{"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"templateName":"quickstart-page-template","canonicalLink":"https://www.snowflake.com/en/developers/guides/comprehensive-guide-to-data-quality-monitoring/","robotsTags":[],"cssClassNames":"page basicpage summit-page","language":"en","title":"Comprehensive Guide to Snowflake Data Quality Monitoring","analyticsPageType":"developer","analyticsCategory":"developers","analyticsSubCategory":"guides","excludeFromAnalytics":false,":path":"/content/snowflake-site/global/en/developers/guides/comprehensive-guide-to-data-quality-monitoring","isPasswordProtected":false,"analyticsEnabled":true,"coveoConfig":{"organizationId":"snowflakecomputingproduction8neljofn","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","pipeline":"snowflake.com","searchHub":"snowflake.com"},"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"guides","pageType":"developer","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/en/developers/guides/comprehensive-guide-to-data-quality-monitoring/","language":"en","category":"developers","pageName":"Comprehensive Guide to Snowflake Data Quality Monitoring","contentTags":["snowflake-site:taxonomy/product/data-engineering","snowflake-site:taxonomy/snowflake-feature/transformation"]},"publicationThemeClass":"","analyticsContentTags":["snowflake-site:taxonomy/product/data-engineering","snowflake-site:taxonomy/snowflake-feature/transformation"],":type":"snowflake-site/components/structure/page",":items":{"root":{"columnClassNames":{"markup_editor_1950346551":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","modal_container":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"experiencefragment-banner":{"id":"experiencefragment-8e81c5b5f0","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"pushdown_banner_copy_1875701434":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-b2cc535682",":type":"snowflake-site/components/container",":items":{"pushdown_banner_copy_1875701434":{"id":"pushdown-banner-7153a7eed1","contentHeadline":"Expedition. Free, on-demand.","contentDescription":"Technical tracks for practitioners, outcomes for leaders.","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"/en/expedition/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Watch now"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy_1875701434"]},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"],"classNames":"aem-xf"},"experiencefragment-header":{"id":"experiencefragment-2891057972","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-15fa351f0e",":type":"snowflake-site/components/container",":items":{"markup_editor":{"id":"markup-editor-4a8e1129a9","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-a304ec2178",":type":"snowflake-site/components/mega-header",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-5b16ade5af",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-ff689b439a","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-b2320efd27",":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-d4ccb4654d",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-6ddc1d289f","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"The Snowflake Platform"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-7f8ee4905b","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake CoWork"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_836345186":{"id":"nav-item-6b697e36a4","additionalClasses":"blue-icon is-analytics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/analytics/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Analytics"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2":{"id":"nav-item-a71f91eaf4","additionalClasses":"blue-icon is-ai","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1314771042":{"id":"nav-item-0cfc27edfb","additionalClasses":"blue-icon is-data-eng","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/data-engineering/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Data Engineering"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634":{"id":"nav-item-3af6b53619","additionalClasses":"blue-icon is-apps-collab","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/applications-and-collaboration/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Applications & Collaboration"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_2013333117":{"id":"nav-item-2b0dba49aa","additionalClasses":"blue-icon is-transactions","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/transactions/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","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-cb20539ba7",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_212715":{"id":"nav-item-5de89e74c5","additionalClasses":"is-cortex-code","linkDescription":"Snowflake-native AI coding agent ","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-coco/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake CoCo"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c":{"id":"nav-item-3a72a22c1a","additionalClasses":"is-observe","linkDescription":"AI-powered observability for faster production troubleshooting","flag":"New","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/observe/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Observe"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-8f56d1096b","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Cortex AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590635":{"id":"nav-item-9c4cbc1d43","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Marketplace"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-16b5d9dca8","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Snowpark"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_1664056128":{"id":"nav-item-f12d31390b","additionalClasses":"is-snowpark","linkDescription":"Compute that right-sizes itself for every workload","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/adaptive-compute/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Adaptive Compute"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_212715","nav_item_511717659_c","nav_item","nav_item_copy_660590635","nav_item_copy_660590","nav_item_copy_660590_1664056128"]},"nav_column_692142673":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-dacb799cfc",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_660590_1739526127":{"id":"nav-item-c64d0debae","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Postgres"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_2011265650":{"id":"nav-item-47061d1acb","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Streamlit"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-aaa5f38543","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake ML"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_212715":{"id":"nav-item-044bcb8d5c","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Openflow"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-c2dd62d18c","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Notebooks"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_660590_1739526127","nav_item_copy_660590_2011265650","nav_item_copy_185565","nav_item_copy_212715","nav_item_copy_660590"]},"nav_column_782221091":{"navColumnTitle":" ","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-55b39d32b4",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_258535199":{"id":"nav-item-fd711ec8de","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Native Apps"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-41a44b374a","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Horizon Catalog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_1293798742":{"id":"nav-item-bca64f1c85","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Horizon Context"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c":{"id":"nav-item-0142016897","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Unistore"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1006104884":{"id":"nav-item-88064aac4d","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Interoperable Lakehouse"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_258535199","nav_item_copy","nav_item_copy_660590_1293798742","nav_item_511717659_c","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-af39ed6f8a","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-4a1b06c551",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"navColumnTitle":"INDUSTRIES","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-08f12b8604",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_361384_2056203141":{"id":"nav-item-d804d7f1d7","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"All Industries"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-6ff64b5462","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/advertising-media-entertainment/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Advertising, Media & Entertainment"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-ac1de43357","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/financial-services/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Financial Services"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-fd68b33d7c","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/healthcare-and-life-sciences/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Healthcare & Life Sciences"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1533429516":{"id":"nav-item-9aba685bc4","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/manufacturing/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Manufacturing"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1444458226":{"id":"nav-item-013d47185e","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/public-sector/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Public Sector"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1149488919":{"id":"nav-item-ef618377f0","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Retail & Consumer Goods"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_57417040":{"id":"nav-item-5b9a46aa32","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/technology/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Technology"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384674":{"id":"nav-item-57c85ce90d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/telecom/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Telecom"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384":{"id":"nav-item-f02ba559f4","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/travel-hospitality/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","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-bb94bb6bd6",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-51ce7952a5","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/departments/finance/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Finance"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-11bf45d396","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/marketing/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Marketing"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_197051":{"id":"nav-item-2a190ec5e1","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/departments/sales/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Sales"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-cc3b9edeae","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/departments/information-technology/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"IT"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1970515619","nav_item_copy_197051","nav_item_copy"]},"nav_column_833417450":{"navColumnTitle":"Enablement Solutions","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-1e2894d47d",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_107772":{"id":"nav-item-3b44bb2e38","linkDescription":"Confident migration to a unified platform","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/migrate-to-the-cloud/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Migrate to the AI Data Cloud"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Cloud icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-1c6b903b1e","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Services Delivery"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Migrate icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_107772","nav_item_copy_copy"]},"nav_column_copy_copy":{"navColumnTitle":"PARTNER SOLUTIONS","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-074fb22817",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-48a5516186","linkDescription":"Programs with product, solutions and cloud partners","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake Partner Network"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Partner Network icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-2cdf5522c4","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Partner Finder"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Partner Finder icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-83bd67ccfe","linkDescription":"Live and virtual events","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/event-partnership-opportunities/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Event Partnership Opportunities"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Calendar icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-d2ca06dbd8","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-e176235011",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-b2db6be813",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-8c25b62700","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","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-6d5c5e3d5c",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-8d653e1f41","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Customers"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Customer icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-85f0d4fb75","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"The AI Data Cloud Explained"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Cloud icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-489395c07d","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Security Hub"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"User with security lock icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-ca0ed0f041","additionalClasses":"is-light-gray-icon","linkDescription":"Reduce compute costs across queries, warehouses and storage","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/pricing-options/cost-and-performance-optimization/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Cost Optimization"},"icon":{"id":"icon","isLcpImage":false,"src":"/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/1791996291063/nav-icon-cost-optimization-performance.svg","lazyEnabled":true,"alt":"Cost Optimization icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_903555964":{"id":"nav-item-37bd82f061","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake for Startups"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Launch","height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-5892956860","additionalClasses":"is-light-gray-icon","linkDescription":"Keep queries fast as workloads grow with automatic tuning ","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/workload-optimization/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Workload Optimization"},"icon":{"id":"icon","isLcpImage":false,"src":"/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_copy/icon.coreimg.svg/1791996347807/management-optimizer.svg","lazyEnabled":true,"alt":"Management Optimizer","height":"64","width":"64",":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","nav_item_copy_copy"]}},":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-e361fd54ea","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-0941830f93",":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-8d884b7a4a",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-52c2ffd6c3","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Blog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_180298689":{"id":"nav-item-ca310edca3","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/events/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Events"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_1639361946":{"id":"nav-item-39dcfd4714","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/support/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Support"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_680912746":{"id":"nav-item-18979d984a","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/contact/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","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-d26269c0e9",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-b9a733353c","linkDescription":"Ebooks, videos, white papers and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Resource Library"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Notebooks icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-e3a17f4b1c","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Training"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Training icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-68f92ffcc6","linkDescription":"Expert-led discussions and demos across industries and use cases","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Webinars"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Webinars icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-c7ca9affb8","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Certifications"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Certification icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-2586f838de","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Live Demos"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Live Demo icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-4db69389d8","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Snowflake University"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Education icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-df03d2635b","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Hands-On Labs"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Hands-on Labs icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890":{"id":"nav-item-84f2e90f21","linkDescription":"Academic papers written by Snowflake researchers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/publications/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake Research Publications"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Copy","height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890_930852828":{"id":"nav-item-4ce57b1e2a","linkDescription":"Informative articles about AI and data topics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/fundamentals/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Fundamentals"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Document with list","height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item","nav_item_copy_144634_1984107859","nav_item_copy_1438098918","nav_item_copy_143809","nav_item_copy_333890638","nav_item_copy_189945","nav_item_copy_333890","nav_item_copy_333890_930852828"]}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"]},"nav_promo_section":{"id":"nav-promo-section-776222e651","experience_fragment_1":{"id":"experiencefragment-f7286fc977","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master1/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-0e80bfca03",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-a780be161e","openInNewWindow":true,"layout":"horizontal","headline":"The Modern Marketing Data Stack 5th Edition","description":"AI agents are changing the marketing stack. See how to govern the shift. ","linkTitle":"Learn more","linkUrl":"/en/the-modern-marketing-data-stack-report/","image":{"id":"image","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--b3030d24-fd50-45e6-bfe6-9520d3eb46d8/web-inside-the-mmds-5th-960x540.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"MMDS report 5th edition","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-c2a7f6c94b","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-2/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-62445f742a",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-f4b20315b0","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","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"roi of gen ai and agents","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-ba85cb7361","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-3/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-c7746b50d1",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-b63d1d8cdc","openInNewWindow":true,"layout":"horizontal","headline":"EXPEDITION 2026 | ON-DEMAND","description":"Embark on your agentic transformation with deep technical workshops and curated executive sessions.","linkTitle":"Watch now","linkUrl":"/en/expedition/","image":{"id":"image","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--b02be612-1a04-464a-bc67-97ff0446e009/expedition-card-thumb.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"expedition","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-e935e7cb81","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-db5981b574",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy_copy":{"navColumnTitle":"Build","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-73d7c7a261",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-5ab471fe43","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake for Developers"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Developers icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-c453b2e1fc","linkDescription":"Reference architectures, use cases and best practices","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/guides/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Developer Guides"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Solution Center icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-8e325100b9","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Downloads"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Download icon","height":"28","width":"28",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246","nav_item_copy"]},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Learn","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-dd639a06dc",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-8b78a02ab7","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Documentation"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Docs icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-6ab658833d","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Open Source"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Open Source icon","height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-752b6af92a","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Builder Education"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Northstar logo","height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_copy"]},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-d4011b9ee1",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-349025af3b","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Engineering Blog"},"icon":{"id":"icon","isLcpImage":false,"src":"/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/1789759077904/software-engineers.svg","lazyEnabled":true,"alt":"software engineers icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_892639794":{"id":"nav-item-410cdfacda","linkDescription":"Ideas, solutions and tools for developers of data-intensive applications","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/blog/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Developer Blog"},"icon":{"id":"icon","isLcpImage":false,"src":"/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_892639794/icon.coreimg.svg/1789759047695/nav-icon--developer-center.svg","lazyEnabled":true,"alt":"developer icon","height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-8650d4eeed","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",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Community"},"icon":{"id":"icon","isLcpImage":false,"src":"/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","lazyEnabled":true,"alt":"Partner Network icon","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_892639794","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-66913f9727","experience_fragment_1":{"id":"experiencefragment-8cf2447ce7","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-5/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-234ccad1ef",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-01edc36d82","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","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--dc7e334a-c38b-4283-b1de-fcf829952eef/nav-promo-first-notebook.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"alt","height":"210","width":"415",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{":type":"cq:LiveCopy","cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf"},"experience_fragment_2":{"id":"experiencefragment-3d2880b768","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-1664958a4d",":type":"snowflake-site/components/container",":items":{"nav_promo_card":{"id":"nav-promo-card-6f44e1ce6b","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","isLcpImage":false,"src":"/adobe/dynamicmedia/deliver/dm-aid--14341ced-bc5e-4a29-9762-b7857f6cadfc/nav-promo-northstar.jpg?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Snowflake Northstar logo","height":"700","width":"1440",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"]},"cq:LiveSyncConfig":{":type":"cq:LiveCopy","cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master"}},":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-708f9b780d","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-a7fb563fc9","languageNavItems":[{"title":"English","path":"/en/developers/guides/comprehensive-guide-to-data-quality-monitoring/","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-54d457d9aa","heapButtonClasses":["mega-nav__sign-in"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://app.snowflake.com/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","appliedCssClassNames":"snowflake-button-link snowflake-button-black snowflake-button-compact","text":"Sign in"},"button":{"id":"button-d5527f6f25","heapButtonClasses":["contact_nav","heap-nav-contact"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"/en/contact-sales/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","appliedCssClassNames":"snowflake-button-secondary snowflake-button-blue snowflake-button-compact","text":"CONTACT SALES"},"button_288358396":{"id":"button-b6cd738bc7","heapButtonClasses":["start_for_free_nav","heap-nav-start-for-free"],"showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://signup.snowflake.com/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","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":{":type":"nt:unstructured","cq:targetStatus":"OUT_OF_SYNC","cq:exportTime":1793653056655,"cq:targetOfferId":860250}},":itemsOrder":["root","image","cq:targetMetadata"],"classNames":"aem-xf"},"markup_editor_1950346551":{"id":"markup-editor-a5f6c05886","title":" ","cssUrl":"/conf/snowflake-site/settings/wcm/templates/quickstart-page-template/structure/_jcr_content/root/markup_editor_1950346551.e08bb1bc93839db7.css","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-e7ec8367d0","isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Comprehensive Guide to Snowflake Data Quality Monitoring"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Yoav Ostrinsky","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Transformation","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/transformation","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-ce58910b76","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/comprehensive-guide-to-data-quality-monitoring"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Comprehensive Guide to Snowflake Data Quality Monitoring","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/comprehensive-guide-to-data-quality-monitoring","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}],"fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/comprehensive-guide-to-data-quality-monitoring",":type":"snowflake-site/components/quickstart/quickstart-hero"},"flexible_column_cont":{"id":"flexible-column-container-629a0bd816","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-a82a80c7f5",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-91096161e9","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EData quality problems rarely announce themselves. A device clock drifts and stamps readings two days into the future. An upstream system swaps a missing battery level for -1. A firmware release emits truncated JSON. Nothing errors, and the dashboards keep rendering, on data that is quietly wrong.\u003C/p\u003E\n","\u003Cp\u003ESnowflake can check for these conditions natively, on a schedule, without moving data or standing up another tool. This guide explores what that makes possible.\u003C/p\u003E\n","\u003Ch3\u003EHow to use this guide\u003C/h3\u003E\n","\u003Cp\u003EStart with \u003Cem\u003EThe Example Data\u003C/em\u003E, which creates everything the examples use. Each later section explores \u003Cstrong\u003Eone capability\u003C/strong\u003E and adds checks to the same table, so the sections are meant to be run in order; where a section relies on a check from an earlier one, it says so. Where something can be done both in SQL and in Snowsight, the section shows both. Profiling, AI-suggested checks, and the monitoring dashboard are Snowsight-only; everything else is shown in SQL.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow a check is assembled: metric, association, schedule, expectation, results\u003C/li\u003E\u003Cli\u003EWhich system data metric function to reach for, given a column's shape\u003C/li\u003E\u003Cli\u003ECompleteness, validity, timeliness, integrity, volume, drift and distribution checks\u003C/li\u003E\u003Cli\u003EHow to scope a check with FILTER or compute it per group with WITHIN GROUP\u003C/li\u003E\u003Cli\u003EHow to write a custom metric, cover a whole schema, and let Snowflake suggest checks and learn normal ranges\u003C/li\u003E\u003Cli\u003EHow to find the offending rows, get notified natively or through an alert created from a template\u003C/li\u003E\u003Cli\u003EWhere to place checks, how to keep them in code, and what monitoring costs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA set of quality checks on a small IoT sensor telemetry dataset: system and custom metrics, expectations, scoped and grouped checks, schema-level coverage, anomaly detection, and notifications that fire when a check fails.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA Snowflake account on \u003Cstrong\u003EEnterprise Edition or higher\u003C/strong\u003E. Data Quality Monitoring is not available on Standard Edition or on \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-intro#label-data-quality-limitations\"\u003Etrial accounts\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EA role that can create a database and a warehouse, and that holds:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEXECUTE DATA METRIC FUNCTION on the account, to attach and schedule metrics\u003C/li\u003E\u003Cli\u003EMANAGE DATA QUALITY on the account, for schema-level checks and notifications\u003C/li\u003E\u003Cli\u003EEXECUTE ALERT on the account, for the template alert in \u003Cem\u003EGet Told, Don't Watch\u003C/em\u003E\u003C/li\u003E\u003Cli\u003ECREATE INTEGRATION on the account, for \u003Cem\u003EGet Told, Don't Watch\u003C/em\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EACCOUNTADMIN holds all of these. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-access-control\"\u003EAccess control for data quality\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EFamiliarity with running SQL in a Snowsight worksheet.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EUse Cortex Code with this guide\u003C/h3\u003E\n","\u003Cp\u003EWith Cortex Code, the /data-quality skill can apply these workflows to your own tables: recommending metrics, scoring schema health, and investigating violations.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EThe Example Data\u003C/h2\u003E\n","\u003Cp\u003EThe examples use a small IoT dataset. Refrigerated sites report temperature and humidity from battery-powered sensors; readings land in a raw table keyed to a device inventory, with \u003Cstrong\u003Edeliberate defects\u003C/strong\u003E for the checks to find. Timestamps are relative to the current time.