{"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"cssClassNames":"page basicpage summit-page","designPath":"/libs/settings/wcm/designs/default","brandSlug":"","componentsResourceTypes":["snowflake-site/components/quickstart/quickstart-button","snowflake-site/components/nav/nav-column/nav-column-container","snowflake-site/components/structure/page","snowflake-site/components/button","snowflake-site/components/quickstart/quickstart-hero","snowflake-site/components/quickstart/quickstart-table-of-content","snowflake-site/components/experiencefragment","snowflake-site/components/mega-header","snowflake-site/components/modal/modal-container","snowflake-site/components/image","snowflake-site/components/nav/nav-dropdown-header","snowflake-site/components/wistia-video/cta","nt:folder","snowflake-site/components/container","snowflake-site/components/nav/nav-dropdown-menu","snowflake-site/components/nav/nav-column","snowflake-site/components/flexible-column-container","snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container","snowflake-site/components/button/embedded","snowflake-site/components/icon","snowflake-site/components/nav/nav-mega","cq:LiveCopy","snowflake-site/components/quickstart/quickstart-last-modified","snowflake-site/components/nav/nav-promo-section","snowflake-site/components/markup-editor","snowflake-site/components/text","snowflake-site/components/title-v2","snowflake-site/components/nav/nav-dropdown-footer","nt:unstructured","nt:file","snowflake-site/components/contentfragment","snowflake-site/components/nav/nav-item","snowflake-site/components/nav/nav-promo-card","snowflake-site/components/form/marketo-v2","snowflake-site/components/nav/language-navigation","snowflake-site/components/title","wcm/foundation/components/responsivegrid","nt:resource","snowflake-site/components/structure/xfpage","snowflake-site/components/flexible-column-container/flexible-column-content-container","snowflake-site/components/pushdown-banner"],"clientlibsAsync":false,"dataLayerClientlibIncluded":true,"dataLayerName":"adobeDataLayer","lastModifiedDate":1761002005097,"language":"en","title":"Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler","tags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/solution-center/includes/architecture","snowflake-site:taxonomy/product/ai","snowflake-site:taxonomy/solution-center/certification/partner-solution","snowflake-site:taxonomy/exclude-tags/hidden"],"templateName":"quickstart-page-template","analyticsPageType":"quickstart-page-template","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":hierarchyType":"page",":path":"/content/snowflake-site/global/en/developers/guides/vhol-snowflake-data-wrangler","analyticsEnabled":true,"coveoConfig":{"searchHub":"snowflake.com","organizationId":"snowflakecomputingproduction8neljofn","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","pipeline":"snowflake.com"},"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"","pageType":"quickstart-page-template","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/content/snowflake-site/global/en/developers/guides/vhol-snowflake-data-wrangler","language":"en","category":"general","pageName":"Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler","contentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/solution-center/includes/architecture","snowflake-site:taxonomy/product/ai","snowflake-site:taxonomy/solution-center/certification/partner-solution","snowflake-site:taxonomy/exclude-tags/hidden"]},"isPasswordProtected":false,"analyticsContentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/solution-center/includes/architecture","snowflake-site:taxonomy/product/ai","snowflake-site:taxonomy/solution-center/certification/partner-solution","snowflake-site:taxonomy/exclude-tags/hidden"],":type":"snowflake-site/components/structure/page",":mappedPath":"/en/developers/guides/vhol-snowflake-data-wrangler/",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"columnCount":12,":items":{"experiencefragment-banner":{"id":"experiencefragment-e8817debcd","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"pushdown_banner_copy":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-21c8ab1dda",":type":"snowflake-site/components/container",":items":{"pushdown_banner_copy":{"id":"pushdown-banner-90540c415e","contentHeadline":"Summit Builder Keynote Debut","contentDescription":"Broadcast live on June 23","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://reg.snowflake.com/flow/snowflake/summit26/digitalreg/page/main"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":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-5c9097bb67","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-76c3a246d5",":type":"snowflake-site/components/container",":items":{"markup_editor":{"id":"markup-editor-8772bb7ea8","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}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"mega_header":{"additionalClasses":"heap-nav-header","layout":"SIMPLE","id":"container-db76f48acd",":type":"snowflake-site/components/mega-header",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-66cea87aa4",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-31aa3dc914","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-50530f4a01",":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-2755a65d08",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-0fd9857b1e","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-5b9bfb6c25","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-19d800054a","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-7d04307285","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-3db130260c","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-f5dfa090a0","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-f366408bbd","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-9462132727",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_212715":{"id":"nav-item-f5e4b6e8c2","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-042b2fff38","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-67e8619a13","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-34e8eb7bc8","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-32b2861842","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-c3d7de0b17",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_660590_1739526127":{"id":"nav-item-8ada0dab19","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-eba931a263","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-ba9c491f5a","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-d3161b1005","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-9aa5c18b70","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-fe8b089d66",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-046ac1bba0","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-f472128aca","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-9e9a2d574e","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-4419391e34","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-0832379db9","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-802bf6c885","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-7f109f3a87",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"navColumnTitle":"INDUSTRIES","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-9cfd3d809a",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_361384_2056203141":{"id":"nav-item-168775a80b","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-cff331f472","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-f11f4ee92d","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-1503ac1e5a","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-e3a6f09331","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-41db5800f2","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-eb49ec4e2c","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-da24945f61","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-3c2782ba92","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-936e2d6f6e","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-16b12ee9fd",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-77e30f3169","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-3a22de32ae","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-846ff1c852","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-762a2e8c81",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_107772":{"id":"nav-item-e1439b065e","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-06748d6d40","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,":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-2d82b50947",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-0f335b4336","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-eaa092adcb","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-1ff176559e","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,":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-2d666d9e33","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-c90ed87e10",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-7aa83e5066",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-e2f4113b63","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-b54722b05f",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-87977e08e7","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-ff7d6caf56","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-dbf39a7f3c","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-fae6b990b6","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_903555964":{"id":"nav-item-7c3d814016","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,":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-c1037f9749","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-e44ae5d425",":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-12d4ea7086",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-532727b7e9","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-60419d96f1","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-b6d7002a57","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-5b8d4635f4","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-7b58af12a9",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-a6beecfb1f","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-ce870fed4e","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-7350a5a97a","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-14afe441f2","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-5e54a7a63a","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-8bc013d8ca","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-c7a3e43fd4","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890":{"id":"nav-item-0ed1ac6b48","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890_930852828":{"id":"nav-item-e6a78b9bed","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,":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-72a8a9d482","experience_fragment_1":{"id":"experiencefragment-9b847459c5","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-74a1bc7e12",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-1f098b1147","openInNewWindow":true,"layout":"horizontal","headline":"Dev Day Virtual - June 25","description":"Don’t just hear about AI — build it. Luminary talks and hands-on labs","linkTitle":"Learn more","linkUrl":"/en/dev-day/americas-virtual/","image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--de231e36-6645-4550-abd9-0f8de758ac66/web-dev-day-26-960x540-1x.png?quality=85&preferwebp=true","alt":"dev day","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-886cfc8ef6","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-20f123600d",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-739b5881f3","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-5a72a08e03","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-9a118b786b",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-844251dd9d","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-12487c996a","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-ea52d45ad4",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy_copy":{"navColumnTitle":"Build","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-6975193609",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-0e9d66e8ea","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-02c5154a47","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-500a8da321","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,":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-45599b75c1",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-67ba45666f","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-e605429959","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-8596eee1b2","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,":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-2d30af76e0",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-7fc1604ee6","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,":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-b03c7b3dc6","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,":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-defe6696a7","experience_fragment_1":{"id":"experiencefragment-7430bdd807","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-3f0e7896d0",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-7f34dc3c2b","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-b3bfcb10dc","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-93f7a726a5",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-6876cae9ef","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-124786b3a7","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-f246d0a70a","languageNavItems":[{"title":"English","path":"/en/developers/guides/vhol-snowflake-data-wrangler/","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-d781e7c0f6","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-467a355b16","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-01bb255ece","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-5307616644","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}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-c3e3303599","quickstartHeroTitle":{"lines":["Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"andries.engelbrecht@snowflake.com","quickstartHeroForkRepoLink":{"id":"button-c13e650ea8","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/vhol-snowflake-data-wrangler"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/vhol-snowflake-data-wrangler","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}],"quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/vhol-snowflake-data-wrangler"},"flexible_column_cont":{"id":"flexible-column-container-9da85a330a","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-88240c7e12",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-d3708aaf5d","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide will take you through the process of integrating SageMaker and Snowflake using Data Wrangler and SageMaker Studio. It will cover the powerful features in both Snowflake and Data Wrangler to enrich your data with Snowflake Marketplace data, and also how to quickly and effectively evaluate the enriched data’s potential to train ML models.\u003C/p\u003E\n","\u003Cp\u003EWe will be exploring a financial service use of evaluating loan information to predict if a lender will default on a loan. The base data set was derived from loan data from the Lending Club.\u003C/p\u003E\n","\u003Cp\u003EWe will first load this data set into Snowflake to simulate data collected from internal systems for analytical purposes. Using Snowflake’s Zero Copy Cloning feature will make this data available to the Data Science team, without duplicating the data and also protecting the production data from any data manipulation. The data will then be enriched with unemployment data from Knoema on the Snowflake Marketplace.\u003C/p\u003E\n","\u003Cp\u003EFrom within SageMaker Studio we will then retrieve the data using Data Wrangler, which we will use to do analysis of the combined loan and unemployment data. Using Data Wrangler we will perform feature engineering and then analyze the data for ML model potential. The data prep flow will then be used to provide data for model training. Finally we will deploy a scoring pipeline and write the data back to Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image77.