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ELearn\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/en/resources/\"\u003EResource Library\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/webinars/demo/\"\u003ELive Demos\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/\"\u003EFundamentals\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/training/\"\u003ETraining\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/certifications/\"\u003ECertifications\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://learn.snowflake.com/en/\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides\"\u003EDeveloper Guides\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/data-governance/\"\u003EData Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_1622723482","container","container_copy_copy","container_copy","container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small"},"container_573483281_":{"additionalClasses":"sf-footer__bottom","columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-372f64a6db",":type":"snowflake-site/components/container",":items":{"container_112062425":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-aaee9988db",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-1eea4402f9","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-5590ff288b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer__legal-container","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_16360":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-d39b02847a",":type":"snowflake-site/components/container",":items":{"container":{"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-378db3ed82",":type":"snowflake-site/components/container",":items":{"image":{"id":"image-02370485c6","additionalClasses":"sf-footer__logo","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/_jcr_content/root/container_573483281_/container_112062425/flexible_column_cont/flexible_column_content_container_1/container/container/image.coreimg.svg/1747882370694/nav-icon-snowflake-bug.svg","alt":"Snowflake logo","imageLink":{"valid":true,"url":"/en/"},"lazyEnabled":true,"height":"64","width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"text_copy_copy_16360":{"id":"text-18a27bc6a4","additionalClasses":"sf-footer__legal-links","text":"\u003Cul\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. All Rights Reserved\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/privacy/privacy-policy/\"\u003EPrivacy Policy\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/en/legal/snowflake-site-terms/\"\u003ESite Terms\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://info.snowflake.com/Preference-center.html\"\u003ECommunication Preferences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cbutton id=\"ot-sdk-btn\" class=\"ot-sdk-show-settings\"\u003ECookie Settings\u003C/button\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/privacy/privacy-policy/#12\"\u003EDo Not Share My Personal Information\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/\"\u003ELegal\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"markup_editor":{"id":"markup-editor-3f1ee52e3d","title":" ","htmlContent":"\u003Cdiv class=\"sf-footer__social\"\u003E\r\n\u003Cdiv data-testid=\"snowflake-footer-twitter\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://x.com/Snowflake\" data-testid=\"button-external\" aria-label=\"X (Twitter)\" role=\"button\" class=\"snowflake-button-container\" title=\"X (Twitter)\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"none\" viewBox=\"0 0 59 53\" class=\"button-icon\"\u003E\u003Cpath fill=\"currentColor\" d=\"M46.614 0h9.044L35.8 22.49 59 53H40.795L26.54 34.46 10.223 53H1.18l21.036-24.055L0 0h18.657l12.878 16.937zM43.45 47.72h5.013L16.023 5.085h-5.387z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-linkedin\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.linkedin.com/company/3653845\" data-testid=\"button-external\" aria-label=\"LinkedIn\" role=\"button\" class=\"snowflake-button-container\" title=\"LinkedIn\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M22.223 0H1.772C.792 0 0 .773 0 1.73v20.536C0 23.222.792 24 1.772 24h20.451c.98 0 1.777-.778 1.777-1.73V1.73C24 .773 23.203 0 22.223 0ZM7.12 20.452H3.558V8.995H7.12v11.457ZM5.34 7.434a2.064 2.064 0 1 1 0-4.125 2.063 2.063 0 0 1 0 4.125Zm15.112 13.018h-3.558v-5.57c0-1.326-.024-3.037-1.852-3.037-1.851 0-2.133 1.449-2.133 2.944v5.663H9.356V8.995h3.413v1.566h.047c.473-.9 1.636-1.852 3.365-1.852 3.605 0 4.27 2.372 4.27 5.457v6.286Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-facebook\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.facebook.com/snowflakedb/\" data-testid=\"button-external\" aria-label=\"Facebook\" role=\"button\" class=\"snowflake-button-container\" title=\"Facebook\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M24 12c0-6.627-5.373-12-12-12S0 5.373 0 12c0 5.99 4.388 10.954 10.125 11.854V15.47H7.078V12h3.047V9.356c0-3.007 1.792-4.668 4.533-4.668 1.312 0 2.686.234 2.686.234v2.953H15.83c-1.491 0-1.956.925-1.956 1.875V12h3.328l-.532 3.469h-2.796v8.385C19.612 22.954 24 17.99 24 12Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-youtube\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.youtube.com/user/snowflakecomputing\" data-testid=\"button-external\" aria-label=\"YouTube\" role=\"button\" class=\"snowflake-button-container\" title=\"YouTube\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M23.76 