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE DATABASE IF NOT EXISTS DQ_EXAMPLES;\nUSE DATABASE DQ_EXAMPLES;\nUSE SCHEMA PUBLIC;\n\nCREATE WAREHOUSE IF NOT EXISTS DQ_EXAMPLES_WH\n    WAREHOUSE_SIZE = XSMALL\n    AUTO_SUSPEND = 60\n    INITIALLY_SUSPENDED = TRUE;\nUSE WAREHOUSE DQ_EXAMPLES_WH;\n\nCREATE OR REPLACE TABLE SITES (\n    SITE_ID   VARCHAR(10),\n    SITE_NAME VARCHAR(100),\n    REGION    VARCHAR(50)\n);\n\nCREATE OR REPLACE TABLE DEVICES (\n    DEVICE_ID    VARCHAR(20),\n    SITE_ID      VARCHAR(10),\n    MODEL        VARCHAR(50),\n    INSTALLED_ON DATE,\n    LABEL        VARCHAR(100)\n);\n\nCREATE OR REPLACE TABLE SENSOR_READINGS (\n    READING_ID    NUMBER(10,0),\n    DEVICE_ID     VARCHAR(20),\n    READING_TS    TIMESTAMP_LTZ,\n    INGESTED_AT   TIMESTAMP_LTZ,\n    TEMPERATURE_C NUMBER(5,2),\n    HUMIDITY_PCT  NUMBER(5,2),\n    BATTERY_PCT   NUMBER(5,2),\n    PAYLOAD       VARCHAR(500),\n    STATUS_CODE   VARCHAR(20)\n);\n\nINSERT INTO SITES (SITE_ID, SITE_NAME, REGION) VALUES\n    ('SITE-01', 'Rotterdam Cold Store',  'EMEA'),\n    ('SITE-02', 'Dublin Distribution',   'EMEA'),\n    ('SITE-03', 'Singapore Transit Hub', 'APAC');\n\nINSERT INTO DEVICES (DEVICE_ID, SITE_ID, MODEL, INSTALLED_ON, LABEL) VALUES\n    ('DEV-1001', 'SITE-01', 'TH-200',  '2025-03-14', 'Cold aisle A'),\n    ('DEV-1002', 'SITE-01', 'TH-200',  '2025-03-14', 'Cold aisle B'),\n    ('DEV-1003', 'SITE-02', 'TH-350',  '2025-07-02', '  Loading bay 1  '),\n    ('DEV-1004', 'SITE-02', 'TH-350',  '2025-07-02', 'Loading bay 2'),\n    ('DEV-1005', 'SITE-03', 'TH-350X', '2026-01-20', 'Transit chiller');\n\nINSERT INTO SENSOR_READINGS\n    (READING_ID, DEVICE_ID, READING_TS, INGESTED_AT, TEMPERATURE_C, HUMIDITY_PCT, BATTERY_PCT, PAYLOAD, STATUS_CODE)\nSELECT 1001, 'DEV-1001', DATEADD(HOUR, -9, CURRENT_TIMESTAMP()), DATEADD(HOUR, -9, CURRENT_TIMESTAMP()),  3.90, 61.20, 98.00, '{"rssi":-71,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1002, 'DEV-1001', DATEADD(HOUR, -8, CURRENT_TIMESTAMP()), DATEADD(HOUR, -8, CURRENT_TIMESTAMP()),  4.10, 60.80, 97.50, '{"rssi":-70,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1003, 'DEV-1002', DATEADD(HOUR, -8, CURRENT_TIMESTAMP()), DATEADD(HOUR, -8, CURRENT_TIMESTAMP()),  3.40, 63.10, 91.00, '{"rssi":-77,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1004, 'DEV-1002', DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), DATEADD(HOUR, -7, CURRENT_TIMESTAMP()),  3.60, 62.40, 90.50, '{"rssi":-76,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1005, 'DEV-1003', DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), 12.80, 48.90, 76.00, '{"rssi":-65,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1006, 'DEV-1003', DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), 13.10, 47.60, 75.50, '{"rssi":-66,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1007, 'DEV-1004', DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), 11.90, 50.20, 88.00, '{"rssi":-69,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1008, 'DEV-1004', DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), 12.20, 49.70, 87.50, '{"rssi":-68,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1009, 'DEV-1005', DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), -2.40, 55.30, 64.00, '{"rssi":-81,"fw":"2.4.1"}', 'OK'\nUNION ALL SELECT 1010, 'DEV-1005', DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), -2.10, 54.80, 63.50, '{"rssi":-80,"fw":"2.4.1"}', 'OK'\nUNION ALL SELECT 1011, 'DEV-1001', DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), DATEADD(HOUR, -4, CURRENT_TIMESTAMP()),  4.30, 60.10, 96.00, '{"rssi":-72,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1012, 'DEV-1002', DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), DATEADD(HOUR, -3, CURRENT_TIMESTAMP()),  3.80, 61.90, 89.00, '{"rssi":-75,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1013, 'DEV-1003', DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), 13.40, 46.80, 74.00, '{"rssi":-64,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1014, 'DEV-1004', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), 12.50, 49.10, 86.50, '{"rssi":-67,"fw":"2.4.0"}', 'OK'\nUNION ALL SELECT 1015, 'DEV-1005', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), -1.90, 54.20, 62.50, '{"rssi":-79,"fw":"2.4.1"}', 'OK'\nUNION ALL SELECT 1015, 'DEV-1005', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), -1.90, 54.20, 62.50, '{"rssi":-79,"fw":"2.4.1"}', 'OK'\nUNION ALL SELECT 1016, 'DEV-1001', DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), NULL,  59.70, 95.50, '{"rssi":-73,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1017, 'DEV-9999', DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), DATEADD(HOUR, -1, CURRENT_TIMESTAMP()),  5.20, 58.30, 80.00, '{"rssi":-74,"fw":"2.3.1"}', 'OK'\nUNION ALL SELECT 1018, 'DEV-1003', DATEADD(MINUTE, -30, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -30, CURRENT_TIMESTAMP()), 13.00, 47.10, -1.00, '{"rssi":-63,"fw":"2.4.0"}', 'BATT_UNKNOWN'\nUNION ALL SELECT 1019, 'DEV-1004', DATEADD(MINUTE, -20, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -20, CURRENT_TIMESTAMP()), 12.10, 49.40, 86.00, '{"rssi":-67,"fw":', 'OK'\nUNION ALL SELECT 1020, 'DEV-1002', DATEADD(DAY, 2, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -10, CURRENT_TIMESTAMP()),  3.70, 62.00, 88.50, '{"rssi":-75,"fw":"2.3.1"}', 'OK';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat is 21 rows across 5 devices and 3 sites. The timestamp columns are TIMESTAMP_LTZ on purpose: metrics that compare against the evaluation time (FRESHNESS and FUTURE_TIMESTAMP_COUNT) accept DATE, TIMESTAMP_LTZ and TIMESTAMP_TZ, but \u003Cstrong\u003Enot TIMESTAMP_NTZ\u003C/strong\u003E, Snowflake's default timestamp type.\u003C/p\u003E\n","\u003Ch3\u003EThe defects, named openly\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EReading\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EProblem\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe check that finds it\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1016\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETEMPERATURE_C is NULL\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENULL_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1020\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EREADING_TS is two days in the future (device clock skew)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFUTURE_TIMESTAMP_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1019\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EPAYLOAD is truncated and not valid JSON\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EINVALID_JSON_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1018\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBATTERY_PCT is -1, a sentinel for "unknown"; STATUS_CODE is BATT_UNKNOWN\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENEGATIVE_COUNT, ACCEPTED_VALUES\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1015\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAppears twice (duplicated on ingest)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDUPLICATE_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003E1017\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDEVICE_ID is DEV-9999, which is not in DEVICES\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EREFERENTIAL_INTEGRITY_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDEVICES DEV-1003\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ELABEL has leading and trailing spaces\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EUNTRIMMED_STRING_COUNT\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003ETEMPERATURE_C legitimately goes negative: DEV-1005 is a freezer. That is why the negative-value check targets BATTERY_PCT, not temperature.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAnatomy of a Check\u003C/h2\u003E\n","\u003Cp\u003EA check has five parts:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EA \u003Cstrong\u003Edata metric function (DMF)\u003C/strong\u003E measures something and returns a number. It makes no judgment.\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003Eassociation\u003C/strong\u003E attaches the DMF to a table and column.\u003C/li\u003E\u003Cli\u003EA \u003Cstrong\u003Eschedule\u003C/strong\u003E, set on the table, governs how often every attached metric runs.\u003C/li\u003E\u003Cli\u003EAn \u003Cstrong\u003Eexpectation\u003C/strong\u003E turns the number into a verdict. A metric without one is measured but never judged.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EResults\u003C/strong\u003E land in monitoring views you query or read in Snowsight.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThis is the model the rest of the guide builds on. To set a schedule, then attach a metric with an expectation:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT\n    ON (TEMPERATURE_C)\n    EXPECTATION MUST_HAVE_TEMPERATURE (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach metric, table and column combination can have \u003Cstrong\u003Eonly one association\u003C/strong\u003E. Adding the same combination again, even with a different filter or grouping, returns an error that points to MODIFY; to change a check, modify it, or drop and re-add it.\u003C/p\u003E\n","\u003Cp\u003EOn a new table, you can declare checks in CREATE TABLE, after the column list, so the table and its guarantees arrive together. The bindings go inside parentheses, separated by commas; see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-working\"\u003EUse SQL to set up data quality checks\u003C/a\u003E for the full syntax.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE TABLE SENSOR_READINGS_V2 (\n    READING_ID    NUMBER(10,0),\n    TEMPERATURE_C NUMBER(5,2)\n)\nWITH DATA METRIC FUNCTION (\n    SNOWFLAKE.CORE.NULL_COUNT\n        ON (TEMPERATURE_C)\n        EXPECTATION MUST_HAVE_TEMPERATURE (VALUE = 0)\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDirect calls versus attached metrics\u003C/h3\u003E\n","\u003Cp\u003EMetrics that \u003Cstrong\u003Etake a column argument\u003C/strong\u003E can be called directly in a SELECT, immediately and without a schedule. A direct call is not billed as Data Quality Monitoring; it runs on your warehouse like any other query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.NULL_COUNT(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS MISSING_TEMPERATURE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReturns 1. Metrics that \u003Cstrong\u003Etake no column argument\u003C/strong\u003E (ROW_COUNT, SCHEMA_CHANGE_COUNT, REFERENTIAL_INTEGRITY_COUNT, FRESHNESS with no column, multi-column DUPLICATE_COUNT), and ACCEPTED_VALUES, work only once attached.\u003C/p\u003E\n","\u003Ch3\u003EEvaluating expectations now\u003C/h3\u003E\n","\u003Cp\u003EYou can evaluate every expectation on a table without waiting for the schedule:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT METRIC_NAME, EXPECTATION_NAME, EXPECTATION_EXPRESSION, VALUE, EXPECTATION_VIOLATED\nFROM TABLE(SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS('SENSOR_READINGS'));\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMETRIC_NAME\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EEXPECTATION_NAME\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EEXPECTATION_EXPRESSION\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EVALUE\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EEXPECTATION_VIOLATED\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENULL_COUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMUST_HAVE_TEMPERATURE\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EVALUE = 0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETRUE\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThis form returns a result without recording it. SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS_PERSIST_RESULT also writes it to monitoring history, which anything reading DATA_QUALITY_MONITORING_EXPECTATION_STATUS, such as an alert or your own queries, depends on. The Snowsight Monitoring page is different: it reports a check as not run yet until its first scheduled evaluation. Pass it the fully qualified table name: at the time of writing, with a bare name it returns the same result but records nothing, so an alert reading the status finds no violation.\u003C/p\u003E\n","\u003Ch3\u003EIn Snowsight\u003C/h3\u003E\n","\u003Cp\u003EThe same check can be added from the table's \u003Cstrong\u003EData quality\u003C/strong\u003E tab. On a table with no checks yet, choose \u003Cstrong\u003ESet up manually\u003C/strong\u003E, pick a check type such as \u003Cstrong\u003ENulls\u003C/strong\u003E, then a column and the expected value. A check created in Snowsight is visible to SQL and vice versa, but Snowsight names the expectation for you, here EXP__TEMPERATURE_C__NONULLS rather than MUST_HAVE_TEMPERATURE. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-ui-setup\"\u003EUse Snowsight to set up data quality checks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/quality_check_types.png?v=6989d1d4\" alt=\"The quality check types offered when setting up a check in Snowsight\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/add_null_check.png?v=6989d1d4\" alt=\"A Nulls check expecting a count of 0 nulls in TEMPERATURE_C\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EScheduling options\u003C/h3\u003E\n","\u003Cp\u003EThe schedule belongs to the table, not to a metric: every metric attached to a table runs on that table's schedule. There are three kinds. These are alternatives, not steps: the examples in this guide assume the daily '1440 MINUTE' schedule shown above.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Fixed interval: 5, 15, 30, 60, 720, or 1440 minutes\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '60 MINUTE';\n\n-- Cron: here, 08:00 UTC on weekdays\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'USING CRON 0 8 * * MON-FRI UTC';\n\n-- On change: whenever DML modifies the table\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe right interval matches how fast you would act. TRIGGER_ON_CHANGES suits tables loaded in batches; it works on regular, transient, temporary, dynamic, external and Iceberg tables but \u003Cstrong\u003Enot on views\u003C/strong\u003E, and reclustering does not trigger it. Cron runs in the time zone named in the expression, and a run is skipped if the previous one has not finished. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-working\"\u003ESchedule the DMF to run\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo pause evaluations while keeping checks attached, set the schedule to an empty string:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUNSET is a trap: it reverts the table to the \u003Cstrong\u003Edefault hourly\u003C/strong\u003E cadence, which multiplies a daily table's evaluations by twenty-four. The references show the current schedule; a blank SCHEDULE means suspended:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT METRIC_NAME, SCHEDULE, SCHEDULE_STATUS\nFROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME   => 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN => 'table'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you tried the alternatives, restore the daily schedule before continuing:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/monitoring_overview.png?v=6989d1d4\" alt=\"The Monitoring view of SENSOR_READINGS, showing failing checks and the Freshness, Volume and Schema quality dimensions\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EProfile Before You Check\u003C/h2\u003E\n","\u003Cp\u003EBefore writing rules, look at the data. In Snowsight, open \u003Cstrong\u003EDQ_EXAMPLES » PUBLIC » SENSOR_READINGS\u003C/strong\u003E, go to the \u003Cstrong\u003EData quality\u003C/strong\u003E tab and switch to \u003Cstrong\u003EData Profile\u003C/strong\u003E to see per-column statistics. This view is Snowsight-only.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/data_profile.png?v=6989d1d4\" alt=\"The data profile of SENSOR_READINGS, showing one null in TEMPERATURE_C and a BATTERY_PCT minimum of -1\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETwo things stand out: TEMPERATURE_C has a non-zero null share, and BATTERY_PCT has a minimum of -1. The scripted equivalent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT COUNT(*)                                            AS TOTAL_ROWS,\n       COUNT(*) - COUNT(TEMPERATURE_C)                      AS MISSING_TEMPERATURE,\n       MIN(BATTERY_PCT)                                     AS LOWEST_BATTERY,\n       COUNT(DISTINCT DEVICE_ID)                            AS DEVICES_REPORTING,\n       MAX(READING_TS)                                      AS LATEST_READING\nFROM SENSOR_READINGS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EProfiling tells you what is unusual, not what is \u003Cem\u003Ewrong\u003C/em\u003E; that judgment is yours.