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\u003C/li\u003E\u003Cli\u003EFamiliarity with AWS Services, Networking and the Management Console\u003C/li\u003E\u003Cli\u003EBasic knowledge of Python, Jupyter notebook and Machine Learning\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need During the Lab\u003C/h3\u003E\n","\u003Cp\u003ETo participate in the virtual hands-on lab, attendees need the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_cta=quickstarts_\"\u003ESnowflake Enterprise Account on preferred AWS region\u003C/a\u003E \u003Cstrong\u003EACCOUNTADMIN\u003C/strong\u003E access\u003C/li\u003E\u003Cli\u003EAn \u003Ca href=\"https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/\"\u003EAWS Account\u003C/a\u003E with \u003Cstrong\u003Eadmin access\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EIn the AWS account \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html\"\u003Ecreate a VPC\u003C/a\u003E in the \u003Cstrong\u003Esame region\u003C/strong\u003E as the Snowflake account\u003C/li\u003E\u003Cli\u003EIn the VPC \u003Ca href=\"https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Internet_Gateway.html\"\u003Ecreate subnets\u003C/a\u003E in a few AZs with an internet gateway to allow egress traffic to the internet by using a routing table and security group for outbound traffic\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ESnowflake data management features for machine learning\u003C/li\u003E\u003Cli\u003EHow to leverage data in Snowflake's Data Marketplace\u003C/li\u003E\u003Cli\u003EHow to connect SageMaker Data Wrangler and Studio to Snowflake\u003C/li\u003E\u003Cli\u003EThe analysis and feature engineering capabilities in Data Wrangler\u003C/li\u003E\u003Cli\u003EBuilding and deploying SageMaker Pipelines\u003C/li\u003E\u003Cli\u003EOptions to integrate the ML models and pipeline with Snowflake\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake database for machine learning and data enrichment using the Data Marketplace\u003C/li\u003E\u003Cli\u003ESageMaker Studio environment with integration to Snowflake\u003C/li\u003E\u003Cli\u003ESageMaker Data Wrangler flow with Snowflake data\u003C/li\u003E\u003Cli\u003ESageMaker Pipeline to prep Snowflake data and perform inference\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E\n","\u003Cp\u003EThe first thing you will need to do is download the following .sql file that contains a series of SQL commands we will execute throughout this lab. \u003Cstrong\u003EClick the green button to download the file\u003C/strong\u003E\n<button>\u003Ca href=\"https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Data_Wrangler/V2/files/Data_Wrangler_Snowflake_VHOL_V2.sql\"\u003EData_Wrangler_Snowflake_VHOL_V2.sql\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003EAt this point log into your Snowflake. If you have just created a free trial account, feel free to minimize or close and hint boxes that are looking to help guide you. These will not be needed for this lab and most of the hints will be covered throughout the remainder of this exercise.\u003C/p\u003E\n","\u003Cp\u003EIn the Snowflake UI click on \u003Cstrong\u003EWorksheets\u003C/strong\u003E on the left side.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image14.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ECreate a new Worksheet by clicking on the new worksheet button (+ Worksheet) on the top right side.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image91.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo ingest SQL file in the Snowflake UI, navigate to the worksheet name (the worksheet name will be the date and time) on the top left hand side and click on the small down arrow next to it. This will give you the option to change the worksheet name\nand also the option to import a SQL scrpt file. Click on \u003Cstrong\u003EImport SQL from File\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image6.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can now select the .sql file you downloaded earlier.\u003C/p\u003E\n","\u003Cp\u003EThe SQL script file should show up as text in a new worksheet. You may need to scroll to the top of the file to start executing commands.\u003C/p\u003E\n","\u003Cp\u003EEach step throughout the Snowflake portion of the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code line by line as we walk through the lab.\u003C/p\u003E\n","\u003Cp\u003EFirst we will switch to the SECURITYADMIN role and create a role (ML_ROLE), as well as a user (ML_USER) that we will use in the lab.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE SECURITYADMIN;\n\nCREATE OR REPLACE ROLE ML_ROLE COMMENT='ML Role';\n\nGRANT ROLE ML_ROLE TO ROLE SYSADMIN;\n\n\nCREATE OR REPLACE USER ML_USER PASSWORD='AWSSF123'\n\n        DEFAULT_ROLE=ML_ROLE\n\n        DEFAULT_WAREHOUSE=ML_WH\n\n        DEFAULT_NAMESPACE=ML_WORKSHOP.PUBLIC\n\n        COMMENT='ML User';\n\nGRANT ROLE ML_ROLE TO USER ML_USER;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPlease note the default password assigned for the user. If you choose to change it make sure to record the password as you will need to provide it later in the lab for the integration with Data Wrangler.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will grant privileges to the ML_ROLE to allow it to create storage integrations that are needed for Data Wrangler. We will also grant privileges to create databases in Snowflake and also import shares. This will allow the roles to access and import Snowflake Marketplace data, as well as create the Snowflake database that will be used for machine learning. For this we need to use the ACCOUNTADMIN role.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE ACCOUNTADMIN;\n\nGRANT CREATE INTEGRATION ON ACCOUNT TO ROLE ML_ROLE;\n\nGRANT IMPORT SHARE ON ACCOUNT TO ML_ROLE;\n\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE ML_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfigure Amazon SageMaker Studio and Snowflake Storage Integration with AWS\u003C/h2\u003E\n","\u003Cp\u003EBefore configuring the Sagemaker environment we will need to determine the \u003Cstrong\u003ESnowflake Account Identifier\u003C/strong\u003E for your account.\nFirst you can reference the Snowflake region by running the following SQL.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT CURRENT_REGION();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will show you the Snowflake region.\u003C/p\u003E\n","\u003Cp\u003ENext you can find the account name by running.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT CURRENT_ACCOUNT();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECombine the output from CURRENT_ACCOUNT with the reference below for the Snowflake region to get your full Snowflake Account Identifier.\u003C/p\u003E\n","\u003Ch4\u003EUse this \u003Cstrong\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#non-vps-account-locator-formats-by-cloud-platform-and-region\"\u003Ereference\u003C/a\u003E\u003C/strong\u003E to identify the region you need to add to your account identifier.\u003C/h4\u003E\n","\u003Cp\u003E<br />\n<br />\n<br />\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETo save some time we will configure the SageMaker environment and the Snowflake Storage Integration that will be used by Data Wrangler by using a CloudFormation Template.\u003C/strong\u003E <br />\nOpen another tab in your browser and log into your AWS console.\u003C/p\u003E\n","\u003Cp\u003ENext we will go to a Github repo by \u003Cstrong\u003Eclicking the link below\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops\"\u003EGithub Repo\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the launch template button in the \u003Cstrong\u003ELaunch Template\u003C/strong\u003E column\u003C/p\u003E\n","\u003Cp\u003EThe top button will deploy SageMaker Studio in the AWS region.\u003C/p\u003E\n","\u003Cp\u003EThe bottom button can be used if you already have SageMaker Studio deployed in the AWS region.\u003C/p\u003E\n","\u003Cp\u003EWe will use the top button to deploy SageMaker Studio in this example.\u003C/p\u003E\n","\u003Cp\u003ESelect the \u003Cstrong\u003EAWS region\u003C/strong\u003E where you want to deploy the CloudFormation. \u003Cstrong\u003EIt is recommended to use the same region as where you have your Snowflake account\u003C/strong\u003E. For example we will use Oregon(us-west-2).\u003C/p\u003E\n","\u003Cp\u003EOn the Create Stack page select \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image79.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOn the next page you will specify your stack details. Please pay close attention as these parameters are used to create various objects.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EStack name\u003C/strong\u003E - use the default \u003Cstrong\u003Esnowflake-sagemaker-credit-risk-workshop\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERepository Configurations\u003C/strong\u003E - Workshop Templates URL\n\u003Cul\u003E\u003Cli\u003ELeave the URL as is\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Configurations\u003C/strong\u003E:\n\u003Cul\u003E\u003Cli\u003ESnowflake External Stage S3 Bucket Name - This is a base name to which the region and account id will be added. Must be universally unique.\u003C/li\u003E\u003Cli\u003ESnowflake Storage Integration Name - Base name for what the storage integration will be called. Region and account ID will be added. Leave as is or if changed - only use CAPITAL LETTER, numbers and underscores\u003C/li\u003E\u003Cli\u003ESnowflake User Name - ML_USER\u003C/li\u003E\u003Cli\u003ESnowflake Password - The password you assigned to the ML_USER in the previous step - Required to enter.\u003C/li\u003E\u003Cli\u003ESnowflake Account - the Snowflake account name -\u003Cstrong\u003Esee account identifier section above\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESnowflake Role - ML_ROLE\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESageMaker Studio Configurations\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003ESageMaker Studio Domain Name\u003C/li\u003E\u003Cli\u003ESageMaker Studio User Profile Name\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVPC Configurations\u003C/strong\u003E\n\u003Cul\u003E\u003Cli\u003EVPC - Choose existing VPC\u003C/li\u003E\u003Cli\u003EVPC Subnet - Choose existing VPC Subnet(s) in availability zones supported by SageMaker (You can select multiple subnets)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ENext\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOn the next page - \u003Cstrong\u003EConfigure stack options\u003C/strong\u003E - click \u003Cstrong\u003ENext\u003C/strong\u003E (no need to change anything)\u003C/p\u003E\n","\u003Cp\u003EOn the Review page -\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReview the parameters\u003C/li\u003E\u003Cli\u003ECheck the \u003Cstrong\u003EI acknowledge that AWS CloudFormation might create IAM resources with custom names.\u003C/strong\u003E Box\u003C/li\u003E\u003Cli\u003ECheck the \u003Cstrong\u003EI acknowledge that AWS CloudFormation might require the following capability: CAPABILITY_AUTO_EXPAND\u003C/strong\u003E Box\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003ECreate Stack\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image20.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIt may take a few minutes for the stack to be created and we will use the time to continue with Snowflake.\u003C/p\u003E\n\u003Chr\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELoad data in Snowflake and access the Marketplace\u003C/h2\u003E\n","\u003Cp\u003ENext we will create a virtual warehouse that we will use to compute with the \u003Cstrong\u003ESYSADMIN\u003C/strong\u003E role, and then grant all privileges to the \u003Cstrong\u003EML_ROLE\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE SYSADMIN;\n\n--Create Warehouse for AI/ML work\n\nCREATE OR REPLACE WAREHOUSE ML_WH\n\n  WITH WAREHOUSE_SIZE = 'XSMALL'\n\n  AUTO_SUSPEND = 120\n\n  AUTO_RESUME = true\n\n  INITIALLY_SUSPENDED = TRUE;\n\nGRANT ALL ON WAREHOUSE ML_WH TO ROLE ML_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe are now ready to start creating databases and loading data.\u003C/p\u003E\n","\u003Cp\u003EFirst we will switch to the ML_ROLE and use the ML_WH warehouse for compute.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE ROLE ML_ROLE;\n\nUSE WAREHOUSE ML_WH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we will create a database and table that will represent the aggregation of data from internal systems.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE DATABASE IF NOT EXISTS LOANS_V2;\n\n\n\nCREATE OR REPLACE TABLE LOAN_DATA (\n\n    LOAN_ID NUMBER(38,0),\n\n    LOAN_AMNT NUMBER(38,0),\n\n    FUNDED_AMNT NUMBER(38,0),\n\n    TERM VARCHAR(16777216),\n\n    INT_RATE VARCHAR(16777216),\n\n    INSTALLMENT FLOAT,\n\n    GRADE VARCHAR(16777216),\n\n    SUB_GRADE VARCHAR(16777216),\n\n    EMP_TITLE VARCHAR(16777216),\n\n    EMP_LENGTH VARCHAR(16777216),\n\n    HOME_OWNERSHIP VARCHAR(16777216),\n\n    ANNUAL_INC NUMBER(38,0),\n\n    VERIFICATION_STATUS VARCHAR(16777216),\n\n    PYMNT_PLAN VARCHAR(16777216),\n\n    URL VARCHAR(16777216),\n\n    DESCRIPTION VARCHAR(16777216),\n\n    PURPOSE VARCHAR(16777216),\n\n    TITLE VARCHAR(16777216),\n\n    ZIP_SCODE VARCHAR(16777216),\n\n    ADDR_STATE VARCHAR(16777216),\n\n    DTI FLOAT,\n\n    DELINQ_2YRS NUMBER(38,0),\n\n    EARLIEST_CR_LINE DATE,\n\n    INQ_LAST_6MON NUMBER(38,0),\n\n    MNTHS_SINCE_LAST_DELINQ VARCHAR(16777216),\n\n    MNTHS_SINCE_LAST_RECORD VARCHAR(16777216),\n\n    OPEN_ACC NUMBER(38,0),\n\n    PUB_REC NUMBER(38,0),\n\n    REVOL_BAL NUMBER(38,0),\n\n    REVOL_UTIL FLOAT,\n\n    TOTAL_ACC NUMBER(38,0),\n\n    INITIAL_LIST_STATUS VARCHAR(16777216),\n\n    MTHS_SINCE_LAST_MAJOR_DEROG VARCHAR(16777216),\n\n    POLICY_CODE NUMBER(38,0),\n\n    LOAN_DEFAULT NUMBER(38,0),\n\n    ISSUE_MONTH NUMBER(2,0),\n\n    ISSUE_YEAR NUMBER(4,0)\n\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we will create an external stage to load the lab data into the table. This is done from a public S3 bucket to simplify the workshop. Typically an external stage will be using various secure integrations as described in this \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-s3-config.html\"\u003Elink\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE STAGE LOAN_DATA\n\n  url='s3://snowflake-corp-se-workshop/VHOL_Snowflake_Data_Wrangler/V2/data/';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can now use the COPY command to load the data into Snowflake.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECOPY INTO LOAN_DATA FROM @LOAN_DATA/loan_data.csv\n\n    FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThis data represents aggregation from various internal systems for lender information and loans.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can have a quick look and see the various attributes in it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM LOAN_DATA LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowflake Marketplace data\u003C/h3\u003E\n","\u003Cp\u003EWe can now look at additional data in the Snowflake Marketplace that can be helpful for improving ML models. It may be good to look at employment data in the region when analyzing loan defaults. Let’s look in the Snowflake Marketplace and see what external data is available from the data providers.\u003C/p\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EHome (House symbol)\u003C/strong\u003E button on the top left corner to go to the Snowflake UI home menu.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image38.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce in the Home Menu on the top left hand corner click on your username and then hover over the \u003Cstrong\u003ESwitch Role\u003C/strong\u003E menu. Scroll and select the \u003Cstrong\u003EML_ROLE\u003C/strong\u003E from the list of roles.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image37.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen select \u003Cstrong\u003EMarketplace\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image88.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce in the Marketplace type \u003Cstrong\u003EUnemployment\u003C/strong\u003E in the top \u003Cstrong\u003ESearch Data Marketplace\u003C/strong\u003E and hit Enter/Return. This will provide a list of Data Providers with employment data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image68.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the tile with \u003Cstrong\u003EKnoema - Labor Data Atlas.\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will show what data is available from the listing. We will notice indicators such as employment and unemployment rates.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image41.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext click on the \u003Cstrong\u003EGet Data\u003C/strong\u003E button. This will provide a pop up window in which you can create a database in your account that will provide the data from the data provider.