7.2s-.233-1.655-.955-2.381c-.914-.956-1.936-.961-2.405-1.017-3.356-.244-8.395-.244-8.395-.244h-.01s-5.039 0-8.395.244c-.469.056-1.49.06-2.405 1.017C.473 5.545.244 7.2.244 7.2S0 9.145 0 11.086v1.819c0 1.94.24 3.886.24 3.886s.233 1.654.95 2.38c.915.957 2.115.924 2.65 1.027 1.92.183 8.16.24 8.16.24s5.044-.01 8.4-.249c.469-.056 1.49-.06 2.405-1.017.722-.727.956-2.381.956-2.381S24 14.85 24 12.905v-1.819c0-1.94-.24-3.886-.24-3.886ZM9.52 15.113V8.367l6.483 3.385-6.483 3.36Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\r\n\u003C/div\u003E",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container","text_copy_copy_16360","markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none"}},":itemsOrder":["container"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_112062425"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none"},"markup_editor_copy":{"id":"markup-editor-5476d9acbc","title":"New css","cssContent":".snowflake-image-container img{background-color:transparent}div.snowflake-person-chip-avatar{width:80px !important}#snowflake-blog-template-main-container .snowflake-quote-item-card{margin-top:40px}#snowflake-blog-template-main-container .aem-GridColumn:has(.vertical-video){background-color:#000;border-radius:16px;overflow:hidden}#snowflake-blog-template-main-container .is-vertical img{max-width:400px;margin-left:auto;margin-right:auto}#snowflake-blog-template-main-container .vertical-video{max-width:240px;margin-left:auto;margin-right:auto}@media screen and (min-width:1367px){.dynamic .heading-1-v2 .snowflake-title-v2-line{font-size:72px !important;line-height:60px !important}}.snowflake-flexible-column-container-items-alignment-match-height .download-card,.snowflake-flexible-column-container-items-alignment-match-height .download-card\u003E.container{height:100%}.download-card div.code-toolbar\u003E.toolbar .copy-to-clipboard-button{background-color:white;border:1px solid #a9e1f6;margin-right:4px;top:6px;border-radius:16px;height:26px;width:40px}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:before{content:'';background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23249EDC;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23249EDC;'%3E%3C/path%3E%3C/svg%3E\");background-size:auto 65%;background-position:center;background-repeat:no-repeat;top:0;left:0;width:100%;height:100%}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:hover:before{background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23fff;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23fff;'%3E%3C/path%3E%3C/svg%3E\")}.download-card\u003Ediv{background-color:#fff;border:1px solid #ccc;border-radius:8px;padding:24px}.download-chip__headline{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:16px}.download-chip{padding:8px 12px !important;border-radius:4px;transition:300ms ease background-color}.download-chip .black-blue-text-color .snowflake-title-v2-line{color:#000 !important;padding-right:24px;font-family:'Lato',sans-serif;font-size:14px !important;font-weight:500 !important}.download-chip .black-blue-text-color .snowflake-title-v2-line:not(:first-child){opacity:.6;font-style:italic !important}.download-chip .snowflake-content-chip-button{display:none}.download-chip.is-external-link{background-size:16px 16px;background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='15' viewBox='0 0 15 15' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1.06055 13.0607L11.8605 2.26067M13.0605 10.6607V1.06067H3.46055' stroke='%23249EDC' stroke-width='2.12132' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-chip{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_883_7979)'%3E%3Cpath d='M3.375 16.875H14.625' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M9 1.125V11.25' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M4.5 7.875L9 12.375L13.5 7.875' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/g%3E%3Cdefs%3E%3CclipPath id='clip0_883_7979'%3E%3Crect width='18' height='18' fill='white'/%3E%3C/clipPath%3E%3C/defs%3E%3C/svg%3E%0A\");background-size:24px auto;background-repeat:no-repeat;background-position:calc(100% - 12px) center}.download-chip__headline{display:flex;gap:16px;flex-direction:row !important;flex-wrap:nowrap}.download-chip__headline::before{content:'';display:inline-block;width:24px;height:24px;background-position:center;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='21' viewBox='0 0 21 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 9.89999C8.13657 9.89999 8.74702 9.64713 9.19711 9.19704C9.64719 8.74696 9.90005 8.13651 9.90005 7.49999V2.69999C9.90005 2.06347 9.64719 1.45302 9.19711 1.00293C8.74702 .552844 8.13657 .299988 7.50005 .299988H2.70005C2.06353 .299988 1.45308 .552844 1.00299 1.00293C.552905 1.45302 .300049 2.06347 .300049 2.69999V7.49999C.300049 8.13651 .552905 8.74696 1.00299 9.19704C1.45308 9.64713 2.06353 9.89999 2.70005 9.89999H7.50005ZM7.50005 7.49999H2.70005V2.69999H7.50005V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 20.3C8.13657 20.3 8.74702 20.0472 9.19711 19.5971C9.64719 19.147 9.90005 18.5365 9.90005 17.9V13.1C9.90005 12.4635 9.64719 11.853 9.19711 11.403C8.74702 10.9529 8.13657 10.7 7.50005 10.7H2.70005C2.06353 10.7 1.45308 10.9529 1.00299 11.403C.552905 11.853 .300049 12.4635 .300049 13.1V17.9C.300049 18.5365 .552905 