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EChoosing a Check\u003C/h2\u003E\n","\u003Cp\u003ESnowflake's system metrics live in SNOWFLAKE.CORE: more than 185 of them, grouped into categories. Start from the category that matches the column, then pick a metric:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECategory\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECatches\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EExamples\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECompleteness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMissing or empty values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENULL_COUNT, BLANK_COUNT, EMPTY_STRING_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValidity\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValues that break a format or rule\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EACCEPTED_VALUES, INVALID_JSON_COUNT, FUTURE_TIMESTAMP_COUNT, REFERENTIAL_INTEGRITY_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EConsistency\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFormatting regressions in text\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EUNTRIMMED_STRING_COUNT, CASE_FORMAT_VIOLATION_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EUniqueness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDouble ingestion, broken grain\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDUPLICATE_COUNT, UNIQUE_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFreshness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EStalled pipelines\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFRESHNESS\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EVolume\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETables that stop growing or collapse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EROW_COUNT, TABLE_SIZE_BYTES\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESchema\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EColumns added, dropped, renamed, retyped\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESCHEMA_CHANGE_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EStatistics\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDrift no single row reveals; sentinel values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAVG, STDDEV, APPROX_QUANTILE_50, NEGATIVE_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EOutliers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValues far outside the column's range\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EOUTLIER_IQR_COUNT, EXTREME_OUTLIER_ZSCORE_COUNT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMachine Learning\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EModel predictions drifting from actuals\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EACCURACY, MAPE, R_SQUARED\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EMost metrics come in a _COUNT and a _PERCENT form: _COUNT suits cases where any occurrence is a defect, and _PERCENT cases where proportion matters in a table whose size varies. For the current list, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-system-dmfs\"\u003ESystem data metric functions\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECompleteness and Validity\u003C/h2\u003E\n","\u003Cp\u003EEvery reading should carry a temperature (reading 1016 does not), a parseable payload (1019 does not), and a known status code (1018 does not). The column-argument metrics can be measured directly:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.NULL_PERCENT(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS MISSING_PCT,\n       SNOWFLAKE.CORE.INVALID_JSON_COUNT(SELECT PAYLOAD FROM SENSOR_READINGS) AS BAD_PAYLOADS,\n       SNOWFLAKE.CORE.UNTRIMMED_STRING_COUNT(SELECT LABEL FROM DEVICES)       AS PADDED_LABELS;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EMISSING_PCT\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EBAD_PAYLOADS\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EPADDED_LABELS\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E4.761900\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E1\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E1\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EOne row in twenty-one has no temperature. The count form expresses a rule ("never"); the percentage form, a tolerance ("under 1%"). MUST_HAVE_TEMPERATURE from \u003Cem\u003EAnatomy of a Check\u003C/em\u003E is already the rule for temperature; the payload rule attaches the same way:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.INVALID_JSON_COUNT\n    ON (PAYLOAD)\n    EXPECTATION PAYLOAD_IS_JSON (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDMF\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMissing values\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENULL_COUNT, NULL_PERCENT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECount for a rule, percent for a tolerance\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMalformed JSON\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EINVALID_JSON_COUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETests TRY_PARSE_JSON; NULLs are not counted as invalid\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EText that will fail a numeric cast\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EINVALID_NUMERIC_TYPE_CAST_COUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAttach to staging columns you plan to cast\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESentinel or impossible numbers\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENEGATIVE_COUNT, ZERO_COUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EApply only where the value is genuinely impossible; see \u003Cem\u003EScoping Checks\u003C/em\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EEmpty or padded strings\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBLANK_COUNT, UNTRIMMED_STRING_COUNT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFinds the DEV-1003 label in DEVICES\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EValue outside a known set\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EACCEPTED_VALUES\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ETakes a lambda; must be attached\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EACCEPTED_VALUES is the one pattern that differs. It takes a Boolean lambda and counts the rows that fail it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ACCEPTED_VALUES\n    ON (STATUS_CODE, STATUS_CODE -> STATUS_CODE IN ('OK', 'WARN'))\n    EXPECTATION STATUS_IS_KNOWN (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvaluating now with SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS shows STATUS_IS_KNOWN at 1 (reading 1018) and saves you writing a custom value-in-list metric.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETimeliness\u003C/h2\u003E\n","\u003Cp\u003EFRESHNESS returns the seconds between the newest value in a timestamp column and the moment the check ran:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.FRESHNESS(SELECT READING_TS FROM SENSOR_READINGS) AS STALENESS_SECONDS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOn the example data this returns something like -172700, which is \u003Cstrong\u003Enegative\u003C/strong\u003E. Reading 1020 is stamped two days ahead, so freshness reports data that has not happened yet, and an expectation like VALUE < 3600 would pass while the pipeline was dead. \u003Cstrong\u003EA freshness check alone can be defeated by one bad timestamp.\u003C/strong\u003E Paired with a clock-skew check, it cannot be fooled:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FRESHNESS\n    ON (READING_TS)\n    EXPECTATION READINGS_ARE_RECENT (VALUE < 600);\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FUTURE_TIMESTAMP_COUNT\n    ON (READING_TS)\n    EXPECTATION NO_SKEWED_READINGS (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EApproach\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EIs the \u003Cem\u003Edata\u003C/em\u003E current?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFRESHNESS on a timestamp column\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDid the \u003Cem\u003Epipeline\u003C/em\u003E run?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFRESHNESS with ON (), which measures the last DML against the table\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EIs any row dated in the future?\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFUTURE_TIMESTAMP_COUNT; prefer the count over FUTURE_TIMESTAMP_PERCENT\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBusiness hours, per-source SLAs\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EA custom metric using SNOWFLAKE.CORE.DATA_METRIC_SCHEDULE_TIME(); see \u003Cem\u003EWrite Your Own Check\u003C/em\u003E\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFUTURE_TIMESTAMP_COUNT compares against the evaluation time, so a row that passes today can fail on a rerun of history. That is correct: a backfill dated next Tuesday is wrong on both days.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntegrity and Uniqueness\u003C/h2\u003E\n","\u003Cp\u003EReading 1017 reports device DEV-9999, which is not in DEVICES; joins silently drop it. REFERENTIAL_INTEGRITY_COUNT replaces a hand-written NOT IN check. The second argument names the parent table and its matching column:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.REFERENTIAL_INTEGRITY_COUNT\n    ON (DEVICE_ID, TABLE(DEVICES(DEVICE_ID)))\n    EXPECTATION NO_ORPHAN_READINGS (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor compound keys, the columns are listed in matching order on both sides. \u003Cem\u003EFind the Bad Rows\u003C/em\u003E shows how to retrieve reading 1017 itself.\u003C/p\u003E\n","\u003Cp\u003EReading 1015 was ingested twice:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.DUPLICATE_COUNT(SELECT READING_ID FROM SENSOR_READINGS) AS DUPLICATED_VALUES,\n       SNOWFLAKE.CORE.UNIQUE_COUNT(SELECT READING_ID FROM SENSOR_READINGS)    AS DISTINCT_VALUES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReturns 1 and 20. DUPLICATE_COUNT is the number of \u003Cem\u003Evalues that have duplicates\u003C/em\u003E, not excess rows; UNIQUE_COUNT is the number of \u003Cem\u003Edistinct\u003C/em\u003E values, not values occurring once.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EApproach\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESingle-column key\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDUPLICATE_COUNT ON (READING_ID) with VALUE = 0\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EGrain is a column combination\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDUPLICATE_COUNT on a column list; must be attached\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EOrphaned foreign keys\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EREFERENTIAL_INTEGRITY_COUNT\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT\n    ON (READING_ID)\n    EXPECTATION READING_ID_IS_UNIQUE (VALUE = 0);\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT\n    ON (DEVICE_ID, READING_TS)\n    EXPECTATION ONE_READING_PER_DEVICE_PER_TIMESTAMP (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBoth measure 1: reading 1015's two copies share a reading ID and a device-and-timestamp pair. NULLs count as a value, so a nullable key reports duplicates once two rows are both NULL.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVolume, Drift and Distribution\u003C/h2\u003E\n","\u003Cp\u003ESome failures are not bad rows: good data stops arriving, a column is renamed upstream, or a sensor recalibrated to Fahrenheit produces individually plausible readings with a shifted distribution.\u003C/p\u003E\n","\u003Cp\u003EROW_COUNT takes no column, so you attach it and read the measurement once an evaluation has run. \u003Cem\u003ELet Snowflake Learn Normal\u003C/em\u003E builds on this association:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT\n    ON ()\n    EXPECTATION ENOUGH_READINGS (VALUE > 15);\n\nSELECT MEASUREMENT_TIME, TABLE_NAME, METRIC_NAME, VALUE\nFROM TABLE(SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS(\n    REF_ENTITY_NAME   => 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN => 'TABLE'))\nWHERE METRIC_NAME = 'ROW_COUNT'\nORDER BY MEASUREMENT_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUntil the first scheduled run, the query returns no rows; evaluating now reports ENOUGH_READINGS at 21. A fixed floor like VALUE > 15 cannot express "less than usual"; anomaly detection can.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECase\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EApproach\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EVolume collapse\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EROW_COUNT ON ()\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EPrefer anomaly detection over a fixed floor\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESchema drift\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESCHEMA_CHANGE_COUNT ON ()\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EMeasures change since the previous evaluation; needs two evaluations and reports a delta, not a state\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDistribution shift\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EAVG, MEDIAN, STDDEV, APPROX_QUANTILE_99\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ERecord without an expectation; MEDIAN is exact, APPROX_QUANTILE_50 is cheaper\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EText truncation\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESTRING_LENGTH_MIN/AVG/MAX\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe quickest way to catch a truncation regression\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.SCHEMA_CHANGE_COUNT\n    ON ();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis association records changes without judging them. At the time of writing, giving SCHEMA_CHANGE_COUNT an expectation makes SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS fail with a syntax error for the whole table, and the persisting form silently record nothing, so read schema changes from the monitoring results instead.\u003C/p\u003E\n","\u003Cp\u003EStatistics metrics measure shape rather than validity:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.AVG(SELECT TEMPERATURE_C FROM SENSOR_READINGS)                AS MEAN_TEMP,\n       SNOWFLAKE.CORE.MEDIAN(SELECT TEMPERATURE_C FROM SENSOR_READINGS)             AS MEDIAN_TEMP,\n       SNOWFLAKE.CORE.STDDEV(SELECT TEMPERATURE_C FROM SENSOR_READINGS)             AS SPREAD_TEMP,\n       SNOWFLAKE.CORE.APPROX_QUANTILE_99(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS P99_TEMP;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EMEAN_TEMP\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EMEDIAN_TEMP\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003ESPREAD_TEMP\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EP99_TEMP\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E6.235\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E4.2\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E5.836\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E13.343\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EMean well above median, with a spread as large as the mean, is the signature of a mixed population: chilled sites and a freezer pooled in one table. Rather than inventing a threshold, attach the metric without an expectation to build history. It will not appear in SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS output:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.AVG\n    ON (TEMPERATURE_C);\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EScoping Checks\u003C/h2\u003E\n","\u003Cp\u003ERules rarely apply to a whole table, and fleet-wide numbers hide per-device failures.\u003C/p\u003E\n","\u003Cp\u003EFILTER scopes a check to a subset of rows. A negative battery level is impossible, except that readings flagged BATT_UNKNOWN deliberately carry -1 as a sentinel. Unfiltered, the rule fails on reading 1018 forever:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SNOWFLAKE.CORE.NEGATIVE_COUNT(SELECT BATTERY_PCT FROM SENSOR_READINGS) AS NEGATIVE_BATTERY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReturns 1. Excluding the sentinel rows by definition, rather than loosening the threshold to tolerate them:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NEGATIVE_COUNT\n    ON (BATTERY_PCT)\n    FILTER (STATUS_CODE = 'OK')\n    EXPECTATION HEALTHY_DEVICES_REPORT_BATTERY (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EScheduled evaluations apply the filter, so this measures 0 and passes, and would still catch a -1 from a device that claims to be healthy. FILTER comes after the column and before the expectation, and takes multiple conditions. To change or clear the filter later, modify the association rather than adding the metric again:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.NEGATIVE_COUNT\n    ON (BATTERY_PCT)\n    FILTER (STATUS_CODE = 'OK');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAt the time of writing, on-demand evaluation does not apply the filter: SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS reports HEALTHY_DEVICES_REPORT_BATTERY at 1, the unfiltered count. Judge a filtered check by its scheduled results.