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EClick on the down arrow next to Options\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EChange the name of the database to \u003Cstrong\u003EKNOEMA_LABOR_DATA_ATLAS\u003C/strong\u003E and then click the \u003Cstrong\u003EGet\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image54.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWhen the confirmation is provided click on \u003Cstrong\u003EDone\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image54a.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EWorksheets\u003C/strong\u003E on the left hand menu and then select your worksheet you have been using.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image92.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EData from the Snowflake Marketplace does not require any additional work and will show up as a database in your account. A further benefit is that the data will automatically update as soon as the data provider does any updates to the data on their account.\u003C/p\u003E\n","\u003Cp\u003ELet’s start using the marketplace data. First we will create a view to pivot the data for the different employment metrics to columns for easier consumption.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EUSE LOANS_V2.PUBLIC;\n\n\nCREATE OR REPLACE VIEW KNOEMA_EMPLOYMENT_DATA AS (\n\nSELECT *\n\nFROM (SELECT "Measure Name" MeasureName, "Date", "RegionId" State, AVG("Value") Value FROM "KNOEMA_LABOR_DATA_ATLAS"."LABOR"."BLSLA" WHERE "RegionId" is not null and "Date" >= '2018-01-01' AND "Date" < '2018-12-31' GROUP BY "RegionId", "Measure Name", "Date")\n\n        PIVOT(AVG(Value) FOR MeasureName IN ('civilian noninstitutional population', 'employment', 'employment-population ratio', 'labor force', 'labor force participation rate', 'unemployment', 'unemployment rate'))\n\n         AS p (Date, State, civilian_noninstitutional_population, employment, employment_population_ratio, labor_force, labor_force_participation_rate, unemployment, unemployment_rate)\n\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will now create a new table to join the loan data with the unemployment data using the geography and time periods. This will provide us with unemployment data in the region associated with the specific loan.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE UNEMPLOYMENT_DATA AS\n\n        SELECT l.LOAN_ID, e.CIVILIAN_NONINSTITUTIONAL_POPULATION, e.EMPLOYMENT, e.EMPLOYMENT_POPULATION_RATIO, e.LABOR_FORCE,            e.LABOR_FORCE_PARTICIPATION_RATE, e.UNEMPLOYMENT, e.UNEMPLOYMENT_RATE\n\n        FROM LOAN_DATA l LEFT JOIN KNOEMA_EMPLOYMENT_DATA e\n\n            on l.ADDR_STATE = right(e.state,2) and l.issue_month = month(e.date) and l.issue_year = year(e.date);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can quickly look at the metrics by running a simple query.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM UNEMPLOYMENT_DATA LIMIT 100;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDatabase for Machine Learning\u003C/h3\u003E\n","\u003Cp\u003EThe last step is to create a database that can be used by the data science team. This will allow them full access to the data without impacting any of the other analyst or production teams. Snowflake provides a very unique feature called \u003Ca href=\"https://www.youtube.com/watch?v=yQIMmXg7Seg\"\u003EZero Copy Cloning\u003C/a\u003E that will create a new copy of the data by \u003Cstrong\u003Eonly making a copy of the metadata of the objects\u003C/strong\u003E. This drastically speeds up creation of copies and also drastically reduces the storage space needed for data copies.\u003C/p\u003E\n","\u003Cp\u003EThis feature can be very handy for Machine Learning as it will allow for feature engineering in Snowflake and also the ability to save copies of the data used for the training of ML models for future reference.\u003C/p\u003E\n","\u003Cp\u003EIn this lab we will just clone table objects, though complete databases can also be cloned. First we will create a database and schema to clone the tables objects to.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE DATABASE ML_LENDER_DATA;\n\nCREATE OR REPLACE SCHEMA ML_LENDER_DATA.ML_DATA;\n\nUSE ML_LENDER_DATA.ML_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext we will clone the loan_data and unemployment_data tables to the new database.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE TABLE LOAN_DATA_ML CLONE LOANS_V2.PUBLIC.LOAN_DATA;\n\nCREATE TABLE UNEMPLOYMENT_DATA CLONE LOANS_V2.PUBLIC.UNEMPLOYMENT_DATA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will also create a table to allow us to write the ML Model predictions back in the future.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECREATE OR REPLACE TABLE ML_RESULTS (LABEL NUMBER, PREDICTIONS NUMBER, P_DEFAULT FLOAT);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELastly we will get the storage integration information that was created by the CloudFormation template.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESHOW INTEGRATIONS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EMake sure to note the name of the storage integration that was created as it will be used with Data Wrangler configuration.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E We can also get the storage integration name from the CloudFormation stack. Click on the NESTED stack with a name like snowflake-sagemaker-credit-risk-workshop-StorageIntegrationStack and then the Outputs tab in the CloudFormation browser tab.\nYou can also find Snowflake Secret ARN in the Output tab.\u003C/p\u003E\n\u003Chr\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Wrangler - Data Prep & Feature Analysis\u003C/h2\u003E\n","\u003Ch4\u003ELet’s go to SageMaker Studio.\u003C/h4\u003E\n","\u003Cp\u003EClick on this link (\u003Ca href=\"https://console.aws.amazon.com/sagemaker/home\"\u003Ehttps://console.aws.amazon.com/sagemaker/home\u003C/a\u003E) and it will open a new browser tab with the SageMaker Console.\u003C/p\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EAmazon SageMaker - Control panel\u003C/strong\u003E menu on the left hand side.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image76.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EUsers\u003C/strong\u003E you will see Name \u003Cstrong\u003Esagemaker-user\u003C/strong\u003E click on the \u003Cstrong\u003ELaunch App\u003C/strong\u003E button then select \u003Cstrong\u003EStudio\u003C/strong\u003E to open SageMaker Studio\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image93.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will open SageMaker Studio. It may take a minute or two to create the environment.\u003C/p\u003E\n","\u003Ch4\u003ENext we can clone the Git repository that includes all the files we need for the lab in SageMaker Studio.\u003C/h4\u003E\n","\u003Cp\u003EOn the Left side menu click on the Git repository icon and then the \u003Cstrong\u003EClone a Repository\u003C/strong\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image40.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will then copy the Git URL below for the repository in the popup window and then click CLONE\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops\"\u003Ehttps://github.com/dylan-tong-aws/snowflake-sagemaker-workshops\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image82.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Studio environment will then switch to the folder browser.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Cstrong\u003E/snowflake-sagemaker-workshops/loan-default/notebooks\u003C/strong\u003E folder by double clicking each folder in the path.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image39.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOpen the \u003Cstrong\u003Esnowflake-loan-default-workshop-v1.1.ipnyb\u003C/strong\u003E notebook by double clicking on it.\u003C/p\u003E\n","\u003Cp\u003EA window will pop up to select the Image and Kernel you want to use. Under \u003Cstrong\u003EImage\u003C/strong\u003E select the \u003Cstrong\u003ECustom Image\u003C/strong\u003E then \u003Cstrong\u003Esnowflake-workshop\u003C/strong\u003E and then select \u003Cstrong\u003Esnowflake-workshop-v1\u003C/strong\u003E, keep the Kernel as Python 3 and click \u003Cstrong\u003ESelect\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image31.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGive the kernel a little time to startup.\u003C/p\u003E\n","\u003Cp\u003EThe notebook is very well documented with the steps needed for the workshop.\u003C/p\u003E\n","\u003Cp\u003EYou can execute the code sections by selecting them and hitting the \u003Cstrong\u003Erun button\u003C/strong\u003E at the top or \u003Cstrong\u003Eshift+return/enter\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EExecute the first code block to import libraries and set environment variables.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you have run the cell you will note a number in the square brackets next to it.\u003C/p\u003E\n","\u003Cp\u003ENext we will provide access to the AWS Secrets Manager. Execute the next code block.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image75.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EData Wrangler\u003C/h4\u003E\n","\u003Cp\u003EWe will now create a Data Wrangler flow.\u003C/p\u003E\n","\u003Cp\u003ECreate a new Data Wrangler flow by selecting it from the top \u003Cstrong\u003EFile Menu\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFile > New > Data Wrangler Flow\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image67.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis can take a few minutes to start an instance.\u003C/p\u003E\n","\u003Ch5\u003EConnect to Snowflake and Add Data\u003C/h5\u003E\n","\u003Cp\u003EA new tab will open in Studio with untitled.flow\u003C/p\u003E\n","\u003Cp\u003ESelect the \u003Cstrong\u003EImport\u003C/strong\u003E tab at the top left\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image33a.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EAdd data source\u003C/strong\u003E and Select \u003Cstrong\u003ESnowflake\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image33.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can either use the \u003Cstrong\u003Eusername and password\u003C/strong\u003E that was created in Snowflake earlier \u003Cstrong\u003Eor the AWS Secret\u003C/strong\u003E that was created in the CloudFormation template to connect to Snowflake.\nYou can use AWS Secret Manager Console (\u003Ca href=\"https://console.aws.amazon.com/secretsmanager/home\"\u003Ehttps://console.aws.amazon.com/secretsmanager/home\u003C/a\u003E ) to get the secret \u003Cstrong\u003EARN\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EUse the Snowflake account name from Step 3 if you don’t use the AWS Secrets Manager\u003C/p\u003E\n","\u003Cp\u003EThe Snowflake Storage Integration name from Step 4 will be used - \u003Cstrong\u003ESMSNOW_<region>_<accountid>_STORAGE_INTEGRATION</accountid></region>\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EProvide a name for the connection.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image59.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can now navigate the Snowflake data by looking at the Snowflake objects on the left hand side or use the SQL window to run queries.\u003C/p\u003E\n","\u003Cp\u003EWhen using the SQL window you can set the context of the queries, similar to Snowflake. Select:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Warehouse - ML_WH\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDatabase - ML_LENDER_DATA\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESchema - ML_DATA\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ETo see the LOAN_DATA in Data Wrangler execute the following SQL and click \u003Cstrong\u003ERun\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT * FROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou can see the Query Results with the data.\u003C/p\u003E\n","\u003Cp\u003EWe can refine the features by only selecting columns that will likely be good features.\u003C/p\u003E\n","\u003Cp\u003EWe will also Snowflake to generate a repeatable sampling of the table’s data to split the data in a train/test data set.\u003C/p\u003E\n","\u003Cp\u003EExecute the following SQL to acquire a filtered list of potential features.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n\nLOAN_ID, LOAN_AMNT, FUNDED_AMNT,\n\nTERM, INT_RATE, INSTALLMENT,\n\nGRADE, SUB_GRADE, EMP_LENGTH,\n\nHOME_OWNERSHIP, ANNUAL_INC, VERIFICATION_STATUS,\n\nPYMNT_PLAN, PURPOSE, ZIP_SCODE,\n\nDTI, DELINQ_2YRS, EARLIEST_CR_LINE,\n\nINQ_LAST_6MON, MNTHS_SINCE_LAST_DELINQ,\n\nMNTHS_SINCE_LAST_RECORD, OPEN_ACC,\n\nPUB_REC, REVOL_BAL, REVOL_UTIL,\n\nTOTAL_ACC, INITIAL_LIST_STATUS,\n\nMTHS_SINCE_LAST_MAJOR_DEROG,\n\nPOLICY_CODE, LOAN_DEFAULT, ISSUE_MONTH\n\nFROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML\n\nSAMPLE BLOCK (80) REPEATABLE(100)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E and then click the \u003Cstrong\u003EImport\u003C/strong\u003E button on the top right.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image4.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEnter a name for the Dataset - loan_data\u003C/p\u003E\n","\u003Cp\u003EThen click \u003Cstrong\u003EAdd\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image1.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch5\u003EProfile your data\u003C/h5\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003EData Flow\u003C/strong\u003E tab\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image32a.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EProfile the data by Clicking the \u003Cstrong\u003E+ sign\u003C/strong\u003E next to the Data types block\u003C/p\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003EAdd Analysis\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image32.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the Analysis select:\u003C/p\u003E\n","\u003Cp\u003EType - Histogram\u003C/p\u003E\n","\u003Cp\u003EX axis - LOAN_DEFAULT\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image60.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen click \u003Cstrong\u003EPreview\u003C/strong\u003E to get an analysis of the skew.\u003C/p\u003E\n","\u003Ch4\u003EApply Feature Transformations\u003C/h4\u003E\n","\u003Cp\u003ENext we will use Data Wrangler to perform some feature transformations.\u003C/p\u003E\n","\u003Cp\u003EIn the Analysis window click \u003Cstrong\u003EData flow\u003C/strong\u003E at the top.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image64.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003E+\u003C/strong\u003E and select \u003Cstrong\u003EAdd Transform\u003C/strong\u003E by the Data types box.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image42.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003E+ Add Step\u003C/strong\u003E - To add a transform step\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image78.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst we will fix the \u003Cstrong\u003EINT_RATE\u003C/strong\u003E column from a string with a % sign to a numeric data type.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EScroll down under ADD TRANSFORM and click on \u003Cstrong\u003ESearch and edit\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect Input Columns as INT_RATE\u003C/li\u003E\u003Cli\u003EEnter % in the Pattern field\u003C/li\u003E\u003Cli\u003EIn the \u003Cstrong\u003EReplacement string\u003C/strong\u003E \u003Cem\u003Etype space and then delete it\u003C/em\u003E to have % replaced with an empty string\u003C/li\u003E\u003Cli\u003EClick Preview\u003C/li\u003E\u003Cli\u003EClick Add\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image11.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo add additional steps click \u003Cstrong\u003E+ Add Step\u003C/strong\u003E each time\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENext select \u003Cstrong\u003EParse column as type\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect INT_RATE column\u003C/li\u003E\u003Cli\u003EFrom: String\u003C/li\u003E\u003Cli\u003ETo: Float\u003C/li\u003E\u003Cli\u003EClick Preview\u003C/li\u003E\u003Cli\u003EClick Add\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image21.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will address the \u003Cstrong\u003EVERIFICATION_STATUS\u003C/strong\u003E column, which has various string values to indicate boolean values.