19.147 1.00299 19.5971C1.45308 20.0472 2.06353 20.3 2.70005 20.3H7.50005ZM7.50005 17.9H2.70005V13.1H7.50005V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 9.89999C18.5366 9.89999 19.147 9.64713 19.5971 9.19704C20.0472 8.74696 20.3001 8.13651 20.3001 7.49999V2.69999C20.3001 2.06347 20.0472 1.45302 19.5971 1.00293C19.147 .552844 18.5366 .299988 17.9001 .299988H13.1001C12.4636 .299988 11.8531 .552844 11.403 1.00293C10.9529 1.45302 10.7001 2.06347 10.7001 2.69999V7.49999C10.7001 8.13651 10.9529 8.74696 11.403 9.19704C11.8531 9.64713 12.4636 9.89999 13.1001 9.89999H17.9001ZM17.9001 7.49999H13.1001V2.69999H17.9001V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 20.3C18.5366 20.3 19.147 20.0472 19.5971 19.5971C20.0472 19.147 20.3001 18.5365 20.3001 17.9V13.1C20.3001 12.4635 20.0472 11.853 19.5971 11.403C19.147 10.9529 18.5366 10.7 17.9001 10.7H13.1001C12.4636 10.7 11.8531 10.9529 11.403 11.403C10.9529 11.853 10.7001 12.4635 10.7001 13.1V17.9C10.7001 18.5365 10.9529 19.147 11.403 19.5971C11.8531 20.0472 12.4636 20.3 13.1001 20.3H17.9001ZM17.9001 17.9H13.1001V13.1H17.9001V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat}.download-chip__headline.is-cli::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M4 17L10 11L4 5' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M12 19H20' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-card pre[class*=language-]{padding:8px 12px;background-color:var(--ui-background-05);overflow:hidden}.download-chip__headline.is-windows,.download-chip__headline.is-mac{gap:12px}.download-chip__headline.is-windows::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='4875' height='4875' viewBox='0 0 4875 4875' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_122_201)'%3E%3Cpath d='M0 0H2311V2310H0V0ZM2564 0H4875V2310H2564V0ZM0 2564H2311V4875H0V2564ZM2564 2564H4875V4875H2564' fill='%23000'/%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-mac::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg version='1.1' id='Layer_1' xmlns:x='ns_extend;' xmlns:i='ns_ai;' xmlns:graph='ns_graphs;' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' x='0' y='0' viewBox='0 0 41.5 51' style='enable-background:new 0 0 41.5 51;' xml:space='preserve'%3E%3Cmetadata%3E%3Csfw xmlns='ns_sfw;'%3E%3Cslices%3E%3C/slices%3E%3CsliceSourceBounds bottomLeftOrigin='true' height='51' width='41.5' x='166.1' y='-208.1'%3E%3C/sliceSourceBounds%3E%3C/sfw%3E%3C/metadata%3E%3Cg%3E%3Cpath d='M40.2,17.4c-3.4,2.1-5.5,5.7-5.5,9.7c0,4.5,2.7,8.6,6.8,10.3c-.8,2.6-2,5-3.5,7.2c-2.2,3.1-4.5,6.3-7.9,6.3s-4.4-2-8.4-2 c-3.9,0-5.3,2.1-8.5,2.1s-5.4-2.9-7.9-6.5C2,39.5,.1,33.7,0,27.6c0-9.9,6.4-15.2,12.8-15.2c3.4,0,6.2,2.2,8.3,2.2 c2,0,5.2-2.3,9-2.3C34.1,12.2,37.9,14.1,40.2,17.4z M28.3,8.1C30,6.1,30.9,3.6,31,1c0-.3,0-.7-.1-1c-2.9,.3-5.6,1.7-7.5,3.9 c-1.7,1.9-2.7,4.3-2.8,6.9c0,.3,0,.6,.1,.9c.2,0,.5,.1,.7,.1C24.1,11.6,26.6,10.2,28.3,8.1z'%3E%3C/path%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-desktop::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg opacity='.8'%3E%3Cpath d='M1.5 21H22.5V18H1.5V21Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M19.5 15C20.2956 15 21.0587 14.6839 21.6213 14.1213C22.1839 13.5587 22.5 12.7956 22.5 12V6C22.5 5.20435 22.1839 4.44129 21.6213 3.87868C21.0587 3.31607 20.2956 3 19.5 3H4.5C3.70435 3 2.94129 3.31607 2.37868 3.87868C1.81607 4.44129 1.5 5.20435 1.5 6V12C1.5 12.7956 1.81607 13.5587 2.37868 14.1213C2.94129 14.6839 3.70435 15 4.5 15H19.5ZM19.5 12H4.5V6H19.5V12Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3C/g%3E%3C/svg%3E%0A\")}.download-card .snowflake-code-snippet,.download-card .snowflake-code-snippet code,.download-card .snowflake-code-snippet pre{font-size:14px;color:#000;text-shadow:none !important}.download-chip:hover{background-color:var(--ui-background-05) !important;transition:300ms ease background-color}body:has(.snowflake-skip-to-content[style]) #subNav,.pushdown-banner-dismissed #subNav{top:var(--scroll-padding-top) !important;transition:300ms ease top}body:has(.snowflake-skip-to-content[style*=\"58\"]) #subNav{top:34px !important}body:has(.snowflake-skip-to-content[style*=\"82\"]) #subNav{top:58px !important}body:has(.snowflake-skip-to-content[style*=\"130\"]) #subNav{top:106px !important}body:has(.snowflake-skip-to-content[style*=\"138\"]) #subNav{top:114px !important}body:has(.snowflake-skip-to-content[style*=\"146\"]) #subNav{top:122px !important}.is-hidden .snowflake-person-chip-avatar{display:none}.is-small .snowflake-person-chip-avatar{width:56px;height:56px}.ai-summary ul{margin:16px 0 0 0 !important;padding:0 !important;list-style-type:none}.ai-summary li{margin:0;padding:0 0 0 32px;position:relative}.ai-summary li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.ai-summary li:not(:last-child){margin-bottom:1rem}.snowflake-content-chip-image__image{aspect-ratio:5 / 3 !important}.content-chip-new .snowflake-content-chip-image__image{height:100% !important;aspect-ratio:unset !important}.snapshot-card .snowflake-text p:not(:first-child){margin-top:var(--spacing-01)}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p:has(b){font-family:'Texta',sans-serif;margin-top:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p b{font-weight:700 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){border-bottom:1px solid #ccc;padding-bottom:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) p:first-child:has(b){font-family:'Texta',sans-serif;font-size:20px !important;margin-bottom:1rem !