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENeed\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EClause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENotes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECheck a subset of rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EFILTER (condition)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-filter\"\u003EApply data quality checks to a subset of rows\u003C/a\u003E\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESame metric per group, one association\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EWITHIN GROUP (columns)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EResults carry GROUP_BY_INFO; SYSTEM$DATA_METRIC_SCAN accepts WITHIN_GROUP_VALUES\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EWide grouping\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EGROUP LIMIT (number)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBetween 1 and 1000; group by device or status, not by reading ID\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EWITHIN GROUP computes the metric separately for each group from a single association, with no need for one association per group value. Here, every device must report humidity:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT\n    ON (HUMIDITY_PCT)\n    WITHIN GROUP (DEVICE_ID)\n    GROUP LIMIT 500\n    EXPECTATION EVERY_DEVICE_REPORTS_HUMIDITY (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEvaluating now returns one row per device, each identified in GROUP_BY_VALUES. A grouped check raises at most one notification per evaluation, based on the worst group. WITHIN GROUP is not supported for FRESHNESS, REFERENTIAL_INTEGRITY_COUNT, or schema-level associations, and it disables anomaly detection on that association.\u003C/p\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-group-by\"\u003EApply data quality checks by group\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWrite Your Own Check\u003C/h2\u003E\n","\u003Cp\u003EWhen no system metric fits, you can write a DMF: a SQL function that takes a table argument and returns a number. This one counts readings with humidity outside a plausible range:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATA METRIC FUNCTION IMPLAUSIBLE_HUMIDITY(\n    ARG_T TABLE (ARG_HUMIDITY NUMBER)\n)\nRETURNS NUMBER\nAS\n$$\n    SELECT COUNT(*) FROM ARG_T WHERE ARG_HUMIDITY < 0 OR ARG_HUMIDITY > 100\n$$;\n\nSELECT IMPLAUSIBLE_HUMIDITY(SELECT HUMIDITY_PCT FROM SENSOR_READINGS) AS OUT_OF_RANGE;\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION IMPLAUSIBLE_HUMIDITY\n    ON (HUMIDITY_PCT)\n    EXPECTATION HUMIDITY_IN_RANGE (VALUE = 0);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe direct call returns 0. Calling a custom metric directly before attaching it is worthwhile: column validation inside a TABLE(...) argument is weaker than for single-column metrics, so mistakes in multi-table signatures can surface only at evaluation time. Custom metrics can take more than one table argument, and can use SNOWFLAKE.CORE.DATA_METRIC_SCHEDULE_TIME() for freshness rules the built-in cannot express. Check the catalog first: REFERENTIAL_INTEGRITY_COUNT and ACCEPTED_VALUES replace the two most commonly hand-rolled metrics. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-custom-dmfs\"\u003ECustom data metric functions\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECover a Whole Schema in One Statement\u003C/h2\u003E\n","\u003Cp\u003EVolume and freshness apply to any table, so you can attach them at the schema level. Snowflake creates an association for every supported table-like object in the schema, including ones added later. Three metrics work this way: ROW_COUNT, FRESHNESS, and TABLE_SIZE_BYTES. TABLE_SIZE_BYTES covers tables only and does not support anomaly detection.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER SCHEMA PUBLIC\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT ON ()\n    EXCLUDE_TABLE_TYPES = ('VIEW', 'MATERIALIZED_VIEW');\n\nALTER SCHEMA PUBLIC\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FRESHNESS ON ();\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EZero-argument FRESHNESS skips views and external tables on its own. A schema's own schedule defaults to every 60 minutes and cannot be trigger-based; a table's schedule overrides it. The references tell schema-level from table-level associations; an association created at the schema level is detached at the schema level:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT REF_ENTITY_NAME, METRIC_NAME, LEVEL, EXCLUDE_TABLE_TYPES\nFROM SNOWFLAKE.ACCOUNT_USAGE.DATA_METRIC_FUNCTION_REFERENCES\nWHERE REF_DATABASE_NAME = 'DQ_EXAMPLES';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EACCOUNT_USAGE views lag, so new associations can take a few hours to appear here. One statement across two hundred tables creates two hundred scheduled evaluations, so the schema's schedule deserves thought:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER SCHEMA PUBLIC SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-schema-level\"\u003EMonitor the data quality of a schema\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELet Snowflake Propose the Checks\u003C/h2\u003E\n","\u003Cp\u003ECortex Data Quality reads a table's metadata and usage patterns and suggests checks. It runs inside Snowflake using AI_COMPLETE, so data does not leave the account, and it respects the caller's access control. This is Snowsight-only.\u003C/p\u003E\n","\u003Cp\u003EThe role using it needs:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EEXECUTE DATA METRIC FUNCTION\u003C/strong\u003E on the account, and the \u003Cstrong\u003ESNOWFLAKE.DATA_METRIC_USER\u003C/strong\u003E database role.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EThe SNOWFLAKE.CORTEX_USER database role\u003C/strong\u003E, granted to PUBLIC by default.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccess to the mistral-7b and llama3.1-8b models.\u003C/strong\u003E Model access is governed by model role-based access control. By default, the CORTEX-MODEL-ROLE-ALL application role is granted to SNOWFLAKE.PUBLIC, so every role can use every model and suggestions work out of the box. If an administrator has restricted model access, the role needs the application roles for those two models. The data quality setup page still describes this requirement in terms of the CORTEX_MODELS_ALLOWLIST parameter, which is being deprecated in favor of model access control. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-privileges-and-access\"\u003EModel access control\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETo see which model roles PUBLIC holds:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW GRANTS TO APPLICATION ROLE SNOWFLAKE.PUBLIC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESuggestions start from the table's \u003Cstrong\u003EData quality\u003C/strong\u003E tab: choose \u003Cstrong\u003EGenerate with Cortex Data Quality\u003C/strong\u003E, which is a button on a table with no checks yet and an option under \u003Cstrong\u003EAdd quality check\u003C/strong\u003E once it has some.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/add_check_menu.png?v=6989d1d4\" alt=\"The Add quality check menu, offering Generate with Cortex Data Quality and Setup manually\"\u003E Expected values can be edited before accepting; an accepted suggestion is a real, billed, scheduled check. Generating suggestions is billed separately, as AI Services, plus the warehouse that runs Snowsight.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/suggested_checks.png?v=6989d1d4\" alt=\"Cortex suggested data quality checks for SENSOR_READINGS, each with an expected result and a reason\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESuggestions are a first draft: the model sees metadata and usage, not your business rules, so review each one before accepting it.\u003C/p\u003E\n","\u003Cp\u003EAccess is controlled through CORTEX_USER, which is not specific to data quality: revoking it from PUBLIC removes \u003Cstrong\u003Eevery\u003C/strong\u003E Snowflake Cortex feature from users who received it through PUBLIC. To restrict suggestions, an administrator revokes it and then grants it back to the roles that should keep Cortex:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nREVOKE DATABASE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC;\n\nCREATE ROLE IF NOT EXISTS CORTEX_ACCESS_ROLE;\nGRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE CORTEX_ACCESS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ELet Snowflake Learn Normal\u003C/h2\u003E\n","\u003Cp\u003EThe right row count for a Tuesday is not the right row count for a bank holiday. Anomaly detection learns the pattern from history and flags departures from it. It is available on ROW_COUNT and FRESHNESS. It can be switched on when a metric is added, by including ANOMALY_DETECTION = TRUE in the ADD statement, or on an existing association. Here it goes on the ROW_COUNT association from \u003Cem\u003EVolume, Drift and Distribution\u003C/em\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT ON ()\n    SET ANOMALY_DETECTION = TRUE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBefore it flags anything, the algorithm needs a \u003Cstrong\u003Etraining period\u003C/strong\u003E: at least two weeks of measurements for a metric that runs frequently, or at least two data points for one that runs infrequently or on a trigger. Until then, the references report TRAINING_IN_PROGRESS:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT METRIC_NAME, ANOMALY_DETECTION_STATUS\nFROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME   => 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN => 'table'))\nWHERE METRIC_NAME = 'ROW_COUNT';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDetection runs on the measurements the association's schedule produces, so the schedule decides how quickly history accumulates. SENSITIVITY ('LOW', 'MEDIUM' or 'HIGH', default MEDIUM) trades false positives against missed anomalies, and is set with MODIFY in the same way. Anomaly detection is also available at the schema level, by adding ANOMALY_DETECTION = TRUE to the schema-level ADD. Expectations suit rules true by definition; anomaly detection suits quantities with a normal range you could not write down. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-anomaly\"\u003EDetecting anomalies in data quality\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/anomaly_baseline.png?v=6989d1d4\" alt=\"The ROW_COUNT check under Volume, establishing its baseline during the training period\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFind the Bad Rows\u003C/h2\u003E\n","\u003Cp\u003ESYSTEM$DATA_METRIC_SCAN returns the records that fail an attached check, ready to quarantine or send back to the source team. This one uses the referential-integrity check from \u003Cem\u003EIntegrity and Uniqueness\u003C/em\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(SYSTEM$DATA_METRIC_SCAN(\n    REF_ENTITY_NAME => 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    METRIC_NAME     => 'SNOWFLAKE.CORE.REFERENTIAL_INTEGRITY_COUNT',\n    ARGUMENT_NAME   => 'DEVICE_ID'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIt returns one row, reading 1017, with all nine columns; the four that matter here:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003EREADING_ID\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDEVICE_ID\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003ETEMPERATURE_C\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESTATUS_CODE\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E1017\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EDEV-9999\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: right\"\u003E5.20\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EOK\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFor ACCEPTED_VALUES, the same Boolean expression goes in ARGUMENT_EXPRESSION:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(SYSTEM$DATA_METRIC_SCAN(\n    REF_ENTITY_NAME      => 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    METRIC_NAME          => 'SNOWFLAKE.CORE.ACCEPTED_VALUES',\n    ARGUMENT_NAME        => 'STATUS_CODE',\n    ARGUMENT_EXPRESSION  => 'STATUS_CODE IN (''OK'', ''WARN'')'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThat returns reading 1018. AT_TIMESTAMP scans the table as of a point in time, and WITHIN_GROUP_VALUES narrows a grouped association to one group.\u003C/p\u003E\n","\u003Cp\u003EMeasurements live in SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS (shown in \u003Cem\u003EVolume, Drift and Distribution\u003C/em\u003E); there is no ACCOUNT_USAGE version. For "what is failing", read verdicts directly rather than joining measurements to expectation definitions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT MEASUREMENT_TIME, METRIC_NAME, EXPECTATION_NAME, VALUE, EXPECTATION_VIOLATED\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_EXPECTATION_STATUS\nWHERE TABLE_DATABASE = 'DQ_EXAMPLES'\n  AND TABLE_SCHEMA   = 'PUBLIC'\n  AND TABLE_NAME     = 'SENSOR_READINGS'\nORDER BY MEASUREMENT_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis returns rows only once an evaluation has been recorded, by the schedule or by the persisting evaluation shown in \u003Cem\u003EAnatomy of a Check\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003EIn Snowsight, the table's \u003Cstrong\u003EData quality\u003C/strong\u003E tab shows the same, and the account-wide monitoring dashboard (preview) rolls it up across databases. \u003Cstrong\u003EImpacted Assets\u003C/strong\u003E on the table's page shows what reads from it.\u003C/p\u003E\n","\u003Cp\u003EOn a check's side panel, \u003Cstrong\u003EView failed records\u003C/strong\u003E opens a new SQL file in your workspace containing the SYSTEM$DATA_METRIC_SCAN query for that check, ready to run or adapt.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/dq_dashboard.png?v=6989d1d4\" alt=\"The account-wide data quality monitoring dashboard, listing failed expectations on SENSOR_READINGS\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-fixing\"\u003ERemediation of data quality issues\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-centralized-dashboard\"\u003EUsing the data quality monitoring dashboard\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGet Told, Don't Watch\u003C/h2\u003E\n","\u003Cp\u003EA notification needs a destination: email, or a webhook for Slack and similar:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE NOTIFICATION INTEGRATION DQ_EMAIL_INT\n    TYPE = EMAIL\n    ENABLED = TRUE\n    ALLOWED_RECIPIENTS = ('you@example.com');\n\nCREATE OR REPLACE SECRET SLACK_WEBHOOK_SECRET\n    TYPE = GENERIC_STRING\n    SECRET_STRING = 'T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX';\n\nCREATE OR REPLACE NOTIFICATION INTEGRATION DQ_SLACK_INT\n    TYPE = WEBHOOK\n    ENABLED = TRUE\n    WEBHOOK_URL = 'https://hooks.slack.com/services/SNOWFLAKE_WEBHOOK_SECRET'\n    WEBHOOK_SECRET = DQ_EXAMPLES.PUBLIC.SLACK_WEBHOOK_SECRET\n    WEBHOOK_BODY_TEMPLATE = '{"text": "SNOWFLAKE_WEBHOOK_MESSAGE"}'\n    WEBHOOK_HEADERS = ('Content-Type' = 'application/json');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe email recipients must be verified users in your account. Notifications are then switched on for the database, as YAML in a dollar-quoted string:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER DATABASE DQ_EXAMPLES SET DATA_QUALITY_MONITORING_SETTINGS =\n$$\nnotification:\n  enabled: TRUE\n  email_recipients: [ 'you@example.com' ]\n  integrations:\n    - DQ_SLACK_INT\n  cooldown_hours: 24\n  metadata_included: TRUE\n$$;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EEach violated expectation then arrives as its own entry in a notification email:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/notification_email.png?v=6989d1d4\" alt=\"A data quality notification email listing violated expectations on SENSOR_READINGS\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWithout cooldown_hours, an hourly check against a week-long violation sends 168 identical messages. metadata_included adds the table, metric and measured value. Settings apply \u003Cstrong\u003Eper database\u003C/strong\u003E, and a single association can opt out. Here it is the AVG association from \u003Cem\u003EVolume, Drift and Distribution\u003C/em\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.AVG ON (TEMPERATURE_C)\n    SET DATA_QUALITY_NOTIFICATION = FALSE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAn alert from a template\u003C/h3\u003E\n","\u003Cp\u003ENative notifications are settings on a database, not an object: there is nothing to schedule, scope to one table, pause or audit on its own. When you want those, create a Snowflake \u003Cstrong\u003Ealert\u003C/strong\u003E from the DQ_EXPECTATION_VIOLATIONS template. The template supplies the condition, which reads expectation status, and the notification; you choose the scope, the schedule and the destination. Its variables, defaults and allowed values are listed here:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$GET_ALERT_TEMPLATE('DQ_EXPECTATION_VIOLATIONS');\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis alert watches every expectation on SENSOR_READINGS and emails through the integration created above:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR ALTER ALERT READINGS_EXPECTATION_ALERT\n    FROM TEMPLATE DQ_EXPECTATION_VIOLATIONS\n    WAREHOUSE = DQ_EXAMPLES_WH\n    SCHEDULE = '60 MINUTE'\n    COMMENT = 'Emails when any expectation on SENSOR_READINGS is violated'\n    TEMPLATE_PARAMS = '{\n      "template_variables": {\n        "SCOPE_ACTIVE": "TABLE",\n        "SCOPE_DATABASE": "DQ_EXAMPLES",\n        "SCOPE_SCHEMA": "PUBLIC",\n        "SCOPE_TABLE": "SENSOR_READINGS",\n        "NOTIFICATION_MODE": "EMAIL",\n        "EMAIL_NOTIFICATION_INTEGRATION": "DQ_EMAIL_INT"\n      },\n      "notification_config": {\n        "notification_integration": "DQ_EMAIL_INT",\n        "email_config": { "toAddress": ["you@example.com"] }\n      }\n    }';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFor email, the integration goes in two places: EMAIL_NOTIFICATION_INTEGRATION, which the alert's action reads, and notification_config, which carries the recipients. EXPECTATION_NAMES narrows the alert to a comma-separated list, and NOTIFICATION_MODE also accepts WEBHOOK and IN_APP. The alert is created suspended. To test it without waiting, first record a verdict, so the expectation status has something recent:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM TABLE(SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS_PERSIST_RESULT('DQ_EXAMPLES.PUBLIC.SENSOR_READINGS'));\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe verdict takes a few seconds to reach the status. Then run the alert once:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EEXECUTE ALERT READINGS_EXPECTATION_ALERT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA minute later, check the run and the email:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SCHEDULED_TIME, STATE, SQL_ERROR_MESSAGE\nFROM TABLE(INFORMATION_SCHEMA.ALERT_HISTORY(\n    SCHEDULED_TIME_RANGE_START => DATEADD('HOUR', -1, CURRENT_TIMESTAMP())))\nWHERE NAME = 'READINGS_EXPECTATION_ALERT'\nORDER BY SCHEDULED_TIME DESC;\n\nSELECT CREATED, STATUS, ERROR_MESSAGE\nFROM TABLE(INFORMATION_SCHEMA.NOTIFICATION_HISTORY(\n    START_TIME => DATEADD('HOUR', -1, CURRENT_TIMESTAMP()),\n    INTEGRATION_NAME => 'DQ_EMAIL_INT'))\nORDER BY CREATED DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe alert should read TRIGGERED, because several expectations on SENSOR_READINGS are violated, and the email SUCCESS. The template's condition fires on any violation measured since its last successful run (on the first run, the past hour), whether recorded by the schedule or by the call above. ALTER ALERT READINGS_EXPECTATION_ALERT RESUME starts the hourly schedule.