\u003C/p\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003ECustom Transform\u003C/strong\u003E then make sure \u003Cstrong\u003EPython(PySpark)\u003C/strong\u003E is selected and copy the following Python code in the code box to repalce the existing text\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Efrom pyspark.sql.functions import udf\nfrom pyspark.sql.types import LongType\n\n\ndef categories(status) :\n  if not status :\n    return None\n  elif status == "not verified" :\n    return 0\n  elif status == "VERIFIED - income":\n    return 1\n  elif status == "VERIFIED - income source":\n    return 1\n  else :\n    return None\nbucket_udf = udf(categories, LongType())\n\ndf = df.withColumn("VERIFIED", bucket_udf("VERIFICATION_STATUS"))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESelect Preview and then Add\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image3.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis code creates a new column VERIFIED with boolean values.\u003C/p\u003E\n","\u003Cp\u003ENow we can drop the original VERIFICATION_STATUS column.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Cstrong\u003EManage columns\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ETransform - select Drop Column\u003C/li\u003E\u003Cli\u003EColumn to Drop - select VERIFICATION_STATUS\u003C/li\u003E\u003Cli\u003ESelect Preview and then Add\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image57.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFinally we will drop the LOAN_ID column using the steps above.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image35.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EData flow\u003C/strong\u003E. You should see the five transform steps of your data prep flow.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image47.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EData Validation\u003C/h4\u003E\n","\u003Cp\u003ENext we will check for \u003Cstrong\u003ETarget Leakage\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003ETarget leakage occurs when you accidently train a model with features that are not available in production. As a consequence, you end up with a deceptively effective model in development that causes problems in production. You can mitigate production issues by performing target leakage analysis.\u003C/p\u003E\n","\u003Cp\u003EClick the \u003Cstrong\u003E+ sign\u003C/strong\u003E next to the 5 Transform Steps and select Add analysis.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image34.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the Analysis select:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAnalysis type - \u003Cstrong\u003ETarget Leakage\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EMax Features - 30\u003C/li\u003E\u003Cli\u003EProblem type - Classification\u003C/li\u003E\u003Cli\u003ETarget - LOAN_DEFAULT\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image58.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003EPreview\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image63.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe report indicates that there is no target leakage risk. It does detect some potentially redundant features.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENext we will create a Bias Report\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOur data does not have any obvious sensitive attributes like gender and race. However, it does contain zip codes. It's possible that we have a flawed dataset with an abnormal number of loan defaults in minority communities. This might not represent the actual distribution. Regardless, this situation could create a model that is biased against minorities resulting in legal risk.\u003C/p\u003E\n","\u003Cp\u003EIn the Analysis window select:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAnalysis Type - \u003Cstrong\u003EBias Report\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESELECT the column your model predicts (target): LOAN_DEFAULT\u003C/li\u003E\u003Cli\u003EIs your predicted column a value or threshold?: Value\u003C/li\u003E\u003Cli\u003EPredicted value(s): 0;1\u003C/li\u003E\u003Cli\u003ESELECT the column to analyze for bias: ZIPS_CODE\u003C/li\u003E\u003Cli\u003EIs your column a value or threshold?: Value\u003C/li\u003E\u003Cli\u003EColumn value(s) to analyze for bias: 200xx;207xx;206xx;900xx;100xx;941xx\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECheck for bias\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image27.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe report does not reveal any salient data bias issues.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image9.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EPrototype Model\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a Quick Model Report\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EAmazon Data Wrangler provides a Quick Model report which can serve as a prototyping mechanism. The report will sample your dataset, process your flow and generates a Random Forest Model. The report provides model and feature importance scores to help you assess:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EWhat features are most impactful?\u003C/li\u003E\u003Cli\u003EDoes your data have enough predictive signals to produce a practical model?\u003C/li\u003E\u003Cli\u003EAre your changes to your dataset leading to improvements?\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENavigate to the Analysis panel from the tail end of your flow—as you did in the previous section.\u003C/p\u003E\n","\u003Cp\u003EConfigure your report:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAnalysis type: \u003Cstrong\u003EQuick Model\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EAnalysis name: Quick Test\u003C/li\u003E\u003Cli\u003ELabel: LOAN_DEFAULT\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image51.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIt can take few minutes to generate a report like the following:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image28.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETake note of the feature importance ranking in the bar chart. This gives you an approximation of which features have strong predictive signals.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIterate, Experiment and Improve\u003C/h2\u003E\n","\u003Cp\u003EWe will now add additional data to your existing flow.\u003C/p\u003E\n","\u003Cp\u003EFirst click \u003Cstrong\u003EData flow\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003ESELECT the \u003Cstrong\u003EImport tab\u003C/strong\u003E at the top and click on the Snowflake icon.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image66.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERun the following query to extract the unemployment rate data that you obtained from the Snowflake Marketplace.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT LOAN_ID, UNEMPLOYMENT_RATE\n\nFROM ML_LENDER_DATA.ML_DATA.UNEMPLOYMENT_DATA\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E and then the \u003Cstrong\u003EImport button\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EName the dataset unemployment_data\u003C/p\u003E\n","\u003Cp\u003EClick Add\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image84.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext, you're going to merge the two datasets. There are many ways to do this. You could have performed this entirely using Snowflake. In this lab, you'll learn how to perform this merge through Data Wrangler.\u003C/p\u003E\n","\u003Cp\u003EClick back to the \u003Cstrong\u003EData flow\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image64.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst delete the last transformation from the original flow, so that we have \u003Cstrong\u003ELOAN_ID\u003C/strong\u003E available.\u003C/p\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003Elast step\u003C/strong\u003E and then on the + next to it and select \u003Cstrong\u003EDelete\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image69.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EConfirm the Delete\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image10.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext we will merge the data sets using a join operator\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003E+\u003C/strong\u003E at the end of the original flow and select the \u003Cstrong\u003EJoin\u003C/strong\u003E operator.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image8.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESELECT the other flow by clicking on the \u003Cstrong\u003EData Types\u003C/strong\u003E box on the bottom flow\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image29.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EConfigure\u003C/strong\u003E - lower left of the window\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image18.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESELECT Left Outer as the Join Type.\u003C/p\u003E\n","\u003Cp\u003ESELECT LOAN_ID for both the Left and Right join keys.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image2.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003EPreview\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThen click \u003Cstrong\u003EAdd\u003C/strong\u003E in the top right corner\u003C/p\u003E\n","\u003Cp\u003ESelect the Join Node and Add Transform\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image26.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDrop the columns, LOAN_ID_0 and LOAN_ID_1 using the same transformation step as before.\u003C/p\u003E\n","\u003Cp\u003EManage Columns > Drop Column > Columns to Drop > Loan_ID_0 and Loan_ID_1\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image12.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EEvaluate your Dataset Modifications\u003C/p\u003E\n","\u003Cp\u003ECreate a new Quick Model report to assess the impact of your modifications.\u003C/p\u003E\n","\u003Cp\u003EThe steps should be similar to the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the Analysis tab\u003C/li\u003E\u003Cli\u003EAnalysis type - Quick Model\u003C/li\u003E\u003Cli\u003ELabel - LOAN_DEFAULT\u003C/li\u003E\u003Cli\u003EPreview\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image50.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EA couple of key takeaways:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUNEMPLOYMENT_RATE is ranked as the most important feature.\u003C/li\u003E\u003Cli\u003EThe F1 score increased substantially.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis tells us that we are likely heading in the right direction. We added a feature that generated notable improvements to the "quick model" and the new feature had the greatest impact.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate the Dataset and Train your Model\u003C/h2\u003E\n","\u003Cp\u003EWe are now ready to fully train and tune a model. First, we need to generate our datasets by executing the data flow that we've created.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExport your Data Flow\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EDataWrangler supports multiple ways to export the flow for execution.\u003C/p\u003E\n","\u003Cp\u003EIn this lab, you will select the option that generates a notebook that can be run to execute the flow as a SageMaker Processing job. This is the simplest option.\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Cstrong\u003EData flow\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the \u003Cstrong\u003E+ symbol\u003C/strong\u003E next to the last transform step and select \u003Cstrong\u003EExport to\u003C/strong\u003E and select \u003Cstrong\u003EAmazon S3(via Jupyter Notebook)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image25.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will generate a new notebook tab. Select the new notebook.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image30.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EFollow the steps outlined in the generated notebook.\u003C/li\u003E\u003Cli\u003ERun the cells and wait for the processing job to complete.\u003C/li\u003E\u003Cli\u003ECOPY the output S3 URI of the processed dataset.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis can take a few minutes\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image70.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe S3 URI will look similar to: s3://(YOUR_BUCKET)/export-flow-23-23-17-34-6a8a80ec/output/data-wrangler-flow-processing-23-23-17-34-6a8a80ec\u003C/p\u003E\n","\u003Cp\u003ECOPY the S3 URI to the PREP_DATA_S3 variable in your \u003Cstrong\u003Einitial workshop notebook\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image73.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETIP:\u003C/strong\u003E You can monitor the processing jobs in the SageMaker Console\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image72.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ETrain your Model\u003C/h3\u003E\n","\u003Cp\u003EYour data can now be used to train a model. We'll be using an AutoML toolkit, \u003Ca href=\"https://auto.gluon.ai/stable/tutorials/tabular_prediction/index.html\"\u003EAutoGluon\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EMore details are provided in the notebook description.\u003C/p\u003E\n","\u003Cp\u003EExecute the next code cell to set the parameters for the remote training job.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image13.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe following cell will launch the remote training job. This will take a few minutes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image46.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can monitor the training job in the SageMaker Console\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image17.png\" alt=\"\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDeploy your Model [Optional]\u003C/h2\u003E\n","\u003Cp\u003EYou can serve your predictions in a couple of ways. You could deploy the model as a real-time hosted endpoint on SageMaker and integrate it with Snowflake as an External Function. This will enable you to query your predictions in real-time and minimize data staleness.\u003C/p\u003E\n","\u003Cp\u003EAlternatively, you can pre-calculate your predictions as a transient batch process. In the following section, you will use Batch Transform to do just that. When your use case allows you to pre-calculate predictions, Batch Transform is a good option.\u003C/p\u003E\n","\u003Cp\u003EIn the following sections we are going to deploy our model as a batch inference pipeline. The pipeline is designed to consume data from Snowflake, process it using our DataWrangler flow and then pre-calculate predictions using our trained model and Batch Transform.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EModify your Data Preparation flow for Inference\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EFirst we will make a copy of our flow file.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERight click on the \u003Cstrong\u003Euntitled.flow\u003C/strong\u003E file and select \u003Cstrong\u003EDuplicate\u003C/strong\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image71.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ERight click on the duplicate copy \u003Cstrong\u003Euntitled-Copy1.flow\u003C/strong\u003E and select Rename\u003C/li\u003E\u003Cli\u003EUse \u003Cstrong\u003Einference_flow_loan.flow\u003C/strong\u003E as the new name\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETIP:\u003C/strong\u003E Click on the Folder icon on the left hand side of the screen to see the files\u003C/p\u003E\n","\u003Cp\u003ESet the INFERENCE_FLOW_NAME to the new flow file in your workshop notebook. By running the cell shown below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image62.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENext we will open the new flow file by double clicking on it\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe can now change the data source for the flow:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EData Flow\u003C/strong\u003E tab at the top\u003C/li\u003E\u003Cli\u003EClick on the + next to the Snowflake:loan_data source and select Edit Query\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image61.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003EClick on the Snowflake Connection button\u003C/li\u003E\u003Cli\u003EReplace the current SQL query with a new query that will retrieve the 20% sample data that we didn’t use for training.