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li{display:inline-block}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li a{display:inline-block;text-decoration:none;padding:4px 16px !important;border:1px solid #ccc;border-radius:24px;color:#666 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) ul{list-style-type:none;display:flex;padding:0 !important;margin:0 !important;gap:12px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container img{width:90%;max-width:240px;margin:0 auto}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container{padding:40px;max-width:450px;margin:0 0 0 auto;background-color:#fff;box-shadow:0 2px 6px 0 rgba(152,162,179,.25),0 10px 20px 0 rgba(152,162,179,.10);border-radius:8px;border-top:4px solid var(--ui-01)}.ai-summary{background-color:#f3fbfe;border-left:2px solid var(--ui-01);padding:40px}.ai-summary\u003Espan p:last-child:has(i){color:#666;font-size:14px !important}.ai-summary\u003Espan p:last-child:has(i) a{color:#666 !important;text-decoration:underline !important}.ai-summary\u003Espan p:last-child:has(i) a:hover{color:var(--ui-01) !Important}.ai-summary\u003Espan p:first-child:has(b)::after{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9.3158 3.15226C8.6475 6.2258 6.22698 8.64545 3.15232 9.31587C2.94923 9.36072 2.94923 9.63928 3.15232 9.68413C6.22698 10.3522 8.6475 12.7742 9.3158 15.8477C9.36067 16.0508 9.63933 16.0508 9.6842 15.8477C10.3525 12.7742 12.773 10.3545 15.8477 9.68413C16.0508 9.63928 16.0508 9.36072 15.8477 9.31587C12.773 8.64781 10.3525 6.2258 9.6842 3.15226C9.63933 2.94925 9.36067 2.94925 9.3158 3.15226Z' fill='%23249EDC'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.3725 11.5461C16.9098 13.6739 15.2341 15.3491 13.1054 15.8132C12.9649 15.8443 12.9649 16.0371 13.1054 16.0681C15.2341 16.5307 16.9098 18.2074 17.3725 20.3353C17.4035 20.4758 17.5965 20.4758 17.6275 20.3353C18.0902 18.2074 19.7659 16.5323 21.8946 16.0681C22.0352 16.0371 22.0352 15.8443 21.8946 15.8132C19.7659 15.3507 18.0902 13.6739 17.6275 11.5461C17.5965 11.4055 17.4035 11.4055 17.3725 11.5461Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-repeat:no-repeat;background-size:contain;background-position:center;vertical-align:middle;margin-left:8px}.ai-summary\u003Espan p:first-child:has(b){color:var(--ui-01) !important;text-transform:uppercase}.border-top{border-top:1px solid rgba(0,0,0,.2)}.border-top\u003Espan{display:block;padding-top:32px}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9 !important}.content-chip-new .snowflake-content-chip-image__image{border-radius:0;object-fit:cover;height:100%}.sf-footer #ot-sdk-btn.ot-sdk-show-settings,.sf-footer #ot-sdk-btn.optanon-show-settings{color:rgba(255,255,255,.7) !important;text-underline-offset:4px;border-top:none;border-left:none;border-right:none;border-bottom:1px dotted transparent;background-color:transparent !important;background-image:none !important;transition:300ms ease text-decoration-color;padding:0 !important;font-size:12px;font-family:'Lato',sans-serif}.sf-footer #ot-sdk-btn.ot-sdk-show-settings:hover,.sf-footer #ot-sdk-btn.optanon-show-settings:hover{color:rgba(255,255,255,1) !important;border-bottom:1px dotted var(--ui-01);transition:300ms ease text-decoration-color}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{flex-shrink:0}.sf-footer__disclaimers{background-color:#042130}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p a{color:inherit;text-decoration:none !important}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p sup{margin-right:2px}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p{text-indent:-5px;padding-left:5px}.sf-footer__disclaimers-inner{border-top:1px solid rgba(255,255,255,.25);padding:40px 0}.sf-footer__disclaimers .snowflake-simple-stat{align-items:flex-start;text-align:left;color:rgba(255,255,255,.7);margin-bottom:10px}.sf-footer__social{display:flex;justify-content:center;gap:12px}.sf-footer .snowflake-footer-social-item{margin:0 !important}.sf-footer .snowflake-footer-social-item a{line-height:0;background-color:rgba(3,24,35,.8);display:inline-block;width:48px !important;height:48px;border-radius:8px;display:inline-flex;justify-content:center;align-items:center;transition:300ms ease background-color}.sf-footer .snowflake-footer-social-item a:hover{background-color:var(--ui-01) !important;transition:300ms ease background-color}.sf-footer__bottom{padding-bottom:40px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoError .mktoErrorMsg{max-width:100%;color:#fff}.sf-footer .mktoForm .mktoError .mktoErrorMsg .mktoErrorDetail{display:inline-block}.sf-footer .mktoFormRow:has(.mktoHtmlText:empty){display:none}.sf-footer .mktoFormRow .mktoHtmlText span{color:#fff !important}.sf-footer{background-color:#042130}.sf-footer .optanon-toggle-display:hover{text-decoration-color:var(--ui-01) !important;cursor:pointer !important;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:var(--ui-01);color:#fff !important;transition:300ms ease text-decoration-color;text-decoration:underline;opacity:1}.sf-footer__logo{width:40px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{row-gap:32px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;align-items:center;text-align:center;row-gap:16px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){text-align:center;flex-grow:1}.sf-footer__legal-links