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/alert_email.png?v=6989d1d4\" alt=\"The email sent by READINGS_EXPECTATION_ALERT, listing violated expectations on SENSOR_READINGS\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe alert's owner role needs EXECUTE ALERT on the account, USAGE on the warehouse (or EXECUTE MANAGED ALERT for a serverless alert, which omits WAREHOUSE), USAGE on the email integration, and the SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER application role to read expectation status. Snowflake publishes templates for anomaly detection (DQ_ANOMALY_DETECTION) and for other products; SYSTEM$LIST_ALERT_TEMPLATES lists them.\u003C/p\u003E\n","\u003Cp\u003ESee \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-notifications\"\u003ESending notifications for data quality issues\u003C/a\u003E and \u003Ca href=\"https://docs.snowflake.com/en/user-guide/alerts#label-alerts-create-from-template\"\u003ECreating an alert from a template with SQL\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EOperating Checks in Production\u003C/h2\u003E\n","\u003Ch3\u003EWhere to put your checks\u003C/h3\u003E\n","\u003Cp\u003EThe best place to check is where the defect exists, not where it hurts. Reading 1017 would be dropped by a silver-layer inner join, so a gold check passes \u003Cem\u003Ebecause\u003C/em\u003E the bad row was discarded.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ELayer\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECheck for\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EWhy here\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBronze (raw)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ENulls, formats, duplicates, orphans, clock skew, volume, freshness\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EThe only place the defect still exists\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ESilver (conformed)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EReferential integrity, grain uniqueness, row counts against bronze\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EWhere joins and dedup happen, so where rows go missing\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EGold (serving)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003EBusiness invariants and totals\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\" style=\"text-align: left\"\u003ECatches logic errors row-level checks cannot see\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EChecks as code\u003C/h3\u003E\n","\u003Cp\u003EFor anything you intend to keep, checks can live in the same version-controlled definition as the table. Snowflake's declarative deployment model (DCM Projects) supports data metric functions and expectations as first-class definitions. Attaching a metric to a table you do not own needs an explicit executing role, and the deployment tooling can run every expectation as a \u003Cstrong\u003Etest step\u003C/strong\u003E, so a deployment fails when the data does not satisfy its own rules. A threshold change then shows up as a reviewable diff. For a worked example, with expectations declared with ATTACH DATA METRIC FUNCTION and run with TEST ALL as part of deployment, see \u003Ca href=\"https://www.snowflake.com/en/developers/guides/build-data-pipelines-with-snowflake-dcm-projects/\"\u003EBuild Data Pipelines with Snowflake DCM Projects\u003C/a\u003E. For the syntax, see the \u003Ca href=\"https://docs.snowflake.com/en/user-guide/dcm-projects/dcm-projects-files\"\u003EDCM Projects file reference\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EWhat It Costs\u003C/h2\u003E\n","\u003Cp\u003EScheduled evaluations run on serverless compute and bill under the \u003Cstrong\u003EData Quality Monitoring\u003C/strong\u003E category. Cost scales with associations × evaluation frequency × work per evaluation; a schema-level attachment across two hundred tables on an hourly schedule is 4,800 evaluations a day. Collecting the results into the event table bills separately, as \u003Cstrong\u003ELogging\u003C/strong\u003E. Creating a metric and suspended schedules are not billed, and a direct call in a SELECT bills only the warehouse that runs it. Spend is visible here:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.DATA_QUALITY_MONITORING_USAGE_HISTORY\nORDER BY START_TIME DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAcross an organization, METERING_DAILY_HISTORY carries the same spend with SERVICE_TYPE = 'DATA_QUALITY_MONITORING'. Costs stay low when cadence matches how fast you would act, TRIGGER_ON_CHANGES is used where evaluating on data arrival fits, and schedules are suspended with an empty string rather than UNSET. Alerts, like the template alert, bill their own warehouse each time they run.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EIf you tried the examples, dropping the database removes its tables, their associations, custom metrics, the secret, the alert, and the database's monitoring settings. The warehouse and the notification integrations live at account level and outlive the database:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP DATABASE IF EXISTS DQ_EXAMPLES;\nDROP WAREHOUSE IF EXISTS DQ_EXAMPLES_WH;\nDROP INTEGRATION IF EXISTS DQ_EMAIL_INT;\nDROP INTEGRATION IF EXISTS DQ_SLACK_INT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo keep the objects but stop the billing, suspend everything that runs on a schedule: the alert and the table and schema schedules.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER ALERT DQ_EXAMPLES.PUBLIC.READINGS_EXPECTATION_ALERT SUSPEND;\nALTER TABLE DQ_EXAMPLES.PUBLIC.SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '';\nALTER TABLE DQ_EXAMPLES.PUBLIC.SENSOR_READINGS_V2 SET DATA_METRIC_SCHEDULE = '';\nALTER SCHEMA DQ_EXAMPLES.PUBLIC SET DATA_METRIC_SCHEDULE = '';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf you revoked CORTEX_USER from PUBLIC in \u003Cem\u003ELet Snowflake Propose the Checks\u003C/em\u003E and want the default back:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE PUBLIC;\nDROP ROLE IF EXISTS CORTEX_ACCESS_ROLE;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EData quality monitoring in Snowflake is a small vocabulary applied with judgment. A metric measures, an expectation judges, a schedule decides how often, and everything else is choosing well.\u003C/p\u003E\n","\u003Ch3\u003EWhat to take away\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EPick the metric from the column's shape.\u003C/strong\u003E The decision table in \u003Cem\u003EChoosing a Check\u003C/em\u003E is the whole method.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKnow which metrics can be called directly.\u003C/strong\u003E Column-argument metrics answer instantly in a SELECT; zero-argument ones must be attached.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOne association per metric and column.\u003C/strong\u003E Change a check with MODIFY; adding it again is not an update.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOne check is rarely enough.\u003C/strong\u003E Freshness alone was defeated by a single future-dated row.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECheck where the defect exists\u003C/strong\u003E, not where it hurts. Gold passing is not evidence.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EScope with FILTER, don't loosen thresholds.\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAssert rules; learn ranges.\u003C/strong\u003E Expectations for what is true by definition, anomaly detection for normal ranges.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENotify with a cooldown\u003C/strong\u003E, or people will filter the alerts.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUse a template when you need an alert object.\u003C/strong\u003E It brings its own scope, schedule and history, without writing the condition yourself.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKeep checks in code\u003C/strong\u003E, so a threshold change is reviewable.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-intro\"\u003EIntroduction to data quality checks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-system-dmfs\"\u003ESystem data metric functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-custom-dmfs\"\u003ECustom data metric functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-working\"\u003EUse SQL to set up data quality checks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-expectations\"\u003EUse SQL to work with expectations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-filter\"\u003EApply data quality checks to a subset of rows\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-group-by\"\u003EApply data quality checks by group\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-schema-level\"\u003EMonitor the data quality of a schema\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-anomaly\"\u003EDetecting anomalies in data quality\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-notifications\"\u003ESending notifications for data quality issues\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-fixing\"\u003ERemediation of data quality issues\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-ui-setup\"\u003EUse Snowsight to set up data quality checks\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-centralized-dashboard\"\u003EUsing the data quality monitoring dashboard\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-quality-access-control\"\u003EAccess control for data quality\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Comprehensive Guide to Snowflake Data Quality Monitoring","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\nData quality problems rarely announce themselves. A device clock drifts and stamps readings two days into the future. An upstream system swaps a missing battery level for -1. A firmware release emits truncated JSON. Nothing errors, and the dashboards keep rendering, on data that is quietly wrong.\n\nSnowflake can check for these conditions natively, on a schedule, without moving data or standing up another tool. This guide explores what that makes possible.\n\n### How to use this guide\n\nStart with *The Example Data*, which creates everything the examples use. Each later section explores **one capability** and adds checks to the same table, so the sections are meant to be run in order; where a section relies on a check from an earlier one, it says so. Where something can be done both in SQL and in Snowsight, the section shows both. Profiling, AI-suggested checks, and the monitoring dashboard are Snowsight-only; everything else is shown in SQL.\n\n### What You'll Learn\n\n- How a check is assembled: metric, association, schedule, expectation, results\n- Which system data metric function to reach for, given a column's shape\n- Completeness, validity, timeliness, integrity, volume, drift and distribution checks\n- How to scope a check with FILTER or compute it per group with WITHIN GROUP\n- How to write a custom metric, cover a whole schema, and let Snowflake suggest checks and learn normal ranges\n- How to find the offending rows, get notified natively or through an alert created from a template\n- Where to place checks, how to keep them in code, and what monitoring costs\n\n### What You'll Build\n\nA set of quality checks on a small IoT sensor telemetry dataset: system and custom metrics, expectations, scoped and grouped checks, schema-level coverage, anomaly detection, and notifications that fire when a check fails.\n\n### What You'll Need\n\n- A Snowflake account on **Enterprise Edition or higher**. Data Quality Monitoring is not available on Standard Edition or on [trial accounts](https://docs.snowflake.com/en/user-guide/data-quality-intro#label-data-quality-limitations).\n- A role that can create a database and a warehouse, and that holds:\n  - EXECUTE DATA METRIC FUNCTION on the account, to attach and schedule metrics\n  - MANAGE DATA QUALITY on the account, for schema-level checks and notifications\n  - EXECUTE ALERT on the account, for the template alert in *Get Told, Don't Watch*\n  - CREATE INTEGRATION on the account, for *Get Told, Don't Watch*\n\n  ACCOUNTADMIN holds all of these. See [Access control for data quality](https://docs.snowflake.com/en/user-guide/data-quality-access-control).\n- Familiarity with running SQL in a Snowsight worksheet.\n\n### Use Cortex Code with this guide\n\nWith Cortex Code, the /data-quality skill can apply these workflows to your own tables: recommending metrics, scoring schema health, and investigating violations.\n\n\u003C!-- ------------------------ --\u003E\n## The Example Data\n\nThe examples use a small IoT dataset. Refrigerated sites report temperature and humidity from battery-powered sensors; readings land in a raw table keyed to a device inventory, with **deliberate defects** for the checks to find. Timestamps are relative to the current time.