\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n  L1.LOAN_ID,\n  L1.LOAN_AMNT,\n  L1.FUNDED_AMNT,\n  L1.TERM,\n  L1.INT_RATE,\n  L1.INSTALLMENT,\n  L1.GRADE,\n  L1.SUB_GRADE,\n  L1.EMP_LENGTH,\n  L1.HOME_OWNERSHIP,\n  L1.ANNUAL_INC,\n  L1.VERIFICATION_STATUS,\n  L1.PYMNT_PLAN,\n  L1.PURPOSE,\n  L1.ZIP_SCODE,\n  L1.DTI,\n  L1.DELINQ_2YRS,\n  L1.EARLIEST_CR_LINE,\n  L1.INQ_LAST_6MON,\n  L1.MNTHS_SINCE_LAST_DELINQ,\n  L1.MNTHS_SINCE_LAST_RECORD,\n  L1.OPEN_ACC,\n  L1.PUB_REC,\n  L1.REVOL_BAL,\n  L1.REVOL_UTIL,\n  L1.TOTAL_ACC,\n  L1.LOAN_DEFAULT,\n  L1.ISSUE_MONTH\nFROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML AS L1\n LEFT OUTER JOIN\n (SELECT * FROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML sample block (80) REPEATABLE(100)) AS L2\n ON L1.LOAN_ID = L2.LOAN_ID\nWHERE L2.LOAN_ID IS NULL\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E and then the \u003Cstrong\u003EApply\u003C/strong\u003E button on the top right\u003C/li\u003E\u003Cli\u003EName the dataset loan_inference and click the \u003Cstrong\u003EAdd\u003C/strong\u003E button\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe purpose of this flow is for use in production to prep data from new loan applications. This data is then passed through your model to make predications about the default risks of these new applicants.\u003C/p\u003E\n","\u003Cp\u003EFor testing purposes, we're using the 20% of the data set we didn't use for training and we're including the LOAN_DEFAULT attribute so that we can evaluate our models and perform error analysis. We're going to move the the LOAN_DEFAULT column to the first position in our dataset, so that it's easier for us to filter this data out of the model input and merge it with the model's predictions.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESelect \u003Cstrong\u003EAdd transform\u003C/strong\u003E at the end of your data flow.\u003C/li\u003E\u003Cli\u003EClick on the orange button labeled \u003Cstrong\u003EAdd step\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003E"Manage columns"\u003C/strong\u003E category.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EMove column\u003C/strong\u003E under the \u003Cstrong\u003ETransform\u003C/strong\u003E dialog.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EMove to start\u003C/strong\u003E under the \u003Cstrong\u003EMove type\u003C/strong\u003E dialog.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ELOAN_DEFAULT\u003C/strong\u003E under the \u003Cstrong\u003EColumn to move\u003C/strong\u003E dialog.\u003C/li\u003E\u003Cli\u003EClick on the \u003Cstrong\u003EPreview\u003C/strong\u003E button to preview the changes and finalize the transformation by clicking on the \u003Cstrong\u003EAdd\u003C/strong\u003E button.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image94.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ERe-export and re-factor your flow as a Pipeline\u003C/h4\u003E\n","\u003Cp\u003EGo back to the \u003Cstrong\u003EData Flow\u003C/strong\u003E by clicking on the top Data Flow link\u003C/p\u003E\n","\u003Cp\u003EAs previously select the \u003Cstrong\u003E+\u003C/strong\u003E next to the last step in the flow.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image43.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThen go to \u003Cstrong\u003EExport to\u003C/strong\u003E and select \u003Cstrong\u003ESageMaker Pipelines (via Jupyter Notebook)\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will generate a new notebook - \u003Cstrong\u003Einference_flow_loan.ipynb\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EScroll down in the notebook till you find the cell with output_name in it.\u003C/p\u003E\n","\u003Cp\u003ECOPY the node ID form this cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image45.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn practice, you will need to refactor the exported script. This has been done for you, so all you need to do is locate the export node-id. Each step in your data flow is a unique node and the export script is dependent on the node that you select for export.\u003C/p\u003E\n","\u003Ch4\u003EGo back to your workshop notebook - snowflake-loan-default-workshop-v1.1.ipynb\u003C/h4\u003E\n","\u003Cp\u003ECOPY the node ID to the FLOW_NODE_ID cell and run the cell\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image19.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can run the next cell if you like to see the refactored script.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image81.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENext you will run your batch scoring pipeline by executing the next cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image85.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will process the data prep steps and then run batch inference using the model that was previously trained. These steps will take some time to complete.\u003C/p\u003E\n","\u003Cp\u003EYou can monitor the pipeline in SageMaker Studio.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESELECT the SageMaker resources icon on the left hand menu bar\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image52.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003ESELECT Pipelines from the drop down menu\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image53.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003ERight click on your pipeline and select \u003Cstrong\u003EOpen pipeline details\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image15.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003EThis will open a pipeline tab. Right click on the status of the pipeline and select \u003Cstrong\u003EOpen execution details\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image48.png\" alt=\"\"\u003E\u003C/li\u003E\u003Cli\u003EThis opens a tab with the pipeline steps. You can click on each step to get more information.\u003C/li\u003E\u003Cli\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image7.png\" alt=\"\"\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETIP:\u003C/strong\u003E Click the small refresh icon at the top left to refresh your few from time to time\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEvaluate Model Performance and Write Back to Snowflake [Optional]\u003C/h2\u003E\n","\u003Cp\u003ESince the data set is small enough we can load it to a local pandas dataframe and review the results.\u003C/p\u003E\n","\u003Cp\u003EThe next cell will load the data set and provide an output of the results.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image83.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe can use some utilities to evaluate how well the model performed using the test data set.\u003C/p\u003E\n","\u003Cp\u003EExecute the next 2 cells\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image65.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image87.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe last cell will provide an interactive chart to see how well the model performed based on the threshold we set for the prediction.\u003C/p\u003E\n","\u003Ch4\u003EWriteback to Snowflake\u003C/h4\u003E\n","\u003Cp\u003ETypically for large batch transforms we will use Snowflake’s automated capability to read data from S3 called \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html\"\u003ESnowpipe\u003C/a\u003E, or alternatively the \u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html\"\u003ECOPY\u003C/a\u003E command to perform bulk loads. Since this data set is in a dataframe we can use the Python connector to write it directly back to Snowflake.\u003C/p\u003E\n","\u003Cp\u003EFirst we will use the AWS Secret we create with the CloudFormation template. Go to the \u003Ca href=\"https://console.aws.amazon.com/secretsmanager/home\"\u003ESecrets Manager Console\u003C/a\u003E. Select the Snowflake Secret and copy the Secret Name i.e. \u003Cstrong\u003ESnowflakeSecret-P4qyGUyk67hj\u003C/strong\u003E in the cell below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image44.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe next cell creates a function to retrieve AWS Secrets for use in the notebook. Run the cell.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image55.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe next cell will establish a connection with Snowflake using the secret’s information.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image56.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe last cell will write the dataframe data into a Snowflake table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETIP:\u003C/strong\u003E The same connection can be used to read data from Snowflake as well as issue Snowflake commands to help process data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image24.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can now query the results in your Snowflake account\u003C/p\u003E\n","\u003Cp\u003ECongratulations! You have completed the lab.\u003C/p\u003E\n\u003Chr\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusions & Next Steps\u003C/h2\u003E\n","\u003Cp\u003EIn this lab we build an example of how you can enrich your internal data with Snowflake Marketplace data to improve the performance of your Machine Learning Models. We also covered how you can integrate Data Wrangler with Snowflake to gain access to the data and drive pipelines for your ML models.\u003C/p\u003E\n","\u003Cp\u003EAdditionally we covered how you can use SageMaker Studio and deploy CloudFormation Templates to create prebuild kernels with the Snowflake Python Connector. Also how to deploy the Snowflake Storage Integrations with a CloudFormation template and using AWS Secrets Manager to provide more secure connections with Snowflake.\u003C/p\u003E\n","\u003Cp\u003ERelated Resources\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/blogs/apn/enabling-data-centric-artificial-intelligence-through-snowflake-and-amazon-sagemaker/\"\u003EData Centric AI with Snowflake and Amazon SageMaker\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-marketplace.html\"\u003ESnowflake Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/sagemaker/data-wrangler/\"\u003ESageMaker Data Wrangler\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://aws.amazon.com/sagemaker/studio/\"\u003ESageMaker Studio\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops?_fsi=TNKw8Mx5&_fsi=TNKw8Mx5\"\u003EFork GitHub Repo\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/04/Student-Loan-Default-Prediction-using-Snowpark-ML-and-Amazon-SageMaker.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/integrating-machine-learning-models-with-snowpark-ml-a-guide-for-azureml-and-sagemaker-users-735292843a7b\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/m0eXjwiHU8E?list=TLGGY7poE1orVScyNDA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Data-centric Approach to Machine Learning Using Snowflake and Amazon SageMaker Data Wrangler","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\r\n\u003C!-- ------------------------ --\u003E\r\n## Overview\r\n\r\n\r\nThis guide will take you through the process of integrating SageMaker and Snowflake using Data Wrangler and SageMaker Studio. It will cover the powerful features in both Snowflake and Data Wrangler to enrich your data with Snowflake Marketplace data, and also how to quickly and effectively evaluate the enriched data’s potential to train ML models.\r\n\r\n\r\nWe will be exploring a financial service use of evaluating loan information to predict if a lender will default on a loan. The base data set was derived from loan data from the Lending Club.\r\n\r\n\r\nWe will first load this data set into Snowflake to simulate data collected from internal systems for analytical purposes. Using Snowflake’s Zero Copy Cloning feature will make this data available to the Data Science team, without duplicating the data and also protecting the production data from any data manipulation. The data will then be enriched with unemployment data from Knoema on the Snowflake Marketplace.\r\n\r\n\r\nFrom within SageMaker Studio we will then retrieve the data using Data Wrangler, which we will use to do analysis of the combined loan and unemployment data. Using Data Wrangler we will perform feature engineering and then analyze the data for ML model potential. The data prep flow will then be used to provide data for model training. Finally we will deploy a scoring pipeline and write the data back to Snowflake.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image77.png)\r\n\r\n\r\n\r\n### Prerequisites\r\n\r\n- Familiarity with Snowflake, basic SQL knowledge, Snowsight UI and Snowflake objects\r\n- Familiarity with AWS Services, Networking and the Management Console\r\n- Basic knowledge of Python, Jupyter notebook and Machine Learning\r\n\r\n### What You'll Need During the Lab\r\n\r\nTo participate in the virtual hands-on lab, attendees need the following:\r\n\r\n- A [Snowflake Enterprise Account on preferred AWS region](https://signup.snowflake.com/?utm_cta=quickstarts_) **ACCOUNTADMIN** access\r\n- An [AWS Account](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/) with **admin access**\r\n- In the AWS account [create a VPC](https://docs.aws.amazon.com/vpc/latest/userguide/working-with-vpcs.html) in the **same region** as the Snowflake account\r\n- In the VPC [create subnets](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_Internet_Gateway.html) in a few AZs with an internet gateway to allow egress traffic to the internet by using a routing table and security group for outbound traffic\r\n\r\n### What You'll Learn\r\n\r\n- Snowflake data management features for machine learning\r\n- How to leverage data in Snowflake's Data Marketplace\r\n- How to connect SageMaker Data Wrangler and Studio to Snowflake\r\n- The analysis and feature engineering capabilities in Data Wrangler\r\n- Building and deploying SageMaker Pipelines\r\n- Options to integrate the ML models and pipeline with Snowflake\r\n\r\n### What You'll Build\r\n\r\n- A Snowflake database for machine learning and data enrichment using the Data Marketplace\r\n- SageMaker Studio environment with integration to Snowflake\r\n- SageMaker Data Wrangler flow with Snowflake data\r\n- SageMaker Pipeline to prep Snowflake data and perform inference\r\n\r\n\r\n\r\n\r\n\r\n\r\n- - -\r\n\r\n\r\n## Setting up Snowflake\r\n\r\n\r\nThe first thing you will need to do is download the following .sql file that contains a series of SQL commands we will execute throughout this lab. **Click the green button to download the file**\r\n\u003Cbutton\u003E[Data_Wrangler_Snowflake_VHOL_V2.sql](https://snowflake-corp-se-workshop.s3.us-west-1.amazonaws.com/VHOL_Snowflake_Data_Wrangler/V2/files/Data_Wrangler_Snowflake_VHOL_V2.sql)\u003C/button\u003E\r\n\r\nAt this point log into your Snowflake. If you have just created a free trial account, feel free to minimize or close and hint boxes that are looking to help guide you. These will not be needed for this lab and most of the hints will be covered throughout the remainder of this exercise.\r\n\r\nIn the Snowflake UI click on **Worksheets** on the left side.\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image14.png)\r\n\r\nCreate a new Worksheet by clicking on the new worksheet button (+ Worksheet) on the top right side.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image91.png)\r\n\r\nTo ingest SQL file in the Snowflake UI, navigate to the worksheet name (the worksheet name will be the date and time) on the top left hand side and click on the small down arrow next to it. This will give you the option to change the worksheet name\r\nand also the option to import a SQL scrpt file. Click on **Import SQL from File**\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image6.png)\r\n\r\nYou can now select the .sql file you downloaded earlier.\r\n\r\nThe SQL script file should show up as text in a new worksheet. You may need to scroll to the top of the file to start executing commands.\r\n\r\n\r\nEach step throughout the Snowflake portion of the guide has an associated SQL command to perform the work we are looking to execute, and so feel free to step through each action running the code line by line as we walk through the lab.\r\n\r\n\r\n\r\nFirst we will switch to the SECURITYADMIN role and create a role (ML_ROLE), as well as a user (ML_USER) that we will use in the lab.\r\n\r\n\r\n\r\n```\r\nUSE ROLE SECURITYADMIN;\r\n\r\nCREATE OR REPLACE ROLE ML_ROLE COMMENT='ML Role';\r\n\r\nGRANT ROLE ML_ROLE TO ROLE SYSADMIN;\r\n\r\n\r\nCREATE OR REPLACE USER ML_USER PASSWORD='AWSSF123'\r\n\r\n        DEFAULT_ROLE=ML_ROLE\r\n\r\n        DEFAULT_WAREHOUSE=ML_WH\r\n\r\n        DEFAULT_NAMESPACE=ML_WORKSHOP.PUBLIC\r\n\r\n        COMMENT='ML User';\r\n\r\nGRANT ROLE ML_ROLE TO USER ML_USER;\r\n```\r\n\r\n\r\n\r\n**Please note the default password assigned for the user. If you choose to change it make sure to record the password as you will need to provide it later in the lab for the integration with Data Wrangler.