li button,.sf-footer__legal-links li a,.sf-footer__legal-links li{margin:0;color:rgba(255,255,255,.7) !important;font-weight:500}.sf-footer__legal-links li a:hover{color:rgba(255,255,255,1) !important}.sf-footer div.sf-footer__copyright p,.sf-footer div.sf-footer__legal-links li,.sf-footer div.sf-footer__legal-links a,.sf-footer div.sf-footer__legal-links p{font-size:12px !important}.sf-footer__legal-links ul{list-style-type:none;margin:0;padding:0;display:flex;gap:20px;row-gap:4px;justify-content:center;flex-wrap:wrap;text-align:center}.sf-footer__legal-links li:last-child{width:100%}.sf-footer .mktoFormRow:has(.mktoPlaceholder),.sf-footer .mktoFormRow:has(input[type=\"hidden\"]){display:none !important}.sf-footer .mktoFormCol{margin-bottom:0 !important}.sf-footer label[for=\"adhoc1\"]{width:auto !important;flex-grow:1;margin-left:16px}.sf-footer .mktoFieldWrap:has(label[for=\"adhoc1\"]){display:flex;flex-direction:row-reverse;margin-top:22px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoCheckboxList input[type=checkbox]{background-color:transparent !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-color:transparent !important;color:#fff !important;height:auto !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important;padding:12px 18px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select:focus{border-color:var(--ui-01) !important}.sf-footer .mktoForm *{padding:0 !important}.sf-footer .mktoForm,.sf-footer .snowflake-marketo-form-container{padding:0 !important;background:transparent;margin-bottom:0;box-shadow:none}.sf-footer .mktoHtmlText.mktoHasWidth{width:100% !important;margin:24px 0}.sf-footer .mktoFormRow{flex-direction:column}.sf-footer .mktoForm .mktoButtonWrap{margin:0 !important}.sf-footer select{background-image:url(\"data:image/svg+xml,%3Csvg width='14' height='8' viewBox='0 0 14 8' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M.981445 1.43496L6.90897 7.32496L12.9314 1.33496' stroke='white' stroke-width='1.33333' stroke-miterlimit='10' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\") !important}.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative{justify-content:flex-start}.sf-footer *::placeholder{color:#fff !important;opacity:.8}.sf-footer .mktoForm .mktoButtonWrap.mktoSimple .mktoButton{background-color:var(--ui-01) !important;color:#fff !important;width:100% !important;padding:12px 16px !important;border:1px solid var(--ui-01) !important;background-image:none !important;border-radius:48px;text-transform:uppercase;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:16px !important;line-height:1.2}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoHtmlText\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoLabel\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap label.mktoLabel{color:#fff !important}.sf-footer__newsletter-title p:not(:first-child){margin-top:8px !important}.sf-footer__newsletter-title p b{font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:22px !important;line-height:1.2}.sf-footer__newsletter-title p:last-child{font-size:14px !important;opacity:.8}.sf-footer__link-group li a[target=\"_blank\"]::after{content:'';display:inline-block;width:10px;height:10px;margin-left:5px;background-image:url(\"data:image/svg+xml,%3Csvg width='11' height='11' viewBox='0 0 11 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.72222 1.22222C6.38471 1.22222 6.11111 .948616 6.11111 .611111C6.11111 .273607 6.38471 0 6.72222 0H10.3889C10.551 0 10.7064 .0643867 10.821 .178988C10.9356 .293596 11 .449032 11 .611111V4.27778C11 4.61529 10.7264 4.88889 10.3889 4.88889C10.0514 4.88889 9.77778 4.61529 9.77778 4.27778V2.08647L4.09879 7.76545C3.86013 8.00409 3.4732 8.00409 3.23454 7.76545C2.99589 7.52681 2.99589 7.13986 3.23454 6.90122L8.91355 1.22222H6.72222ZM0 2.44444C0 1.76943 .547207 1.22222 1.22222 1.22222H4.27778C4.61529 1.22222 4.88889 1.49583 4.88889 1.83333C4.88889 2.17084 4.61529 2.44444 4.27778 2.44444H1.22222V9.77778H8.55556V6.72222C8.55556 6.38471 8.82915 6.11111 9.16667 6.11111C9.50418 6.11111 9.77778 6.38471 9.77778 6.72222V9.77778C9.77778 10.4528 9.23059 11 8.55556 11H1.22222C.547207 11 0 10.4528 0 9.77778V2.44444Z' fill='white'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-position:center}.sf-footer__link-group ul,.sf-footer__link-group li{margin:0;padding:0;list-style-type:none}.sf-footer__link-group ul{margin-top:20px !important}.sf-footer__link-group li{margin-top:15px}.sf-footer div.sf-footer__link-group\u003Espan\u003Ep\u003Ea,.sf-footer div.sf-footer__link-group\u003Espan\u003Ep{color:var(--ui-01) !important;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:20px !important;line-height:1.2}.sf-footer__link-group li a{opacity:.9;color:#fff !important;font-weight:500 !important;font-size:15px !important;line-height:1.3}.sf-footer__link-group li a:hover{opacity:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::before,.