\n\n```sql\nCREATE DATABASE IF NOT EXISTS DQ_EXAMPLES;\nUSE DATABASE DQ_EXAMPLES;\nUSE SCHEMA PUBLIC;\n\nCREATE WAREHOUSE IF NOT EXISTS DQ_EXAMPLES_WH\n    WAREHOUSE_SIZE = XSMALL\n    AUTO_SUSPEND = 60\n    INITIALLY_SUSPENDED = TRUE;\nUSE WAREHOUSE DQ_EXAMPLES_WH;\n\nCREATE OR REPLACE TABLE SITES (\n    SITE_ID   VARCHAR(10),\n    SITE_NAME VARCHAR(100),\n    REGION    VARCHAR(50)\n);\n\nCREATE OR REPLACE TABLE DEVICES (\n    DEVICE_ID    VARCHAR(20),\n    SITE_ID      VARCHAR(10),\n    MODEL        VARCHAR(50),\n    INSTALLED_ON DATE,\n    LABEL        VARCHAR(100)\n);\n\nCREATE OR REPLACE TABLE SENSOR_READINGS (\n    READING_ID    NUMBER(10,0),\n    DEVICE_ID     VARCHAR(20),\n    READING_TS    TIMESTAMP_LTZ,\n    INGESTED_AT   TIMESTAMP_LTZ,\n    TEMPERATURE_C NUMBER(5,2),\n    HUMIDITY_PCT  NUMBER(5,2),\n    BATTERY_PCT   NUMBER(5,2),\n    PAYLOAD       VARCHAR(500),\n    STATUS_CODE   VARCHAR(20)\n);\n\nINSERT INTO SITES (SITE_ID, SITE_NAME, REGION) VALUES\n    ('SITE-01', 'Rotterdam Cold Store',  'EMEA'),\n    ('SITE-02', 'Dublin Distribution',   'EMEA'),\n    ('SITE-03', 'Singapore Transit Hub', 'APAC');\n\nINSERT INTO DEVICES (DEVICE_ID, SITE_ID, MODEL, INSTALLED_ON, LABEL) VALUES\n    ('DEV-1001', 'SITE-01', 'TH-200',  '2025-03-14', 'Cold aisle A'),\n    ('DEV-1002', 'SITE-01', 'TH-200',  '2025-03-14', 'Cold aisle B'),\n    ('DEV-1003', 'SITE-02', 'TH-350',  '2025-07-02', '  Loading bay 1  '),\n    ('DEV-1004', 'SITE-02', 'TH-350',  '2025-07-02', 'Loading bay 2'),\n    ('DEV-1005', 'SITE-03', 'TH-350X', '2026-01-20', 'Transit chiller');\n\nINSERT INTO SENSOR_READINGS\n    (READING_ID, DEVICE_ID, READING_TS, INGESTED_AT, TEMPERATURE_C, HUMIDITY_PCT, BATTERY_PCT, PAYLOAD, STATUS_CODE)\nSELECT 1001, 'DEV-1001', DATEADD(HOUR, -9, CURRENT_TIMESTAMP()), DATEADD(HOUR, -9, CURRENT_TIMESTAMP()),  3.90, 61.20, 98.00, '{\"rssi\":-71,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1002, 'DEV-1001', DATEADD(HOUR, -8, CURRENT_TIMESTAMP()), DATEADD(HOUR, -8, CURRENT_TIMESTAMP()),  4.10, 60.80, 97.50, '{\"rssi\":-70,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1003, 'DEV-1002', DATEADD(HOUR, -8, CURRENT_TIMESTAMP()), DATEADD(HOUR, -8, CURRENT_TIMESTAMP()),  3.40, 63.10, 91.00, '{\"rssi\":-77,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1004, 'DEV-1002', DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), DATEADD(HOUR, -7, CURRENT_TIMESTAMP()),  3.60, 62.40, 90.50, '{\"rssi\":-76,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1005, 'DEV-1003', DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), DATEADD(HOUR, -7, CURRENT_TIMESTAMP()), 12.80, 48.90, 76.00, '{\"rssi\":-65,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1006, 'DEV-1003', DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), 13.10, 47.60, 75.50, '{\"rssi\":-66,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1007, 'DEV-1004', DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), DATEADD(HOUR, -6, CURRENT_TIMESTAMP()), 11.90, 50.20, 88.00, '{\"rssi\":-69,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1008, 'DEV-1004', DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), 12.20, 49.70, 87.50, '{\"rssi\":-68,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1009, 'DEV-1005', DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), DATEADD(HOUR, -5, CURRENT_TIMESTAMP()), -2.40, 55.30, 64.00, '{\"rssi\":-81,\"fw\":\"2.4.1\"}', 'OK'\nUNION ALL SELECT 1010, 'DEV-1005', DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), -2.10, 54.80, 63.50, '{\"rssi\":-80,\"fw\":\"2.4.1\"}', 'OK'\nUNION ALL SELECT 1011, 'DEV-1001', DATEADD(HOUR, -4, CURRENT_TIMESTAMP()), DATEADD(HOUR, -4, CURRENT_TIMESTAMP()),  4.30, 60.10, 96.00, '{\"rssi\":-72,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1012, 'DEV-1002', DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), DATEADD(HOUR, -3, CURRENT_TIMESTAMP()),  3.80, 61.90, 89.00, '{\"rssi\":-75,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1013, 'DEV-1003', DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), DATEADD(HOUR, -3, CURRENT_TIMESTAMP()), 13.40, 46.80, 74.00, '{\"rssi\":-64,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1014, 'DEV-1004', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), 12.50, 49.10, 86.50, '{\"rssi\":-67,\"fw\":\"2.4.0\"}', 'OK'\nUNION ALL SELECT 1015, 'DEV-1005', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), -1.90, 54.20, 62.50, '{\"rssi\":-79,\"fw\":\"2.4.1\"}', 'OK'\nUNION ALL SELECT 1015, 'DEV-1005', DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), DATEADD(HOUR, -2, CURRENT_TIMESTAMP()), -1.90, 54.20, 62.50, '{\"rssi\":-79,\"fw\":\"2.4.1\"}', 'OK'\nUNION ALL SELECT 1016, 'DEV-1001', DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), NULL,  59.70, 95.50, '{\"rssi\":-73,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1017, 'DEV-9999', DATEADD(HOUR, -1, CURRENT_TIMESTAMP()), DATEADD(HOUR, -1, CURRENT_TIMESTAMP()),  5.20, 58.30, 80.00, '{\"rssi\":-74,\"fw\":\"2.3.1\"}', 'OK'\nUNION ALL SELECT 1018, 'DEV-1003', DATEADD(MINUTE, -30, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -30, CURRENT_TIMESTAMP()), 13.00, 47.10, -1.00, '{\"rssi\":-63,\"fw\":\"2.4.0\"}', 'BATT_UNKNOWN'\nUNION ALL SELECT 1019, 'DEV-1004', DATEADD(MINUTE, -20, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -20, CURRENT_TIMESTAMP()), 12.10, 49.40, 86.00, '{\"rssi\":-67,\"fw\":', 'OK'\nUNION ALL SELECT 1020, 'DEV-1002', DATEADD(DAY, 2, CURRENT_TIMESTAMP()), DATEADD(MINUTE, -10, CURRENT_TIMESTAMP()),  3.70, 62.00, 88.50, '{\"rssi\":-75,\"fw\":\"2.3.1\"}', 'OK';\n```\n\nThat is 21 rows across 5 devices and 3 sites. The timestamp columns are TIMESTAMP_LTZ on purpose: metrics that compare against the evaluation time (FRESHNESS and FUTURE_TIMESTAMP_COUNT) accept DATE, TIMESTAMP_LTZ and TIMESTAMP_TZ, but **not TIMESTAMP_NTZ**, Snowflake's default timestamp type.\n\n### The defects, named openly\n\n| Reading | Problem | The check that finds it |\n|:--------|:--------|:------------------------|\n| 1016 | TEMPERATURE_C is NULL | NULL_COUNT |\n| 1020 | READING_TS is two days in the future (device clock skew) | FUTURE_TIMESTAMP_COUNT |\n| 1019 | PAYLOAD is truncated and not valid JSON | INVALID_JSON_COUNT |\n| 1018 | BATTERY_PCT is -1, a sentinel for \"unknown\"; STATUS_CODE is BATT_UNKNOWN | NEGATIVE_COUNT, ACCEPTED_VALUES |\n| 1015 | Appears twice (duplicated on ingest) | DUPLICATE_COUNT |\n| 1017 | DEVICE_ID is DEV-9999, which is not in DEVICES | REFERENTIAL_INTEGRITY_COUNT |\n| DEVICES DEV-1003 | LABEL has leading and trailing spaces | UNTRIMMED_STRING_COUNT |\n\nTEMPERATURE_C legitimately goes negative: DEV-1005 is a freezer. That is why the negative-value check targets BATTERY_PCT, not temperature.\n\n\u003C!-- ------------------------ --\u003E\n## Anatomy of a Check\n\nA check has five parts:\n\n1. A **data metric function (DMF)** measures something and returns a number. It makes no judgment.\n2. An **association** attaches the DMF to a table and column.\n3. A **schedule**, set on the table, governs how often every attached metric runs.\n4. An **expectation** turns the number into a verdict. A metric without one is measured but never judged.\n5. **Results** land in monitoring views you query or read in Snowsight.\n\nThis is the model the rest of the guide builds on. To set a schedule, then attach a metric with an expectation:\n\n```sql\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT\n    ON (TEMPERATURE_C)\n    EXPECTATION MUST_HAVE_TEMPERATURE (VALUE = 0);\n```\n\nEach metric, table and column combination can have **only one association**. Adding the same combination again, even with a different filter or grouping, returns an error that points to MODIFY; to change a check, modify it, or drop and re-add it.\n\nOn a new table, you can declare checks in CREATE TABLE, after the column list, so the table and its guarantees arrive together. The bindings go inside parentheses, separated by commas; see [Use SQL to set up data quality checks](https://docs.snowflake.com/en/user-guide/data-quality-working) for the full syntax.\n\n```sql\nCREATE OR REPLACE TABLE SENSOR_READINGS_V2 (\n    READING_ID    NUMBER(10,0),\n    TEMPERATURE_C NUMBER(5,2)\n)\nWITH DATA METRIC FUNCTION (\n    SNOWFLAKE.CORE.NULL_COUNT\n        ON (TEMPERATURE_C)\n        EXPECTATION MUST_HAVE_TEMPERATURE (VALUE = 0)\n);\n```\n\n### Direct calls versus attached metrics\n\nMetrics that **take a column argument** can be called directly in a SELECT, immediately and without a schedule. A direct call is not billed as Data Quality Monitoring; it runs on your warehouse like any other query:\n\n```sql\nSELECT SNOWFLAKE.CORE.NULL_COUNT(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS MISSING_TEMPERATURE;\n```\n\nReturns 1. Metrics that **take no column argument** (ROW_COUNT, SCHEMA_CHANGE_COUNT, REFERENTIAL_INTEGRITY_COUNT, FRESHNESS with no column, multi-column DUPLICATE_COUNT), and ACCEPTED_VALUES, work only once attached.\n\n### Evaluating expectations now\n\nYou can evaluate every expectation on a table without waiting for the schedule:\n\n```sql\nSELECT METRIC_NAME, EXPECTATION_NAME, EXPECTATION_EXPRESSION, VALUE, EXPECTATION_VIOLATED\nFROM TABLE(SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS('SENSOR_READINGS'));\n```\n\n| METRIC_NAME | EXPECTATION_NAME | EXPECTATION_EXPRESSION | VALUE | EXPECTATION_VIOLATED |\n|:------------|:-----------------|:-----------------------|------:|:---------------------|\n| NULL_COUNT | MUST_HAVE_TEMPERATURE | VALUE = 0 | 1 | TRUE |\n\nThis form returns a result without recording it. SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS_PERSIST_RESULT also writes it to monitoring history, which anything reading DATA_QUALITY_MONITORING_EXPECTATION_STATUS, such as an alert or your own queries, depends on. The Snowsight Monitoring page is different: it reports a check as not run yet until its first scheduled evaluation. Pass it the fully qualified table name: at the time of writing, with a bare name it returns the same result but records nothing, so an alert reading the status finds no violation.\n\n### In Snowsight\n\nThe same check can be added from the table's **Data quality** tab. On a table with no checks yet, choose **Set up manually**, pick a check type such as **Nulls**, then a column and the expected value. A check created in Snowsight is visible to SQL and vice versa, but Snowsight names the expectation for you, here EXP__TEMPERATURE_C__NONULLS rather than MUST_HAVE_TEMPERATURE. See [Use Snowsight to set up data quality checks](https://docs.snowflake.com/en/user-guide/data-quality-ui-setup).\n\n![The quality check types offered when setting up a check in Snowsight](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/quality_check_types.png?v=6989d1d4)\n\n![A Nulls check expecting a count of 0 nulls in TEMPERATURE_C](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/add_null_check.png?v=6989d1d4)\n\n### Scheduling options\n\nThe schedule belongs to the table, not to a metric: every metric attached to a table runs on that table's schedule. There are three kinds. These are alternatives, not steps: the examples in this guide assume the daily '1440 MINUTE' schedule shown above.\n\n```sql\n-- Fixed interval: 5, 15, 30, 60, 720, or 1440 minutes\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '60 MINUTE';\n\n-- Cron: here, 08:00 UTC on weekdays\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'USING CRON 0 8 * * MON-FRI UTC';\n\n-- On change: whenever DML modifies the table\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = 'TRIGGER_ON_CHANGES';\n```\n\nThe right interval matches how fast you would act. TRIGGER_ON_CHANGES suits tables loaded in batches; it works on regular, transient, temporary, dynamic, external and Iceberg tables but **not on views**, and reclustering does not trigger it. Cron runs in the time zone named in the expression, and a run is skipped if the previous one has not finished. See [Schedule the DMF to run](https://docs.snowflake.com/en/user-guide/data-quality-working).\n\nTo pause evaluations while keeping checks attached, set the schedule to an empty string:\n\n```sql\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '';\n```\n\nUNSET is a trap: it reverts the table to the **default hourly** cadence, which multiplies a daily table's evaluations by twenty-four. The references show the current schedule; a blank SCHEDULE means suspended:\n\n```sql\nSELECT METRIC_NAME, SCHEDULE, SCHEDULE_STATUS\nFROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME   =\u003E 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN =\u003E 'table'));\n```\n\nIf you tried the alternatives, restore the daily schedule before continuing:\n\n```sql\nALTER TABLE SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n```\n\n![The Monitoring view of SENSOR_READINGS, showing failing checks and the Freshness, Volume and Schema quality dimensions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/monitoring_overview.png?v=6989d1d4)\n\n\u003C!-- ------------------------ --\u003E\n## Profile Before You Check\n\nBefore writing rules, look at the data. In Snowsight, open **DQ_EXAMPLES » PUBLIC » SENSOR_READINGS**, go to the **Data quality** tab and switch to **Data Profile** to see per-column statistics. This view is Snowsight-only.\n\n![The data profile of SENSOR_READINGS, showing one null in TEMPERATURE_C and a BATTERY_PCT minimum of -1](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/data_profile.png?v=6989d1d4)\n\nTwo things stand out: TEMPERATURE_C has a non-zero null share, and BATTERY_PCT has a minimum of -1. The scripted equivalent:\n\n```sql\nSELECT COUNT(*)                                            AS TOTAL_ROWS,\n       COUNT(*) - COUNT(TEMPERATURE_C)                      AS MISSING_TEMPERATURE,\n       MIN(BATTERY_PCT)                                     AS LOWEST_BATTERY,\n       COUNT(DISTINCT DEVICE_ID)                            AS DEVICES_REPORTING,\n       MAX(READING_TS)                                      AS LATEST_READING\nFROM SENSOR_READINGS;\n```\n\nProfiling tells you what is unusual, not what is *wrong*; that judgment is yours.\n\n\u003C!-- ------------------------ --\u003E\n## Choosing a Check\n\nSnowflake's system metrics live in SNOWFLAKE.CORE: more than 185 of them, grouped into categories. Start from the category that matches the column, then pick a metric:\n\n| Category | Catches | Examples |\n|:-----------------------|:--------|:---------|\n| Completeness | Missing or empty values | NULL_COUNT, BLANK_COUNT, EMPTY_STRING_COUNT |\n| Validity | Values that break a format or rule | ACCEPTED_VALUES, INVALID_JSON_COUNT, FUTURE_TIMESTAMP_COUNT, REFERENTIAL_INTEGRITY_COUNT |\n| Consistency | Formatting regressions in text | UNTRIMMED_STRING_COUNT, CASE_FORMAT_VIOLATION_COUNT |\n| Uniqueness | Double ingestion, broken grain | DUPLICATE_COUNT, UNIQUE_COUNT |\n| Freshness | Stalled pipelines | FRESHNESS |\n| Volume | Tables that stop growing or collapse | ROW_COUNT, TABLE_SIZE_BYTES |\n| Schema | Columns added, dropped, renamed, retyped | SCHEMA_CHANGE_COUNT |\n| Statistics | Drift no single row reveals; sentinel values | AVG, STDDEV, APPROX_QUANTILE_50, NEGATIVE_COUNT |\n| Outliers | Values far outside the column's range | OUTLIER_IQR_COUNT, EXTREME_OUTLIER_ZSCORE_COUNT |\n| Machine Learning | Model predictions drifting from actuals | ACCURACY, MAPE, R_SQUARED |\n\nMost metrics come in a _COUNT and a _PERCENT form: _COUNT suits cases where any occurrence is a defect, and _PERCENT cases where proportion matters in a table whose size varies. For the current list, see [System data metric functions](https://docs.snowflake.com/en/user-guide/data-quality-system-dmfs).\n\n\u003C!-- ------------------------ --\u003E\n## Completeness and Validity\n\nEvery reading should carry a temperature (reading 1016 does not), a parseable payload (1019 does not), and a known status code (1018 does not). The column-argument metrics can be measured directly:\n\n```sql\nSELECT SNOWFLAKE.CORE.NULL_PERCENT(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS MISSING_PCT,\n       SNOWFLAKE.CORE.INVALID_JSON_COUNT(SELECT PAYLOAD FROM SENSOR_READINGS) AS BAD_PAYLOADS,\n       SNOWFLAKE.CORE.UNTRIMMED_STRING_COUNT(SELECT LABEL FROM DEVICES)       AS PADDED_LABELS;\n```\n\n| MISSING_PCT | BAD_PAYLOADS | PADDED_LABELS |\n|------------:|-------------:|--------------:|\n| 4.761900 | 1 | 1 |\n\nOne row in twenty-one has no temperature. The count form expresses a rule (\"never\"); the percentage form, a tolerance (\"under 1%\"). MUST_HAVE_TEMPERATURE from *Anatomy of a Check* is already the rule for temperature; the payload rule attaches the same way:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.INVALID_JSON_COUNT\n    ON (PAYLOAD)\n    EXPECTATION PAYLOAD_IS_JSON (VALUE = 0);\n```\n\n| Case | DMF | Notes |\n|:-----|:----|:------|\n| Missing values | NULL_COUNT, NULL_PERCENT | Count for a rule, percent for a tolerance |\n| Malformed JSON | INVALID_JSON_COUNT | Tests TRY_PARSE_JSON; NULLs are not counted as invalid |\n| Text that will fail a numeric cast | INVALID_NUMERIC_TYPE_CAST_COUNT | Attach to staging columns you plan to cast |\n| Sentinel or impossible numbers | NEGATIVE_COUNT, ZERO_COUNT | Apply only where the value is genuinely impossible; see *Scoping Checks* |\n| Empty or padded strings | BLANK_COUNT, UNTRIMMED_STRING_COUNT | Finds the DEV-1003 label in DEVICES |\n| Value outside a known set | ACCEPTED_VALUES | Takes a lambda; must be attached |\n\nACCEPTED_VALUES is the one pattern that differs. It takes a Boolean lambda and counts the rows that fail it:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ACCEPTED_VALUES\n    ON (STATUS_CODE, STATUS_CODE -\u003E STATUS_CODE IN ('OK', 'WARN'))\n    EXPECTATION STATUS_IS_KNOWN (VALUE = 0);\n```\n\nEvaluating now with SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS shows STATUS_IS_KNOWN at 1 (reading 1018) and saves you writing a custom value-in-list metric.\n\n\u003C!-- ------------------------ --\u003E\n## Timeliness\n\nFRESHNESS returns the seconds between the newest value in a timestamp column and the moment the check ran:\n\n```sql\nSELECT SNOWFLAKE.CORE.FRESHNESS(SELECT READING_TS FROM SENSOR_READINGS) AS STALENESS_SECONDS;\n```\n\nOn the example data this returns something like -172700, which is **negative**. Reading 1020 is stamped two days ahead, so freshness reports data that has not happened yet, and an expectation like VALUE \u003C 3600 would pass while the pipeline was dead. **A freshness check alone can be defeated by one bad timestamp.** Paired with a clock-skew check, it cannot be fooled:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FRESHNESS\n    ON (READING_TS)\n    EXPECTATION READINGS_ARE_RECENT (VALUE \u003C 600);\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FUTURE_TIMESTAMP_COUNT\n    ON (READING_TS)\n    EXPECTATION NO_SKEWED_READINGS (VALUE = 0);\n```\n\n| Case | Approach |\n|:-----|:---------|\n| Is the *data* current? | FRESHNESS on a timestamp column |\n| Did the *pipeline* run? | FRESHNESS with ON (), which measures the last DML against the table |\n| Is any row dated in the future? | FUTURE_TIMESTAMP_COUNT; prefer the count over FUTURE_TIMESTAMP_PERCENT |\n| Business hours, per-source SLAs | A custom metric using SNOWFLAKE.CORE.DATA_METRIC_SCHEDULE_TIME(); see *Write Your Own Check* |\n\nFUTURE_TIMESTAMP_COUNT compares against the evaluation time, so a row that passes today can fail on a rerun of history. That is correct: a backfill dated next Tuesday is wrong on both days.