**\r\n\r\n\r\n\r\nNext we will grant privileges to the ML_ROLE to allow it to create storage integrations that are needed for Data Wrangler. We will also grant privileges to create databases in Snowflake and also import shares. This will allow the roles to access and import Snowflake Marketplace data, as well as create the Snowflake database that will be used for machine learning. For this we need to use the ACCOUNTADMIN role.\r\n\r\n\r\n\r\n```\r\nUSE ROLE ACCOUNTADMIN;\r\n\r\nGRANT CREATE INTEGRATION ON ACCOUNT TO ROLE ML_ROLE;\r\n\r\nGRANT IMPORT SHARE ON ACCOUNT TO ML_ROLE;\r\n\r\nGRANT CREATE DATABASE ON ACCOUNT TO ROLE ML_ROLE;\r\n```\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Configure Amazon SageMaker Studio and Snowflake Storage Integration with AWS\r\n\r\nBefore configuring the Sagemaker environment we will need to determine the **Snowflake Account Identifier** for your account.\r\nFirst you can reference the Snowflake region by running the following SQL.\r\n```\r\nSELECT CURRENT_REGION();\r\n```\r\nThis will show you the Snowflake region.  \r\n\r\nNext you can find the account name by running.\r\n```\r\nSELECT CURRENT_ACCOUNT();\r\n```\r\n\r\nCombine the output from CURRENT_ACCOUNT with the reference below for the Snowflake region to get your full Snowflake Account Identifier.\r\n\r\n#### Use this **[reference](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#non-vps-account-locator-formats-by-cloud-platform-and-region)** to identify the region you need to add to your account identifier.\r\n\r\n\r\n\u003Cbr /\u003E\r\n\u003Cbr /\u003E\r\n\u003Cbr /\u003E\r\n\r\n\r\n**To save some time we will configure the SageMaker environment and the Snowflake Storage Integration that will be used by Data Wrangler by using a CloudFormation Template.** \u003Cbr /\u003E\r\nOpen another tab in your browser and log into your AWS console.\r\n\r\nNext we will go to a Github repo by **clicking the link below**.\r\n\r\n\u003Cbutton\u003E[Github Repo](https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops)\u003C/button\u003E\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea1.png)\r\n\r\nClick on the launch template button in the **Launch Template** column\r\n\r\nThe top button will deploy SageMaker Studio in the AWS region.\r\n\r\nThe bottom button can be used if you already have SageMaker Studio deployed in the AWS region.\r\n\r\nWe will use the top button to deploy SageMaker Studio in this example.\r\n\r\nSelect the **AWS region** where you want to deploy the CloudFormation. **It is recommended to use the same region as where you have your Snowflake account**. For example we will use Oregon(us-west-2).\r\n\r\n\r\n\r\nOn the Create Stack page select **Next**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image79.png)\r\n\r\n\r\n\r\nOn the next page you will specify your stack details. Please pay close attention as these parameters are used to create various objects.\r\n\r\n\r\n\r\n- **Stack name** - use the default **snowflake-sagemaker-credit-risk-workshop**\r\n- **Repository Configurations** - Workshop Templates URL\r\n  - Leave the URL as is\r\n- **Snowflake Configurations**:\r\n  - Snowflake External Stage S3 Bucket Name - This is a base name to which the region and account id will be added. Must be universally unique.\r\n  - Snowflake Storage Integration Name - Base name for what the storage integration will be called. Region and account ID will be added. Leave as is or if changed - only use CAPITAL LETTER, numbers and underscores\r\n  - Snowflake User Name - ML_USER\r\n  - Snowflake Password - The password you assigned to the ML_USER in the previous step - Required to enter.\r\n  - Snowflake Account - the Snowflake account name -**see account identifier section above**\r\n  - Snowflake Role - ML_ROLE\r\n* **SageMaker Studio Configurations**\r\n  - SageMaker Studio Domain Name\r\n  - SageMaker Studio User Profile Name\r\n* **VPC Configurations**\r\n  - VPC - Choose existing VPC\r\n  - VPC Subnet - Choose existing VPC Subnet(s) in availability zones supported by SageMaker (You can select multiple subnets)\r\n\r\n\r\n\r\n- - -\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea2.png)\r\n\r\n\r\n\r\nClick **Next**\r\n\r\n\r\n\r\nOn the next page - **Configure stack options** - click **Next** (no need to change anything)\r\n\r\n\r\n\r\nOn the Review page - \r\n\r\n- Review the parameters\r\n- Check the **I acknowledge that AWS CloudFormation might create IAM resources with custom names.** Box\r\n- Check the **I acknowledge that AWS CloudFormation might require the following capability: CAPABILITY_AUTO_EXPAND** Box\r\n- Click on **Create Stack**\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image20.png)\r\n\r\n\r\n\r\n\r\n\r\nIt may take a few minutes for the stack to be created and we will use the time to continue with Snowflake.\r\n\r\n\r\n\r\n\r\n\r\n- - -\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Load data in Snowflake and access the Marketplace\r\n\r\n\r\nNext we will create a virtual warehouse that we will use to compute with the **SYSADMIN** role, and then grant all privileges to the **ML_ROLE**.\r\n\r\n\r\n\r\n```\r\nUSE ROLE SYSADMIN;\r\n\r\n--Create Warehouse for AI/ML work\r\n\r\nCREATE OR REPLACE WAREHOUSE ML_WH\r\n\r\n  WITH WAREHOUSE_SIZE = 'XSMALL'\r\n\r\n  AUTO_SUSPEND = 120\r\n\r\n  AUTO_RESUME = true\r\n\r\n  INITIALLY_SUSPENDED = TRUE;\r\n\r\nGRANT ALL ON WAREHOUSE ML_WH TO ROLE ML_ROLE;\r\n```\r\n\r\n\r\n\r\nWe are now ready to start creating databases and loading data.\r\n\r\nFirst we will switch to the ML_ROLE and use the ML_WH warehouse for compute.\r\n\r\n\r\n\r\n```\r\nUSE ROLE ML_ROLE;\r\n\r\nUSE WAREHOUSE ML_WH;\r\n```\r\n\r\n\r\n\r\nNext we will create a database and table that will represent the aggregation of data from internal systems.\r\n\r\n\r\n\r\n```\r\nCREATE DATABASE IF NOT EXISTS LOANS_V2;\r\n\r\n\r\n\r\nCREATE OR REPLACE TABLE LOAN_DATA (\r\n\r\n    LOAN_ID NUMBER(38,0),\r\n\r\n    LOAN_AMNT NUMBER(38,0),\r\n\r\n    FUNDED_AMNT NUMBER(38,0),\r\n\r\n    TERM VARCHAR(16777216),\r\n\r\n    INT_RATE VARCHAR(16777216),\r\n\r\n    INSTALLMENT FLOAT,\r\n\r\n    GRADE VARCHAR(16777216),\r\n\r\n    SUB_GRADE VARCHAR(16777216),\r\n\r\n    EMP_TITLE VARCHAR(16777216),\r\n\r\n    EMP_LENGTH VARCHAR(16777216),\r\n\r\n    HOME_OWNERSHIP VARCHAR(16777216),\r\n\r\n    ANNUAL_INC NUMBER(38,0),\r\n\r\n    VERIFICATION_STATUS VARCHAR(16777216),\r\n\r\n    PYMNT_PLAN VARCHAR(16777216),\r\n\r\n    URL VARCHAR(16777216),\r\n\r\n    DESCRIPTION VARCHAR(16777216),\r\n\r\n    PURPOSE VARCHAR(16777216),\r\n\r\n    TITLE VARCHAR(16777216),\r\n\r\n    ZIP_SCODE VARCHAR(16777216),\r\n\r\n    ADDR_STATE VARCHAR(16777216),\r\n\r\n    DTI FLOAT,\r\n\r\n    DELINQ_2YRS NUMBER(38,0),\r\n\r\n    EARLIEST_CR_LINE DATE,\r\n\r\n    INQ_LAST_6MON NUMBER(38,0),\r\n\r\n    MNTHS_SINCE_LAST_DELINQ VARCHAR(16777216),\r\n\r\n    MNTHS_SINCE_LAST_RECORD VARCHAR(16777216),\r\n\r\n    OPEN_ACC NUMBER(38,0),\r\n\r\n    PUB_REC NUMBER(38,0),\r\n\r\n    REVOL_BAL NUMBER(38,0),\r\n\r\n    REVOL_UTIL FLOAT,\r\n\r\n    TOTAL_ACC NUMBER(38,0),\r\n\r\n    INITIAL_LIST_STATUS VARCHAR(16777216),\r\n\r\n    MTHS_SINCE_LAST_MAJOR_DEROG VARCHAR(16777216),\r\n\r\n    POLICY_CODE NUMBER(38,0),\r\n\r\n    LOAN_DEFAULT NUMBER(38,0),\r\n\r\n    ISSUE_MONTH NUMBER(2,0),\r\n\r\n    ISSUE_YEAR NUMBER(4,0)\r\n\r\n);\r\n```\r\n\r\n\r\n\r\nNext we will create an external stage to load the lab data into the table. This is done from a public S3 bucket to simplify the workshop. Typically an external stage will be using various secure integrations as described in this [link](https://docs.snowflake.com/en/user-guide/data-load-s3-config.html).\r\n\r\n\r\n\r\n\r\n```\r\nCREATE OR REPLACE STAGE LOAN_DATA\r\n\r\n  url='s3://snowflake-corp-se-workshop/VHOL_Snowflake_Data_Wrangler/V2/data/';\r\n```\r\n\r\n\r\n\r\nWe can now use the COPY command to load the data into Snowflake.\r\n\r\n```\r\nCOPY INTO LOAN_DATA FROM @LOAN_DATA/loan_data.csv\r\n\r\n    FILE_FORMAT = (TYPE = 'CSV' SKIP_HEADER = 1);\r\n```\r\n\r\n\r\n\r\n**This data represents aggregation from various internal systems for lender information and loans.**\r\n\r\nWe can have a quick look and see the various attributes in it.\r\n\r\n\r\n\r\n```\r\nSELECT * FROM LOAN_DATA LIMIT 100;\r\n```\r\n\r\n\r\n### Snowflake Marketplace data\r\n\r\n\r\n\r\nWe can now look at additional data in the Snowflake Marketplace that can be helpful for improving ML models. It may be good to look at employment data in the region when analyzing loan defaults. Let’s look in the Snowflake Marketplace and see what external data is available from the data providers.\r\n\r\n\r\n\r\nClick on the **Home (House symbol)** button on the top left corner to go to the Snowflake UI home menu.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image38.png)\r\n\r\n\r\n\r\nOnce in the Home Menu on the top left hand corner click on your username and then hover over the **Switch Role** menu. Scroll and select the **ML_ROLE** from the list of roles.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image37.png)\r\n\r\n\r\n\r\nThen select **Marketplace**.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image88.png)\r\n\r\n\r\n\r\nOnce in the Marketplace type **Unemployment** in the top **Search Data Marketplace** and hit Enter/Return. This will provide a list of Data Providers with employment data.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image68.png)\r\n\r\n\r\n\r\nClick on the tile with **Knoema - Labor Data Atlas.**\r\n\r\nThis will show what data is available from the listing. We will notice indicators such as employment and unemployment rates.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image41.png)\r\n\r\n\r\n\r\nNext click on the **Get Data** button. This will provide a pop up window in which you can create a database in your account that will provide the data from the data provider.\r\n\r\n**Click on the down arrow next to Options**\r\n\r\nChange the name of the database to **KNOEMA_LABOR_DATA_ATLAS** and then click the **Get** button.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image54.png)\r\n\r\n\r\n\r\nWhen the confirmation is provided click on **Done** \r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image54a.png)\r\n\r\nClick on **Worksheets** on the left hand menu and then select your worksheet you have been using.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image92.png)\r\n\r\nData from the Snowflake Marketplace does not require any additional work and will show up as a database in your account. A further benefit is that the data will automatically update as soon as the data provider does any updates to the data on their account.\r\n\r\n\r\n\r\nLet’s start using the marketplace data. First we will create a view to pivot the data for the different employment metrics to columns for easier consumption.\r\n\r\n\r\n\r\n```\r\nUSE LOANS_V2.PUBLIC;\r\n\r\n\r\nCREATE OR REPLACE VIEW KNOEMA_EMPLOYMENT_DATA AS (\r\n\r\nSELECT *\r\n\r\nFROM (SELECT \"Measure Name\" MeasureName, \"Date\", \"RegionId\" State, AVG(\"Value\") Value FROM \"KNOEMA_LABOR_DATA_ATLAS\".\"LABOR\".\"BLSLA\" WHERE \"RegionId\" is not null and \"Date\" \u003E= '2018-01-01' AND \"Date\" \u003C '2018-12-31' GROUP BY \"RegionId\", \"Measure Name\", \"Date\")\r\n\r\n        PIVOT(AVG(Value) FOR MeasureName IN ('civilian noninstitutional population', 'employment', 'employment-population ratio', 'labor force', 'labor force participation rate', 'unemployment', 'unemployment rate'))\r\n\r\n         AS p (Date, State, civilian_noninstitutional_population, employment, employment_population_ratio, labor_force, labor_force_participation_rate, unemployment, unemployment_rate)\r\n\r\n);\r\n```\r\n\r\n\r\n\r\nWe will now create a new table to join the loan data with the unemployment data using the geography and time periods. This will provide us with unemployment data in the region associated with the specific loan.\r\n\r\n\r\n\r\n```\r\nCREATE OR REPLACE TABLE UNEMPLOYMENT_DATA AS\r\n\r\n        SELECT l.LOAN_ID, e.CIVILIAN_NONINSTITUTIONAL_POPULATION, e.EMPLOYMENT, e.EMPLOYMENT_POPULATION_RATIO, e.LABOR_FORCE,            e.LABOR_FORCE_PARTICIPATION_RATE, e.UNEMPLOYMENT, e.UNEMPLOYMENT_RATE\r\n\r\n        FROM LOAN_DATA l LEFT JOIN KNOEMA_EMPLOYMENT_DATA e\r\n\r\n            on l.ADDR_STATE = right(e.state,2) and l.issue_month = month(e.date) and l.issue_year = year(e.date);\r\n```\r\n\r\n\r\n\r\nWe can quickly look at the metrics by running a simple query.\r\n\r\n\r\n\r\n```\r\nSELECT * FROM UNEMPLOYMENT_DATA LIMIT 100;\r\n```\r\n\r\n\r\n\r\n\r\n### Database for Machine Learning\r\n\r\nThe last step is to create a database that can be used by the data science team. This will allow them full access to the data without impacting any of the other analyst or production teams. Snowflake provides a very unique feature called [Zero Copy Cloning](https://www.youtube.com/watch?v=yQIMmXg7Seg) that will create a new copy of the data by **only making a copy of the metadata of the objects**. This drastically speeds up creation of copies and also drastically reduces the storage space needed for data copies.\r\n\r\nThis feature can be very handy for Machine Learning as it will allow for feature engineering in Snowflake and also the ability to save copies of the data used for the training of ML models for future reference.\r\n\r\n\r\n\r\nIn this lab we will just clone table objects, though complete databases can also be cloned. First we will create a database and schema to clone the tables objects to.\r\n\r\n\r\n\r\n\r\n\r\n```\r\nCREATE OR REPLACE DATABASE ML_LENDER_DATA;\r\n\r\nCREATE OR REPLACE SCHEMA ML_LENDER_DATA.ML_DATA;\r\n\r\nUSE ML_LENDER_DATA.ML_DATA;\r\n```\r\n\r\nNext we will clone the loan_data and unemployment_data tables to the new database.\r\n\r\n\r\n\r\n```\r\nCREATE TABLE LOAN_DATA_ML CLONE LOANS_V2.PUBLIC.LOAN_DATA;\r\n\r\nCREATE TABLE UNEMPLOYMENT_DATA CLONE LOANS_V2.PUBLIC.UNEMPLOYMENT_DATA;\r\n```\r\n\r\n\r\n\r\nWe will also create a table to allow us to write the ML Model predictions back in the future.\r\n\r\n\r\n\r\n```\r\nCREATE OR REPLACE TABLE ML_RESULTS (LABEL NUMBER, PREDICTIONS NUMBER, P_DEFAULT FLOAT);\r\n```\r\n\r\n\r\n\r\nLastly we will get the storage integration information that was created by the CloudFormation template.\r\n\r\n\r\n\r\n```\r\nSHOW INTEGRATIONS;\r\n```\r\n\r\n\r\n\r\nMake sure to note the name of the storage integration that was created as it will be used with Data Wrangler configuration.\r\n\r\n**Note:** We can also get the storage integration name from the CloudFormation stack. Click on the NESTED stack with a name like snowflake-sagemaker-credit-risk-workshop-StorageIntegrationStack and then the Outputs tab in the CloudFormation browser tab.\r\nYou can also find Snowflake Secret ARN in the Output tab.\r\n\r\n\r\n\r\n\r\n\r\n\r\n- - -\r\n\u003C!-- ------------------------ --\u003E\r\n## Data Wrangler - Data Prep & Feature Analysis\r\n\r\n\r\n\r\n#### Let’s go to SageMaker Studio.\r\n\r\nClick on this link ([https://console.aws.amazon.com/sagemaker/home](https://console.aws.amazon.com/sagemaker/home)) and it will open a new browser tab with the SageMaker Console.\r\n\r\nClick on the **Amazon SageMaker - Control panel** menu on the left hand side.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image76.png)\r\n\r\nIn the **Users** you will see Name **sagemaker-user** click on the **Launch App** button then select **Studio** to open SageMaker Studio\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image93.png)\r\n\r\n\r\n\r\nThis will open SageMaker Studio. It may take a minute or two to create the environment.\r\n\r\n\r\n\r\n\r\n\r\n#### Next we can clone the Git repository that includes all the files we need for the lab in SageMaker Studio.\r\n\r\nOn the Left side menu click on the Git repository icon and then the **Clone a Repository** button.\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image40.png)\r\n\r\n\r\n\r\nYou will then copy the Git URL below for the repository in the popup window and then click CLONE\r\n\r\n[https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops](https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops)\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image82.png)\r\n\r\n\r\n\r\nThe Studio environment will then switch to the folder browser.\r\n\r\nNavigate to the **/snowflake-sagemaker-workshops/loan-default/notebooks** folder by double clicking each folder in the path.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image39.png)\r\n\r\n\r\n\r\nOpen the **snowflake-loan-default-workshop-v1.1.ipnyb** notebook by double clicking on it.\r\n\r\nA window will pop up to select the Image and Kernel you want to use. Under **Image** select the **Custom Image** then **snowflake-workshop** and then select **snowflake-workshop-v1**, keep the Kernel as Python 3 and click **Select**.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image31.png)\r\n\r\n\r\n\r\nGive the kernel a little time to startup.