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::after{display:none}.sf-footer__column{flex-grow:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child){width:50%}@media (min-width:800px){.sf-footer__legal-links ul{justify-content:flex-start;text-align:left}.sf-footer__social{justify-content:flex-end}.sf-footer__legal-links ul{padding-left:24px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{text-align:right;flex-wrap:nowrap}.sf-footer__legal-links.align-left ul{justify-content:flex-start}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;flex-direction:row}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto !important;max-width:200px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;order:2;width:100% !important;max-width:none}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto}}@media screen and (min-width:1380px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{flex-wrap:nowrap}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{padding-right:48px;max-width:380px;background-color:rgba(3,24,35,.4);padding:32px;margin-left:48px;border-radius:16px}.sf-footer__link-group li,.sf-footer__link-group li a{font-size:14px !important;line-height:1.3}}@media screen and (max-width:991px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{order:2;margin-top:24px !important}}@media screen and (max-width:420px){.is-reduced-mobile .heading-1-v2,.is-reduced-mobile .heading-1-v2-sm{font-size:32px;line-height:28px}}.quote-content-chip{background-color:var(--ui-background-05);padding:24px;border-radius:12px;position:relative}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan{color:rgba(0,0,0,.8) !important;font-size:15px !important;line-height:1.5 !important;font-family:'Lato',sans-serif;font-weight:400 !important}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:not(:first-child){max-width:calc(100% - 200px)}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:nth-child(2){font-family:'Texta',sans-serif;color:#000 !important;font-size:20px !important;font-weight:800 !important;margin-top:24px}.quote-content-chip .snowflake-content-chip-image{width:140px !important}@media screen and (min-width:992px){.quote-content-chip .snowflake-content-chip-image{position:absolute !important;bottom:24px;right:16px}}@media screen and (max-width:991px){.quote-content-chip .snowflake-content-chip-image{margin-bottom:40px}.quote-content-chip{flex-direction:column}}#spa-root{background-color:#fff}.lowercase .snowflake-title-v2-line{text-transform:none !important}.centered .snowflake-logo-content-container-inner{justify-content:center}div.snowflake-linklist-dropdown-menu{max-height:380px}.first-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:first-child{color:var(--ui-01) !important}.is-front{position:relative;z-index:2}.use-case-body .snowflake-text h1,.use-case-body .snowflake-text h2,.use-case-body .snowflake-text h3,.use-case-body .snowflake-text h4,.use-case-body .snowflake-text h5,.use-case-body .snowflake-text h6{font-family:'Texta',sans-serif;color:#000;margin:.25rem 0 0 0}.pc-hero .button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:flex-start}.sf-footer .mktoFormRow .mktoHtmlText span{font-family:'Lato',sans-serif !important}.snowflake-button-primary.snowflake-button-blue .snowflake-button-container{justify-content:center}.related-chip-25{background-color:#fff;border:1px solid rgba(204,204,204,.5);border-radius:8px;padding:20px;position:relative}.related-chip-25:hover{box-shadow:rgba(152,162,179,.1) 0 10px 20px 0}.related-chip-25:hover::after{right:24px;transition:300ms ease right}.related-chip-25::after{content:'';display:block;transition:300ms ease right;background-image:url(\"data:image/svg+xml,%3Csvg width='8' height='14' viewBox='0 0 8 14' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.66699 7C7.66699 6.6571 7.53559 6.32825 7.30169 6.08578L2.34446 .947072C1.84529 .429617 1.0164 .429617 .517219 .947072C.0427878 1.43887 .042788 2.21798 .517219 2.70978L4.65591 7L.51722 11.2902C.0427889 11.782 .0427887 12.5611 .51722 13.0529C1.0164 13.5704 1.84529 13.5704 2.34447 13.0529L7.30169 7.91421C7.53559 7.67175 7.66699 7.34289 7.66699 7Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\");width:8px;height:14px;display:block;position:absolute;right:30px;top:50%;transform:translateY(-50%);background-size:contain;background-position:center;background-repeat:no-repeat}.related-chip-25 .heading-5-v2{font-size:22px;line-height:1.1}.related-chip-25 .snowflake-content-chip-image{width:48px;flex-shrink:0}.related-chip-25 .snowflake-content-chip-image__image{aspect-ratio:1;height:auto;object-fit:contain}.related-chip-25 .snowflake-content-chip-button{display:none}.related-chip-25 .snowflake-content-chip-content-without-tag{flex-grow:1;padding-right:24px}.case-study-25.small-logo .snowflake-case-study-card-logo img{width:60px !important}.swiper-slide .case-study-25{width:95%;margin-left:auto;margin-right:auto}.case-study-25 .snowflake-case-study-card-logo img{width:140px !important;height:auto !important;transform:none !important;margin:24px 0 8px 0}.case-study-25 .snowflake-case-study-card-image__image{object-position:left center}.case-study-25 .snowflake-case-study-card-information-container{padding-right:24px}.case-study-25 ul{list-style-type:none;padding:0;margin:8px 0 0 0}.case-study-25 li{font-size:15px !important;line-height:1.3 !important;display:flex;flex-direction:column;border-left:4px solid var(--ui-01);padding-left:24px;margin-top:24px;color:#535862;gap:4px}.case-study-25 li b{display:block;font-family:'Texta',sans-serif;font-weight:900 !important;font-size:48px !important;line-height:.9 !important;color:var(--ui-01)}.case-study-25 .snowflake-case-study-card-description p{color:#535862}.case-study-25 .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){color:#000;font-family:Texta;font-size:30px !important;line-height:1 !important;font-style:normal;font-weight:700;text-indent:-8px}.case-study-25.is-story .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){text-indent:0}.case-study-25 .snowflake-case-study-card-key-card{background-color:transparent}.case-study-25 .snowflake-case-study-card-button{display:none}.case-study-25{border-radius:24px;overflow:hidden}@media screen and (min-width:1024px){.case-study-25 .snowflake-case-study-card-left-container{position:static;width:60%;min-height:0}.case-study-25 .snowflake-case-study-card-right-container::after{content:'';display:block;width:60%;max-width:340px;padding-bottom:50%;background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 22 16' class='snowflake-pushdown-banner-placeholder-arrow'%3E%3Cpath fill='%2329B5E8' fill-rule='evenodd' d='M17.865 8.756c.088-.274.124-.555.118-.834a2.551 2.551 0 0 0-1.3-2.142L7.887.76C6.645.055 5.063.475 4.35 1.7a2.535 2.535 0 0 0 .947 3.494l4.916 2.809-4.916 2.801a2.543 2.543 0 0 0-.947 3.502c.713 1.222 2.295 1.64 3.537.934l8.796-5.024a2.541 2.541 0 0 0 1.182-1.46Z' clip-rule='evenodd'%3E%3C/path%3E%3C/svg%3E\");background-size:contain;background-repeat:no-repeat;position:absolute;top:-10%;left:-20%}.case-study-25 .snowflake-case-study-card-right-container{max-width:none;width:40%;position:absolute;top:-5%;right:-5%;z-index:0;height:110%}}@media screen and (min-width:768px){.case-study-25 li{max-width:50%}.case-study-25 ul{display:flex;gap:48px}}.snowflake-text.section-eyebrow p{margin-left:auto;margin-right:auto;margin-bottom:16px !important}.snowflake-text.section-eyebrow p,.snowflake-text.eyebrow-text p{text-transform:uppercase;font-family:'Texta',sans-serif !important;font-weight:800 !important;letter-spacing:.025em;margin-bottom:12px;line-height:1.1 !important}.snowflake-title-v2.dynamic .heading-2-v2 span.snowflake-title-v2-line{font-size:clamp(2.5rem,4.5vw,4rem) !important;line-height:.82 !important}.checklist ul{padding:0;margin:0}.checklist ul li{list-style-type:none;padding-left:32px;position:relative}.checklist ul li:not(:last-child){margin-bottom:1em}.checklist ul li::before{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='25' viewBox='0 0 24 25' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Crect y='.985352' width='24' height='24' rx='12' fill='%23D4F0FA'/%3E%3Cpath d='M7.28613 13.2967L10.7147 16.7253L17.5718 9.86816' stroke='%2329B5E8' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;position:absolute;top:3px;left:0}.last-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:last-child{color:var(--ui-01)}.snowflake-text p sup{line-height:0}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px;line-height:1;text-transform:none;font-weight:700}.snowflake-title-v2.lowercase .heading-2-v2{font-size:32px;line-height:1;text-transform:none;font-weight:700}.content-chip-new{border:1px solid rgba(204,204,204,.5);border-radius:16px;overflow:hidden}.content-chip-new .snowflake-image-container{border-radius:0;display:none}.content-chip-new .snowflake-content-chip-image{margin-right:0;max-width:180px;flex-shrink:0}.content-chip-new .snowflake-content-chip-content{padding:24px}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:first-child{font-size:24px;line-height:1.1}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){font-family:'Lato',sans-serif;font-size:17px;color:#535862 !important;font-weight:500;line-height:1.45;margin-top:8px;display:none}div.snowflake-text a{font-weight:normal;color:var(--ui-01);text-decoration:underline;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:transparent;transition:300ms ease text-decoration-color}div.snowflake-text a:hover{text-decoration-color:var(--ui-01);transition:300ms ease text-decoration-color}.footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container .snowflake-button-primary+.snowflake-button-link{margin-left:12px !important}.snowflake-button-regular.snowflake-button-link .snowflake-button-container{font-size:18px !important;text-align:left;justify-content:flex-start;line-height:1.4 !important}body .snowflake-card-v2-advanced{border:1px solid rgba(204,204,204,.5);border-radius:var(--spacing-02);transition:300ms ease all}body .snowflake-card-v2-advanced:hover{transform:translateY(-10px);box-shadow:rgba(152,162,179,.1) 0 10px 20px 0;transition:300ms ease all}body .snowflake-card-v2-advanced-inner{border-bottom:none}body .snowflake-card-v2-advanced-image{line-height:0}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9}body .snowflake-card-v2-advanced-content{position:relative}body .snowflake-card-v2-advanced-content::after{content:'';display:block;position:absolute;bottom:0;left:0;transition:300ms ease all;width:20%;height:4px;background-color:var(--ui-01);opacity:0}body .snowflake-card-v2-advanced:hover .snowflake-card-v2-advanced-content::after{width:100%;opacity:1;transition:300ms ease all}body .snowflake-card-v2-advanced .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transition:300ms ease transform}body .snowflake-card-v2-advanced:hover .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transform:translateX(4px);transition:300ms ease transform}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-wrap:wrap;gap:24px}.six-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:100%;margin:0 !important}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px);margin:0 !important}@media screen and (min-width:768px){.