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nThe notebook is very well documented with the steps needed for the workshop.\r\n\r\nYou can execute the code sections by selecting them and hitting the **run button** at the top or **shift+return/enter**.\r\n\r\nExecute the first code block to import libraries and set environment variables.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/imagea3.png)\r\n\r\n\r\n\r\nOnce you have run the cell you will note a number in the square brackets next to it.\r\n\r\n\r\n\r\nNext we will provide access to the AWS Secrets Manager. Execute the next code block.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image75.png)\r\n\r\n\r\n\r\n\r\n\r\n#### Data Wrangler\r\n\r\nWe will now create a Data Wrangler flow.\r\n\r\n\r\n\r\nCreate a new Data Wrangler flow by selecting it from the top **File Menu**\r\n\r\n**File \u003E New \u003E Data Wrangler Flow**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image67.png)\r\n\r\n\r\n\r\nThis can take a few minutes to start an instance.\r\n\r\n\r\n\r\n##### Connect to Snowflake and Add Data\r\n\r\nA new tab will open in Studio with untitled.flow\r\n\r\nSelect the **Import** tab at the top left\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image33a.png)\r\n\r\nClick on **Add data source** and Select **Snowflake**\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image33.png)\r\n\r\n\r\n\r\n\r\n\r\nYou can either use the **username and password** that was created in Snowflake earlier **or the AWS Secret** that was created in the CloudFormation template to connect to Snowflake.\r\nYou can use AWS Secret Manager Console ([https://console.aws.amazon.com/secretsmanager/home](https://console.aws.amazon.com/secretsmanager/home) ) to get the secret **ARN**.\r\n\r\n\r\n\r\nUse the Snowflake account name from Step 3 if you don’t use the AWS Secrets Manager\r\n\r\nThe Snowflake Storage Integration name from Step 4 will be used - **SMSNOW_\u003Cregion\u003E_\u003Caccountid\u003E_STORAGE_INTEGRATION\u003C/accountid\u003E\u003C/region\u003E**\r\n\r\n\r\n\r\nProvide a name for the connection.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image59.png)\r\n\r\n\r\n\r\n\r\n\r\nYou can now navigate the Snowflake data by looking at the Snowflake objects on the left hand side or use the SQL window to run queries.\r\n\r\n\r\n\r\nWhen using the SQL window you can set the context of the queries, similar to Snowflake. Select:\r\n\r\n**Data Warehouse - ML_WH**\r\n\r\n**Database - ML_LENDER_DATA**\r\n\r\n**Schema - ML_DATA**\r\n\r\n\r\n\r\nTo see the LOAN_DATA in Data Wrangler execute the following SQL and click **Run**\r\n\r\n```\r\nSELECT * FROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML\r\n```\r\n\r\nYou can see the Query Results with the data.\r\n\r\n\r\n\r\nWe can refine the features by only selecting columns that will likely be good features.\r\n\r\nWe will also Snowflake to generate a repeatable sampling of the table’s data to split the data in a train/test data set.\r\n\r\n\r\n\r\nExecute the following SQL to acquire a filtered list of potential features.\r\n\r\n\r\n\r\n```\r\nSELECT\r\n\r\nLOAN_ID, LOAN_AMNT, FUNDED_AMNT,\r\n\r\nTERM, INT_RATE, INSTALLMENT,\r\n\r\nGRADE, SUB_GRADE, EMP_LENGTH,\r\n\r\nHOME_OWNERSHIP, ANNUAL_INC, VERIFICATION_STATUS,\r\n\r\nPYMNT_PLAN, PURPOSE, ZIP_SCODE,\r\n\r\nDTI, DELINQ_2YRS, EARLIEST_CR_LINE,\r\n\r\nINQ_LAST_6MON, MNTHS_SINCE_LAST_DELINQ,\r\n\r\nMNTHS_SINCE_LAST_RECORD, OPEN_ACC,\r\n\r\nPUB_REC, REVOL_BAL, REVOL_UTIL,\r\n\r\nTOTAL_ACC, INITIAL_LIST_STATUS,\r\n\r\nMTHS_SINCE_LAST_MAJOR_DEROG,\r\n\r\nPOLICY_CODE, LOAN_DEFAULT, ISSUE_MONTH\r\n\r\nFROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML\r\n\r\nSAMPLE BLOCK (80) REPEATABLE(100)\r\n```\r\n\r\nClick **Run** and then click the **Import** button on the top right.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image4.png)\r\n\r\n\r\n\r\nEnter a name for the Dataset - loan_data\r\n\r\nThen click **Add**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image1.png)\r\n\r\n\r\n\r\n\r\n##### Profile your data\r\n\r\nClick on the **Data Flow** tab\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image32a.png)\r\n\r\nProfile the data by Clicking the **+ sign** next to the Data types block\r\n\r\nSelect **Add Analysis**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image32.png)\r\n\r\n\r\n\r\nIn the Analysis select:\r\n\r\nType - Histogram\r\n\r\nX axis - LOAN_DEFAULT\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image60.png)\r\n\r\n\r\n\r\nThen click **Preview** to get an analysis of the skew.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#### Apply Feature Transformations\r\n\r\nNext we will use Data Wrangler to perform some feature transformations.\r\n\r\nIn the Analysis window click **Data flow** at the top.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image64.png)\r\n\r\n\r\n\r\nClick on the **+** and select **Add Transform** by the Data types box.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image42.png)\r\n\r\n\r\n\r\nClick on **+ Add Step** - To add a transform step\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image78.png)\r\n\r\n\r\n\r\nFirst we will fix the **INT_RATE** column from a string with a % sign to a numeric data type.\r\n\r\n- Scroll down under ADD TRANSFORM and click on **Search and edit**\r\n- Select Input Columns as INT_RATE\r\n- Enter % in the Pattern field\r\n- In the **Replacement string** *type space and then delete it* to have % replaced with an empty string\r\n- Click Preview\r\n- Click Add\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image11.png)\r\n\r\n\r\n\r\nTo add additional steps click **+ Add Step** each time\r\n\r\n\r\n\r\n- Next select **Parse column as type**\r\n- Select INT_RATE column\r\n- From: String\r\n- To: Float\r\n- Click Preview\r\n- Click Add\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image21.png)\r\n\r\n\r\n\r\nNext we will address the **VERIFICATION_STATUS** column, which has various string values to indicate boolean values.\r\n\r\n\r\n\r\nSelect **Custom Transform** then make sure **Python(PySpark)** is selected and copy the following Python code in the code box to repalce the existing text\r\n\r\n```\r\nfrom pyspark.sql.functions import udf\r\nfrom pyspark.sql.types import LongType\r\n\r\n\r\ndef categories(status) :\r\n  if not status :\r\n    return None\r\n  elif status == \"not verified\" :\r\n    return 0\r\n  elif status == \"VERIFIED - income\":\r\n    return 1\r\n  elif status == \"VERIFIED - income source\":\r\n    return 1\r\n  else :\r\n    return None\r\nbucket_udf = udf(categories, LongType())\r\n\r\ndf = df.withColumn(\"VERIFIED\", bucket_udf(\"VERIFICATION_STATUS\"))\r\n```\r\n\r\nSelect Preview and then Add\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image3.png)\r\n\r\n\r\n\r\nThis code creates a new column VERIFIED with boolean values.\r\n\r\n\r\n\r\nNow we can drop the original VERIFICATION_STATUS column.\r\n\r\n- Select **Manage columns**\r\n- Transform - select Drop Column\r\n- Column to Drop - select VERIFICATION_STATUS\r\n- Select Preview and then Add\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image57.png)\r\n\r\n\r\n\r\nFinally we will drop the LOAN_ID column using the steps above.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image35.png)\r\n\r\n\r\n\r\nClick on **Data flow**. You should see the five transform steps of your data prep flow.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image47.png)\r\n\r\n\r\n\r\n\r\n\r\n#### Data Validation\r\n\r\nNext we will check for **Target Leakage**.\r\n\r\nTarget leakage occurs when you accidently train a model with features that are not available in production. As a consequence, you end up with a deceptively effective model in development that causes problems in production. You can mitigate production issues by performing target leakage analysis.\r\n\r\n\r\n\r\nClick the **+ sign** next to the 5 Transform Steps and select Add analysis.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image34.png)\r\n\r\n\r\n\r\nIn the Analysis select:\r\n\r\n- Analysis type - **Target Leakage**\r\n- Max Features - 30\r\n- Problem type - Classification\r\n- Target - LOAN_DEFAULT\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image58.png)\r\n\r\n\r\n\r\nSelect **Preview**\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image63.png)\r\n\r\nThe report indicates that there is no target leakage risk. It does detect some potentially redundant features.\r\n\r\n\r\n\r\n**Next we will create a Bias Report**\r\n\r\nOur data does not have any obvious sensitive attributes like gender and race. However, it does contain zip codes. It's possible that we have a flawed dataset with an abnormal number of loan defaults in minority communities. This might not represent the actual distribution. Regardless, this situation could create a model that is biased against minorities resulting in legal risk.\r\n\r\n\r\n\r\nIn the Analysis window select:\r\n\r\n- Analysis Type - **Bias Report**\r\n- SELECT the column your model predicts (target): LOAN_DEFAULT\r\n- Is your predicted column a value or threshold?: Value\r\n- Predicted value(s): 0;1\r\n- SELECT the column to analyze for bias: ZIPS_CODE\r\n- Is your column a value or threshold?: Value\r\n- Column value(s) to analyze for bias: 200xx;207xx;206xx;900xx;100xx;941xx\r\n\r\n\r\n\r\nClick **Check for bias**\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image27.png)\r\n\r\n\r\n\r\nThe report does not reveal any salient data bias issues.\r\n\r\n\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image9.png)\r\n\r\n\r\n\r\n\r\n#### Prototype Model\r\n\r\n**Create a Quick Model Report**\r\n\r\n\r\n\r\nAmazon Data Wrangler provides a Quick Model report which can serve as a prototyping mechanism. The report will sample your dataset, process your flow and generates a Random Forest Model. The report provides model and feature importance scores to help you assess:\r\n\r\n\r\n\r\n- What features are most impactful?\r\n- Does your data have enough predictive signals to produce a practical model?\r\n- Are your changes to your dataset leading to improvements?\r\n\r\n\r\n\r\nNavigate to the Analysis panel from the tail end of your flow—as you did in the previous section.\r\n\r\nConfigure your report:\r\n\r\n- Analysis type: **Quick Model**\r\n- Analysis name: Quick Test\r\n- Label: LOAN_DEFAULT\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image51.png)\r\n\r\n\r\n\r\nIt can take few minutes to generate a report like the following:\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image28.png)\r\n\r\n\r\n\r\nTake note of the feature importance ranking in the bar chart. This gives you an approximation of which features have strong predictive signals.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Iterate, Experiment and Improve\r\n\r\n\r\nWe will now add additional data to your existing flow.\r\n\r\nFirst click **Data flow**\r\n\r\nSELECT the **Import tab** at the top and click on the Snowflake icon.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image66.png)\r\n\r\n\r\n\r\nRun the following query to extract the unemployment rate data that you obtained from the Snowflake Marketplace.\r\n\r\n\r\n\r\n```\r\nSELECT LOAN_ID, UNEMPLOYMENT_RATE\r\n\r\nFROM ML_LENDER_DATA.ML_DATA.UNEMPLOYMENT_DATA\r\n```\r\n\r\n\r\n\r\nClick **Run** and then the **Import button**\r\n\r\nName the dataset unemployment_data\r\n\r\nClick Add\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image84.png)\r\n\r\n\r\n\r\nNext, you're going to merge the two datasets. There are many ways to do this. You could have performed this entirely using Snowflake. In this lab, you'll learn how to perform this merge through Data Wrangler.\r\n\r\nClick back to the **Data flow**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image64.png)\r\n\r\n\r\n\r\nFirst delete the last transformation from the original flow, so that we have **LOAN_ID** available.\r\n\r\nClick on the **last step** and then on the + next to it and select **Delete**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image69.png)\r\n\r\n\r\n\r\nConfirm the Delete\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image10.png)\r\n\r\n\r\n\r\nNext we will merge the data sets using a join operator\r\n\r\nClick on **+** at the end of the original flow and select the **Join** operator.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image8.png)\r\n\r\nSELECT the other flow by clicking on the **Data Types** box on the bottom flow\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image29.png)\r\n\r\nClick on **Configure** - lower left of the window\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image18.png)\r\n\r\n\r\n\r\nSELECT Left Outer as the Join Type.\r\n\r\nSELECT LOAN_ID for both the Left and Right join keys.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image2.png)\r\n\r\n\r\n\r\nClick **Preview**\r\n\r\n\r\n\r\nThen click **Add** in the top right corner\r\n\r\n\r\n\r\nSelect the Join Node and Add Transform\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image26.png)\r\n\r\nDrop the columns, LOAN_ID_0 and LOAN_ID_1 using the same transformation step as before.\r\n\r\nManage Columns \u003E Drop Column \u003E Columns to Drop \u003E Loan_ID_0 and Loan_ID_1\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image12.png)\r\n\r\n\r\n\r\n\r\n\r\n\r\nEvaluate your Dataset Modifications\r\n\r\nCreate a new Quick Model report to assess the impact of your modifications.\r\n\r\n\r\n\r\nThe steps should be similar to the following:\r\n\r\n- Click on the Analysis tab \r\n- Analysis type - Quick Model\r\n- Label - LOAN_DEFAULT\r\n- Preview\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image50.png)\r\n\r\n\r\n\r\nA couple of key takeaways:\r\n\r\n* UNEMPLOYMENT_RATE is ranked as the most important feature.\r\n* The F1 score increased substantially.\r\n\r\nThis tells us that we are likely heading in the right direction. We added a feature that generated notable improvements to the \"quick model\" and the new feature had the greatest impact.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Generate the Dataset and Train your Model\r\n\r\n\r\n\r\n\r\n\r\nWe are now ready to fully train and tune a model. First, we need to generate our datasets by executing the data flow that we've created.\r\n\r\n\r\n\r\n**Export your Data Flow**\r\n\r\nDataWrangler supports multiple ways to export the flow for execution.\r\n\r\nIn this lab, you will select the option that generates a notebook that can be run to execute the flow as a SageMaker Processing job. This is the simplest option.\r\n\r\n\r\n\r\nClick on **Data flow**\r\n\r\nClick on the **+ symbol** next to the last transform step and select **Export to** and select **Amazon S3(via Jupyter Notebook)**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image25.png)\r\n\r\n\r\n\r\n\r\nThis will generate a new notebook tab. Select the new notebook.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image30.png)\r\n\r\n\r\n\r\n- Follow the steps outlined in the generated notebook.\r\n- Run the cells and wait for the processing job to complete.\r\n- COPY the output S3 URI of the processed dataset.\r\n\r\n\r\n\r\nThis can take a few minutes\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image70.png)\r\n\r\n\r\n\r\nThe S3 URI will look similar to: s3://(YOUR_BUCKET)/export-flow-23-23-17-34-6a8a80ec/output/data-wrangler-flow-processing-23-23-17-34-6a8a80ec\r\n\r\n\r\n\r\nCOPY the S3 URI to the PREP_DATA_S3 variable in your **initial workshop notebook**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image73.png)\r\n\r\n\r\n\r\n**TIP:** You can monitor the processing jobs in the SageMaker Console\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image72.png)\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n### Train your Model\r\n\r\n\r\n\r\nYour data can now be used to train a model. We'll be using an AutoML toolkit, [AutoGluon](https://auto.gluon.ai/stable/tutorials/tabular_prediction/index.html).\r\n\r\nMore details are provided in the notebook description.\r\n\r\nExecute the next code cell to set the parameters for the remote training job.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image13.png)\r\n\r\n\r\n\r\nThe following cell will launch the remote training job. This will take a few minutes.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image46.png)\r\n\r\n\r\n\r\nYou can monitor the training job in the SageMaker Console\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image17.png)\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Deploy your Model [Optional]\r\n\r\n\r\n\r\n\r\nYou can serve your predictions in a couple of ways. You could deploy the model as a real-time hosted endpoint on SageMaker and integrate it with Snowflake as an External Function. This will enable you to query your predictions in real-time and minimize data staleness.\r\n\r\n\r\n\r\nAlternatively, you can pre-calculate your predictions as a transient batch process. In the following section, you will use Batch Transform to do just that. When your use case allows you to pre-calculate predictions, Batch Transform is a good option.\r\n\r\n\r\n\r\nIn the following sections we are going to deploy our model as a batch inference pipeline. The pipeline is designed to consume data from Snowflake, process it using our DataWrangler flow and then pre-calculate predictions using our trained model and Batch Transform.\r\n\r\n\r\n\r\n**Modify your Data Preparation flow for Inference**\r\n\r\n\r\n\r\nFirst we will make a copy of our flow file.\r\n\r\n- Right click on the **untitled.flow** file and select **Duplicate**\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image71.png)\r\n\r\n- Right click on the duplicate copy **untitled-Copy1.flow** and select Rename\r\n- Use **inference_flow_loan.flow** as the new name\r\n\r\n\r\n**TIP:** Click on the Folder icon on the left hand side of the screen to see the files\r\n\r\n\r\n\r\nSet the INFERENCE_FLOW_NAME to the new flow file in your workshop notebook. By running the cell shown below. \r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image62.png)\r\n\r\n\r\n\r\n**Next we will open the new flow file by double clicking on it**.\r\n\r\nWe can now change the data source for the flow:\r\n\r\n- Click on the **Data Flow** tab at the top\r\n- Click on the + next to the Snowflake:loan_data source and select Edit Query\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image61.png)\r\n- Click on the Snowflake Connection button\r\n- Replace the current SQL query with a new query that will retrieve the 20% sample data that we didn’t use for training.\r\n\r\n```\r\nSELECT\r\n  L1.LOAN_ID,\r\n  L1.LOAN_AMNT,\r\n  L1.FUNDED_AMNT,\r\n  L1.TERM,\r\n  L1.INT_RATE,\r\n  L1.INSTALLMENT,\r\n  L1.GRADE,\r\n  L1.SUB_GRADE,\r\n  L1.EMP_LENGTH,\r\n  L1.HOME_OWNERSHIP,\r\n  L1.ANNUAL_INC,\r\n  L1.VERIFICATION_STATUS,\r\n  L1.PYMNT_PLAN,\r\n  L1.PURPOSE,\r\n  L1.ZIP_SCODE,\r\n  L1.DTI,\r\n  L1.DELINQ_2YRS,\r\n  L1.EARLIEST_CR_LINE,\r\n  L1.INQ_LAST_6MON,\r\n  L1.MNTHS_SINCE_LAST_DELINQ,\r\n  L1.MNTHS_SINCE_LAST_RECORD,\r\n  L1.OPEN_ACC,\r\n  L1.PUB_REC,\r\n  L1.REVOL_BAL,\r\n  L1.REVOL_UTIL,\r\n  L1.TOTAL_ACC,\r\n  L1.LOAN_DEFAULT,\r\n  L1.ISSUE_MONTH\r\nFROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML AS L1\r\n LEFT OUTER JOIN\r\n (SELECT * FROM ML_LENDER_DATA.ML_DATA.LOAN_DATA_ML sample block (80) REPEATABLE(100)) AS L2\r\n ON L1.LOAN_ID = L2.LOAN_ID\r\nWHERE L2.LOAN_ID IS NULL\r\n```\r\n\r\n- Click **Run** and then the **Apply** button on the top right\r\n- Name the dataset loan_inference and click the **Add** button\r\n\r\n\r\nThe purpose of this flow is for use in production to prep data from new loan applications. This data is then passed through your model to make predications about the default risks of these new applicants.\r\n\r\nFor testing purposes, we're using the 20% of the data set we didn't use for training and we're including the LOAN_DEFAULT attribute so that we can evaluate our models and perform error analysis. We're going to move the the LOAN_DEFAULT column to the first position in our dataset, so that it's easier for us to filter this data out of the model input and merge it with the model's predictions.\r\n\r\n- Select **Add transform** at the end of your data flow.\r\n- Click on the orange button labeled **Add step**\r\n- Click on the **\"Manage columns\"** category.\r\n- Select **Move column** under the **Transform** dialog.\r\n- Select **Move to start** under the **Move type** dialog.\r\n- Select **LOAN_DEFAULT** under the **Column to move** dialog.\r\n- Click on the **Preview** button to preview the changes and finalize the transformation by clicking on the **Add** button.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image94.png)\r\n\r\n#### Re-export and re-factor your flow as a Pipeline\r\n\r\nGo back to the **Data Flow** by clicking on the top Data Flow link\r\n\r\nAs previously select the **+** next to the last step in the flow.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image43.png)\r\n\r\n\r\n\r\nThen go to **Export to** and select **SageMaker Pipelines (via Jupyter Notebook)**\r\n\r\n\r\nThis will generate a new notebook - **inference_flow_loan.ipynb**\r\n\r\n\r\n\r\nScroll down in the notebook till you find the cell with output_name in it.\r\n\r\nCOPY the node ID form this cell.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image45.png)\r\n\r\nIn practice, you will need to refactor the exported script. This has been done for you, so all you need to do is locate the export node-id. Each step in your data flow is a unique node and the export script is dependent on the node that you select for export.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#### Go back to your workshop notebook - snowflake-loan-default-workshop-v1.1.ipynb\r\n\r\n\r\n\r\nCOPY the node ID to the FLOW_NODE_ID cell and run the cell\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image19.png)\r\n\r\n\r\n\r\nYou can run the next cell if you like to see the refactored script.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image81.png)\r\n\r\n\r\n\r\nNext you will run your batch scoring pipeline by executing the next cell.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image85.png)\r\n\r\nThis will process the data prep steps and then run batch inference using the model that was previously trained. These steps will take some time to complete.\r\n\r\n\r\n\r\nYou can monitor the pipeline in SageMaker Studio.\r\n\r\n- SELECT the SageMaker resources icon on the left hand menu bar\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image52.png)\r\n- SELECT Pipelines from the drop down menu\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image53.png)\r\n- Right click on your pipeline and select **Open pipeline details**\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image15.png)\r\n- This will open a pipeline tab. Right click on the status of the pipeline and select **Open execution details**\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image48.png)\r\n- This opens a tab with the pipeline steps. You can click on each step to get more information.\r\n- ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image7.png)\r\n\r\n**TIP:** Click the small refresh icon at the top left to refresh your few from time to time\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Evaluate Model Performance and Write Back to Snowflake [Optional]\r\n\r\n\r\n\r\nSince the data set is small enough we can load it to a local pandas dataframe and review the results.\r\n\r\n\r\n\r\nThe next cell will load the data set and provide an output of the results.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image83.png)\r\n\r\n\r\n\r\nWe can use some utilities to evaluate how well the model performed using the test data set.\r\n\r\nExecute the next 2 cells\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image65.png)\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image87.png)\r\n\r\nThe last cell will provide an interactive chart to see how well the model performed based on the threshold we set for the prediction.\r\n\r\n\r\n\r\n\r\n\r\n#### Writeback to Snowflake\r\n\r\nTypically for large batch transforms we will use Snowflake’s automated capability to read data from S3 called [Snowpipe](https://docs.snowflake.com/en/user-guide/data-load-snowpipe-intro.html), or alternatively the [COPY](https://docs.snowflake.com/en/sql-reference/sql/copy-into-table.html) command to perform bulk loads. Since this data set is in a dataframe we can use the Python connector to write it directly back to Snowflake.\r\n\r\n\r\n\r\nFirst we will use the AWS Secret we create with the CloudFormation template. Go to the [Secrets Manager Console](https://console.aws.amazon.com/secretsmanager/home). Select the Snowflake Secret and copy the Secret Name i.e. **SnowflakeSecret-P4qyGUyk67hj** in the cell below.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image44.png)\r\n\r\n\r\n\r\nThe next cell creates a function to retrieve AWS Secrets for use in the notebook. Run the cell.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image55.png)\r\n\r\n\r\n\r\nThe next cell will establish a connection with Snowflake using the secret’s information.\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image56.png)\r\n\r\n\r\n\r\nThe last cell will write the dataframe data into a Snowflake table.\r\n\r\n\r\n**TIP:** The same connection can be used to read data from Snowflake as well as issue Snowflake commands to help process data.\r\n\r\n\r\n\r\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/vhol-snowflake-data-wrangler/image24.png)\r\n\r\n\r\nYou can now query the results in your Snowflake account  \r\n\r\n\r\n\r\nCongratulations! You have completed the lab.\r\n\r\n\r\n\r\n\r\n\r\n- - -\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusions & Next Steps\r\n\r\n\r\n\r\nIn this lab we build an example of how you can enrich your internal data with Snowflake Marketplace data to improve the performance of your Machine Learning Models. We also covered how you can integrate Data Wrangler with Snowflake to gain access to the data and drive pipelines for your ML models.\r\n\r\n\r\n\r\nAdditionally we covered how you can use SageMaker Studio and deploy CloudFormation Templates to create prebuild kernels with the Snowflake Python Connector. Also how to deploy the Snowflake Storage Integrations with a CloudFormation template and using AWS Secrets Manager to provide more secure connections with Snowflake.\r\n\r\n\r\n\r\n\r\n\r\nRelated Resources\r\n\r\n\r\n- [Data Centric AI with Snowflake and Amazon SageMaker](https://aws.amazon.com/blogs/apn/enabling-data-centric-artificial-intelligence-through-snowflake-and-amazon-sagemaker/)\r\n- [Snowflake Marketplace](https://docs.snowflake.com/en/user-guide/data-marketplace.html)\r\n- [SageMaker Data Wrangler](https://aws.amazon.com/sagemaker/data-wrangler/)\r\n- [SageMaker Studio](https://aws.amazon.com/sagemaker/studio/)\r\n- [Fork GitHub Repo](https://github.com/dylan-tong-aws/snowflake-sagemaker-workshops?_fsi=TNKw8Mx5&_fsi=TNKw8Mx5)\r\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/04/Student-Loan-Default-Prediction-using-Snowpark-ML-and-Amazon-SageMaker.pdf)\r\n- [Read the Blog](https://medium.com/snowflake/integrating-machine-learning-models-with-snowpark-ml-a-guide-for-azureml-and-sagemaker-users-735292843a7b)\r\n- [Watch the Demo](https://youtu.be/m0eXjwiHU8E?list=TLGGY7poE1orVScyNDA5MjAyNQ)",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-9799bb3dba","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-0a347662d0",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3b4055ba4e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-07-08",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-0a831fc93b","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-eac7ed7c27",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-d15d59d702",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-6a54d9ed5e","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-6e89077b24",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetting up Snowflake\u003C/h2\u003E","\u003Ch2\u003EConfigure Amazon SageMaker Studio and Snowflake Storage Integration with AWS\u003C/h2\u003E","\u003Ch2\u003ELoad data in Snowflake and access the Marketplace\u003C/h2\u003E","\u003Ch2\u003EData Wrangler - Data Prep & Feature Analysis\u003C/h2\u003E","\u003Ch2\u003EIterate, Experiment and Improve\u003C/h2\u003E","\u003Ch2\u003EGenerate the Dataset and Train your Model\u003C/h2\u003E","\u003Ch2\u003EDeploy your Model [Optional]\u003C/h2\u003E","\u003Ch2\u003EEvaluate Model Performance and Write Back to Snowflake [Optional]\u003C/h2\u003E","\u003Ch2\u003EConclusions & Next Steps\u003C/h2\u003E"],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/vhol-snowflake-data-wrangler"},"quickstart_button":{"id":"quickstart-button-34131d18c6",":type":"snowflake-site/components/quickstart/quickstart-button","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/vhol-snowflake-data-wrangler","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-4b74bef89e","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}}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["quickstart_hero","flexible_column_cont","markup_editor"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"layout":"SIMPLE","id":"container-bf91f90575",":type":"snowflake-site/components/modal/modal-container",":items":{},":itemsOrder":[]},"experiencefragment-footer":{"id":"experiencefragment-c84d0173f6","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-784d90480f",":type":"snowflake-site/components/container",":items":{"container_copy":{"additionalClasses":"sf-footer__inner","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-58b6771369",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-0151d41b12","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-1ad1bd3590",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-928006528c",":type":"snowflake-site/components/container",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-ef3d4efe87",":type":"snowflake-site/components/container",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-1d90102dd3",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-8f372498d0","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-c1ae34a3e9","marketoForm":{"formId":"45871","successUrl":null,"edit":false,"hidden":null,"script":null,"values":null},"marketoConfigured":true,"formConfigured":true,"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com",":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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-0aff08406b",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-21ffdcfd94","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-85ef8a6179","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-56e83959fa",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-53ec852604","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":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-a2246575b0",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-18e557167e","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_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-a3fcf74e4e",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-5b47063eb3","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"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small"},"container_573483281_":{"additionalClasses":"sf-footer__bottom","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-1e8f720ef9",":type":"snowflake-site/components/container",":items":{"container_112062425":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-3899faa05f",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-9f9f860f10","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-c8ef692404",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer__legal-container","gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","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"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-60ea6beb61",":type":"snowflake-site/components/container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-cff4084b6b",":type":"snowflake-site/components/container",":items":{"image":{"id":"image-3a766ebdb3","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,":type":"snowflake-site/components/image"}},":itemsOrder":["image"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"text_copy_copy_16360":{"id":"text-0760f9f894","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-2562fd5d11","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","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["container","text_copy_copy_16360","markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none"}},":itemsOrder":["container"]},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":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-0534c9d436","title":"New css","cssContent":".snowflake-image-container img{background-color:transparent}div.snowflake-person-chip-avatar{width:80px !important}#snowflake-blog-template-main-container .aem-GridColumn:has(.vertical-video){background-color:#000;border-radius:16px;overflow:hidden}#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}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["container_copy","container_573483281_","markup_editor_copy"]}},":itemsOrder":["root"],"classNames":"aem-xf"},"markup_editor":{"id":"markup-editor-06f00cb2c9","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}}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","markup_editor_1950346551","responsivegrid","modal_container","experiencefragment-footer","markup_editor"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],"locale":"en"}