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}}@media screen and (min-width:1024px){.snowflake-title-v2.lowercase .heading-3-v2{font-size:34px}.snowflake-title-v2.lowercase.larger .heading-2-v2{font-size:44px;line-height:.95}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(25% - 18px)}.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(20% - 19.2px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(16.6666% - 20px)}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px !important}}@media screen and (min-width:1200px){.snowflake-title-v2.lowercase .heading-2-v2{font-size:40px}.content-chip-new .snowflake-content-chip-content{padding:32px}.content-chip-new .snowflake-image-container,.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){display:block}}.promo-banner-25{border-radius:16px;overflow:hidden}.promo-banner-25 .snowflake-premium-content-banner-image-container{position:relative;max-width:380px}.promo-banner-25 .snowflake-text{color:#535862}.promo-banner-25 .snowflake-premium-content-banner-image__image{transform:translateY(8px);transition:300ms ease transform;border-radius:0;width:85%;margin:0 auto;display:block;position:relative;z-index:1}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover .snowflake-premium-content-banner-image__image{transform:translateY(0);transition:300ms ease transform}.promo-banner-25 .snowflake-premium-content-banner-image__inner{height:auto;padding-top:24px}.promo-banner-25 .snowflake-premium-content-banner-image__link{position:relative;z-index:1;height:auto}.promo-banner-25 .snowflake-premium-content-banner-image__link::after{content:'';display:block;position:absolute;clip-path:polygon(0 0,66% 0,100% 100%,0 100%);bottom:0;left:0;width:100%;height:100%;background:var(--ui-01);transition:300ms ease width}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover::after{width:110%;transition:300ms ease width}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-position:95% 50%}.sf-footer__disclaimers .text-size-small .snowflake-text p{color:#fff !important;font-size:10px !important;opacity:.8}@media screen and (min-width:768px){.sf-footer__disclaimers .text-size-small .snowflake-text p{font-size:12px !important}}@media screen and (max-width:1023px){.mobile-top-padding{padding-top:64px}}@media (max-width:799px){.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative .mktoButton{width:100% !important}.sf-footer__logo{text-align:center;display:block;margin:0 auto}}.customer-card .snowflake-card-v2-advanced-image{aspect-ratio:4.35 / 1}.customer-card .snowflake-card-v2-advanced-image__image{width:100%;height:100%;padding-left:8px;object-fit:contain;object-position:left center;margin:0 !important;aspect-ratio:initial}.customer-card .snowflake-card-v2-advanced-image__inner{height:110px}.customer-card .snowflake-card-v2-advanced-tag-indicator{display:none}.pc-hero .snowflake-container-arrow-small-gray-image{top:-34% !important;width:18% !important}.pc-hero .snowflake-container-arrow-small-gray-image path{fill:var(--ui-01);opacity:1}@media screen and (max-width:767px){.mobile-padding-top{padding-top:64px}.hide-mobile{display:none !important}.pc-hero{padding-top:52px}.pc-hero .snowflake-text p,.pc-hero .left-alignment .snowflake-title-v2-line,.pc-hero h1 span{text-align:center !important}}div.snowflake-pushdown-banner-button{margin-top:0}.button-group.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:center !important}.text-center .snowflake-breadcrumb-swiper .swiper-wrapper{justify-content:center}div.snowflake-breadcrumb a.snowflake-breadcrumb-item,.snowflake-breadcrumb div.snowflake-breadcrumb-item{text-transform:none;font-weight:500}.snowflake-breadcrumb svg{display:none !important}.snowflake-breadcrumb a:has(svg)::after{content:'/';margin:0 12px;color:#666}.hide-filters .snowflake-filterable-and-searchable-grid-top-part{display:none !important}.page-section{padding-left:24px;padding-right:24px}@media screen and (min-width:768px){.page-section{padding-left:48px;padding-right:48px}}.download-card pre[class*=language-]{overflow-x:scroll !important}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container_copy","container_573483281_","markup_editor_copy"]}},":itemsOrder":["root"],"classNames":"aem-xf"},"markup_editor":{"id":"markup-editor-235df717f1","title":"Quickstarts Overrides","cssContent":".snowflake-markdown blockquote{padding:24px 32px;background:#f6f9fa;border:1px solid #29b5e8;border-radius:16px}.snowflake-markdown .snowflake-image-container img{width:auto !important;max-width:100%}.snowflake-markdown .snowflake-text ol{padding-left:20px !important}.snowflake-markdown .snowflake-text li{margin:0 0 12px 0 !important}.snowflake-markdown h3.snowflake-markdown-h3{font-size:20px !important;font-family:Texta,sans-serif !important}@media (min-width:768px){.snowflake-markdown h3.snowflake-markdown-h3{font-size:28px !important}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","markup_editor_1950346551","responsivegrid","modal_container","experiencefragment-footer","markup_editor"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],"analyticsDebugMode":false,":hierarchyType":"page",":path":"/content/snowflake-site/global/en/developers/guides/dcdf-incremental-processing","locale":"en",":mappedPath":"/en/developers/guides/dcdf-incremental-processing/"}