\n\n\u003C!-- ------------------------ --\u003E\n## Integrity and Uniqueness\n\nReading 1017 reports device DEV-9999, which is not in DEVICES; joins silently drop it. REFERENTIAL_INTEGRITY_COUNT replaces a hand-written NOT IN check. The second argument names the parent table and its matching column:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.REFERENTIAL_INTEGRITY_COUNT\n    ON (DEVICE_ID, TABLE(DEVICES(DEVICE_ID)))\n    EXPECTATION NO_ORPHAN_READINGS (VALUE = 0);\n```\n\nFor compound keys, the columns are listed in matching order on both sides. *Find the Bad Rows* shows how to retrieve reading 1017 itself.\n\nReading 1015 was ingested twice:\n\n```sql\nSELECT SNOWFLAKE.CORE.DUPLICATE_COUNT(SELECT READING_ID FROM SENSOR_READINGS) AS DUPLICATED_VALUES,\n       SNOWFLAKE.CORE.UNIQUE_COUNT(SELECT READING_ID FROM SENSOR_READINGS)    AS DISTINCT_VALUES;\n```\n\nReturns 1 and 20. DUPLICATE_COUNT is the number of *values that have duplicates*, not excess rows; UNIQUE_COUNT is the number of *distinct* values, not values occurring once.\n\n| Case | Approach |\n|:-----|:---------|\n| Single-column key | DUPLICATE_COUNT ON (READING_ID) with VALUE = 0 |\n| Grain is a column combination | DUPLICATE_COUNT on a column list; must be attached |\n| Orphaned foreign keys | REFERENTIAL_INTEGRITY_COUNT |\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT\n    ON (READING_ID)\n    EXPECTATION READING_ID_IS_UNIQUE (VALUE = 0);\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.DUPLICATE_COUNT\n    ON (DEVICE_ID, READING_TS)\n    EXPECTATION ONE_READING_PER_DEVICE_PER_TIMESTAMP (VALUE = 0);\n```\n\nBoth measure 1: reading 1015's two copies share a reading ID and a device-and-timestamp pair. NULLs count as a value, so a nullable key reports duplicates once two rows are both NULL.\n\n\u003C!-- ------------------------ --\u003E\n## Volume, Drift and Distribution\n\nSome failures are not bad rows: good data stops arriving, a column is renamed upstream, or a sensor recalibrated to Fahrenheit produces individually plausible readings with a shifted distribution.\n\nROW_COUNT takes no column, so you attach it and read the measurement once an evaluation has run. *Let Snowflake Learn Normal* builds on this association:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT\n    ON ()\n    EXPECTATION ENOUGH_READINGS (VALUE \u003E 15);\n\nSELECT MEASUREMENT_TIME, TABLE_NAME, METRIC_NAME, VALUE\nFROM TABLE(SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS(\n    REF_ENTITY_NAME   =\u003E 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN =\u003E 'TABLE'))\nWHERE METRIC_NAME = 'ROW_COUNT'\nORDER BY MEASUREMENT_TIME DESC;\n```\n\nUntil the first scheduled run, the query returns no rows; evaluating now reports ENOUGH_READINGS at 21. A fixed floor like VALUE \u003E 15 cannot express \"less than usual\"; anomaly detection can.\n\n| Case | Approach | Notes |\n|:-----|:---------|:------|\n| Volume collapse | ROW_COUNT ON () | Prefer anomaly detection over a fixed floor |\n| Schema drift | SCHEMA_CHANGE_COUNT ON () | Measures change since the previous evaluation; needs two evaluations and reports a delta, not a state |\n| Distribution shift | AVG, MEDIAN, STDDEV, APPROX_QUANTILE_99 | Record without an expectation; MEDIAN is exact, APPROX_QUANTILE_50 is cheaper |\n| Text truncation | STRING_LENGTH_MIN/AVG/MAX | The quickest way to catch a truncation regression |\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.SCHEMA_CHANGE_COUNT\n    ON ();\n```\n\nThis association records changes without judging them. At the time of writing, giving SCHEMA_CHANGE_COUNT an expectation makes SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS fail with a syntax error for the whole table, and the persisting form silently record nothing, so read schema changes from the monitoring results instead.\n\nStatistics metrics measure shape rather than validity:\n\n```sql\nSELECT SNOWFLAKE.CORE.AVG(SELECT TEMPERATURE_C FROM SENSOR_READINGS)                AS MEAN_TEMP,\n       SNOWFLAKE.CORE.MEDIAN(SELECT TEMPERATURE_C FROM SENSOR_READINGS)             AS MEDIAN_TEMP,\n       SNOWFLAKE.CORE.STDDEV(SELECT TEMPERATURE_C FROM SENSOR_READINGS)             AS SPREAD_TEMP,\n       SNOWFLAKE.CORE.APPROX_QUANTILE_99(SELECT TEMPERATURE_C FROM SENSOR_READINGS) AS P99_TEMP;\n```\n\n| MEAN_TEMP | MEDIAN_TEMP | SPREAD_TEMP | P99_TEMP |\n|----------:|------------:|------------:|---------:|\n| 6.235 | 4.2 | 5.836 | 13.343 |\n\nMean well above median, with a spread as large as the mean, is the signature of a mixed population: chilled sites and a freezer pooled in one table. Rather than inventing a threshold, attach the metric without an expectation to build history. It will not appear in SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS output:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.AVG\n    ON (TEMPERATURE_C);\n```\n\n\u003C!-- ------------------------ --\u003E\n## Scoping Checks\n\nRules rarely apply to a whole table, and fleet-wide numbers hide per-device failures.\n\nFILTER scopes a check to a subset of rows. A negative battery level is impossible, except that readings flagged BATT_UNKNOWN deliberately carry -1 as a sentinel. Unfiltered, the rule fails on reading 1018 forever:\n\n```sql\nSELECT SNOWFLAKE.CORE.NEGATIVE_COUNT(SELECT BATTERY_PCT FROM SENSOR_READINGS) AS NEGATIVE_BATTERY;\n```\n\nReturns 1. Excluding the sentinel rows by definition, rather than loosening the threshold to tolerate them:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NEGATIVE_COUNT\n    ON (BATTERY_PCT)\n    FILTER (STATUS_CODE = 'OK')\n    EXPECTATION HEALTHY_DEVICES_REPORT_BATTERY (VALUE = 0);\n```\n\nScheduled evaluations apply the filter, so this measures 0 and passes, and would still catch a -1 from a device that claims to be healthy. FILTER comes after the column and before the expectation, and takes multiple conditions. To change or clear the filter later, modify the association rather than adding the metric again:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.NEGATIVE_COUNT\n    ON (BATTERY_PCT)\n    FILTER (STATUS_CODE = 'OK');\n```\n\nAt the time of writing, on-demand evaluation does not apply the filter: SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS reports HEALTHY_DEVICES_REPORT_BATTERY at 1, the unfiltered count. Judge a filtered check by its scheduled results.\n\n| Need | Clause | Notes |\n|:-----|:-------|:------|\n| Check a subset of rows | FILTER (condition) | See [Apply data quality checks to a subset of rows](https://docs.snowflake.com/en/user-guide/data-quality-filter) |\n| Same metric per group, one association | WITHIN GROUP (columns) | Results carry GROUP_BY_INFO; SYSTEM$DATA_METRIC_SCAN accepts WITHIN_GROUP_VALUES |\n| Wide grouping | GROUP LIMIT (number) | Between 1 and 1000; group by device or status, not by reading ID |\n\nWITHIN GROUP computes the metric separately for each group from a single association, with no need for one association per group value. Here, every device must report humidity:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.NULL_COUNT\n    ON (HUMIDITY_PCT)\n    WITHIN GROUP (DEVICE_ID)\n    GROUP LIMIT 500\n    EXPECTATION EVERY_DEVICE_REPORTS_HUMIDITY (VALUE = 0);\n```\n\nEvaluating now returns one row per device, each identified in GROUP_BY_VALUES. A grouped check raises at most one notification per evaluation, based on the worst group. WITHIN GROUP is not supported for FRESHNESS, REFERENTIAL_INTEGRITY_COUNT, or schema-level associations, and it disables anomaly detection on that association.\n\nSee [Apply data quality checks by group](https://docs.snowflake.com/en/user-guide/data-quality-group-by).\n\n\u003C!-- ------------------------ --\u003E\n## Write Your Own Check\n\nWhen no system metric fits, you can write a DMF: a SQL function that takes a table argument and returns a number. This one counts readings with humidity outside a plausible range:\n\n```sql\nCREATE OR REPLACE DATA METRIC FUNCTION IMPLAUSIBLE_HUMIDITY(\n    ARG_T TABLE (ARG_HUMIDITY NUMBER)\n)\nRETURNS NUMBER\nAS\n$$\n    SELECT COUNT(*) FROM ARG_T WHERE ARG_HUMIDITY \u003C 0 OR ARG_HUMIDITY \u003E 100\n$$;\n\nSELECT IMPLAUSIBLE_HUMIDITY(SELECT HUMIDITY_PCT FROM SENSOR_READINGS) AS OUT_OF_RANGE;\n\nALTER TABLE SENSOR_READINGS\n    ADD DATA METRIC FUNCTION IMPLAUSIBLE_HUMIDITY\n    ON (HUMIDITY_PCT)\n    EXPECTATION HUMIDITY_IN_RANGE (VALUE = 0);\n```\n\nThe direct call returns 0. Calling a custom metric directly before attaching it is worthwhile: column validation inside a TABLE(...) argument is weaker than for single-column metrics, so mistakes in multi-table signatures can surface only at evaluation time. Custom metrics can take more than one table argument, and can use SNOWFLAKE.CORE.DATA_METRIC_SCHEDULE_TIME() for freshness rules the built-in cannot express. Check the catalog first: REFERENTIAL_INTEGRITY_COUNT and ACCEPTED_VALUES replace the two most commonly hand-rolled metrics. See [Custom data metric functions](https://docs.snowflake.com/en/user-guide/data-quality-custom-dmfs).\n\n\u003C!-- ------------------------ --\u003E\n## Cover a Whole Schema in One Statement\n\nVolume and freshness apply to any table, so you can attach them at the schema level. Snowflake creates an association for every supported table-like object in the schema, including ones added later. Three metrics work this way: ROW_COUNT, FRESHNESS, and TABLE_SIZE_BYTES. TABLE_SIZE_BYTES covers tables only and does not support anomaly detection.\n\n```sql\nALTER SCHEMA PUBLIC\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT ON ()\n    EXCLUDE_TABLE_TYPES = ('VIEW', 'MATERIALIZED_VIEW');\n\nALTER SCHEMA PUBLIC\n    ADD DATA METRIC FUNCTION SNOWFLAKE.CORE.FRESHNESS ON ();\n```\n\nZero-argument FRESHNESS skips views and external tables on its own. A schema's own schedule defaults to every 60 minutes and cannot be trigger-based; a table's schedule overrides it. The references tell schema-level from table-level associations; an association created at the schema level is detached at the schema level:\n\n```sql\nSELECT REF_ENTITY_NAME, METRIC_NAME, LEVEL, EXCLUDE_TABLE_TYPES\nFROM SNOWFLAKE.ACCOUNT_USAGE.DATA_METRIC_FUNCTION_REFERENCES\nWHERE REF_DATABASE_NAME = 'DQ_EXAMPLES';\n```\n\nACCOUNT_USAGE views lag, so new associations can take a few hours to appear here. One statement across two hundred tables creates two hundred scheduled evaluations, so the schema's schedule deserves thought:\n\n```sql\nALTER SCHEMA PUBLIC SET DATA_METRIC_SCHEDULE = '1440 MINUTE';\n```\n\nSee [Monitor the data quality of a schema](https://docs.snowflake.com/en/user-guide/data-quality-schema-level).\n\n\u003C!-- ------------------------ --\u003E\n## Let Snowflake Propose the Checks\n\nCortex Data Quality reads a table's metadata and usage patterns and suggests checks. It runs inside Snowflake using AI_COMPLETE, so data does not leave the account, and it respects the caller's access control. This is Snowsight-only.\n\nThe role using it needs:\n\n- **EXECUTE DATA METRIC FUNCTION** on the account, and the **SNOWFLAKE.DATA_METRIC_USER** database role.\n- **The SNOWFLAKE.CORTEX_USER database role**, granted to PUBLIC by default.\n- **Access to the mistral-7b and llama3.1-8b models.** Model access is governed by model role-based access control. By default, the CORTEX-MODEL-ROLE-ALL application role is granted to SNOWFLAKE.PUBLIC, so every role can use every model and suggestions work out of the box. If an administrator has restricted model access, the role needs the application roles for those two models. The data quality setup page still describes this requirement in terms of the CORTEX_MODELS_ALLOWLIST parameter, which is being deprecated in favor of model access control. See [Model access control](https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-privileges-and-access).\n\nTo see which model roles PUBLIC holds:\n\n```sql\nSHOW GRANTS TO APPLICATION ROLE SNOWFLAKE.PUBLIC;\n```\n\nSuggestions start from the table's **Data quality** tab: choose **Generate with Cortex Data Quality**, which is a button on a table with no checks yet and an option under **Add quality check** once it has some.\n\n![The Add quality check menu, offering Generate with Cortex Data Quality and Setup manually](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/add_check_menu.png?v=6989d1d4) Expected values can be edited before accepting; an accepted suggestion is a real, billed, scheduled check. Generating suggestions is billed separately, as AI Services, plus the warehouse that runs Snowsight.\n\n![Cortex suggested data quality checks for SENSOR_READINGS, each with an expected result and a reason](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/suggested_checks.png?v=6989d1d4)\n\nSuggestions are a first draft: the model sees metadata and usage, not your business rules, so review each one before accepting it.\n\nAccess is controlled through CORTEX_USER, which is not specific to data quality: revoking it from PUBLIC removes **every** Snowflake Cortex feature from users who received it through PUBLIC. To restrict suggestions, an administrator revokes it and then grants it back to the roles that should keep Cortex:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\nREVOKE DATABASE ROLE SNOWFLAKE.CORTEX_USER FROM ROLE PUBLIC;\n\nCREATE ROLE IF NOT EXISTS CORTEX_ACCESS_ROLE;\nGRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE CORTEX_ACCESS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Let Snowflake Learn Normal\n\nThe right row count for a Tuesday is not the right row count for a bank holiday. Anomaly detection learns the pattern from history and flags departures from it. It is available on ROW_COUNT and FRESHNESS. It can be switched on when a metric is added, by including ANOMALY_DETECTION = TRUE in the ADD statement, or on an existing association. Here it goes on the ROW_COUNT association from *Volume, Drift and Distribution*:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.ROW_COUNT ON ()\n    SET ANOMALY_DETECTION = TRUE;\n```\n\nBefore it flags anything, the algorithm needs a **training period**: at least two weeks of measurements for a metric that runs frequently, or at least two data points for one that runs infrequently or on a trigger. Until then, the references report TRAINING_IN_PROGRESS:\n\n```sql\nSELECT METRIC_NAME, ANOMALY_DETECTION_STATUS\nFROM TABLE(INFORMATION_SCHEMA.DATA_METRIC_FUNCTION_REFERENCES(\n    REF_ENTITY_NAME   =\u003E 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    REF_ENTITY_DOMAIN =\u003E 'table'))\nWHERE METRIC_NAME = 'ROW_COUNT';\n```\n\nDetection runs on the measurements the association's schedule produces, so the schedule decides how quickly history accumulates. SENSITIVITY ('LOW', 'MEDIUM' or 'HIGH', default MEDIUM) trades false positives against missed anomalies, and is set with MODIFY in the same way. Anomaly detection is also available at the schema level, by adding ANOMALY_DETECTION = TRUE to the schema-level ADD. Expectations suit rules true by definition; anomaly detection suits quantities with a normal range you could not write down. See [Detecting anomalies in data quality](https://docs.snowflake.com/en/user-guide/data-quality-anomaly).\n\n![The ROW_COUNT check under Volume, establishing its baseline during the training period](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/anomaly_baseline.png?v=6989d1d4)\n\n\u003C!-- ------------------------ --\u003E\n## Find the Bad Rows\n\nSYSTEM$DATA_METRIC_SCAN returns the records that fail an attached check, ready to quarantine or send back to the source team. This one uses the referential-integrity check from *Integrity and Uniqueness*:\n\n```sql\nSELECT * FROM TABLE(SYSTEM$DATA_METRIC_SCAN(\n    REF_ENTITY_NAME =\u003E 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    METRIC_NAME     =\u003E 'SNOWFLAKE.CORE.REFERENTIAL_INTEGRITY_COUNT',\n    ARGUMENT_NAME   =\u003E 'DEVICE_ID'));\n```\n\nIt returns one row, reading 1017, with all nine columns; the four that matter here:\n\n| READING_ID | DEVICE_ID | TEMPERATURE_C | STATUS_CODE |\n|-----------:|:----------|--------------:|:------------|\n| 1017 | DEV-9999 | 5.20 | OK |\n\nFor ACCEPTED_VALUES, the same Boolean expression goes in ARGUMENT_EXPRESSION:\n\n```sql\nSELECT * FROM TABLE(SYSTEM$DATA_METRIC_SCAN(\n    REF_ENTITY_NAME      =\u003E 'DQ_EXAMPLES.PUBLIC.SENSOR_READINGS',\n    METRIC_NAME          =\u003E 'SNOWFLAKE.CORE.ACCEPTED_VALUES',\n    ARGUMENT_NAME        =\u003E 'STATUS_CODE',\n    ARGUMENT_EXPRESSION  =\u003E 'STATUS_CODE IN (''OK'', ''WARN'')'));\n```\n\nThat returns reading 1018. AT_TIMESTAMP scans the table as of a point in time, and WITHIN_GROUP_VALUES narrows a grouped association to one group.\n\nMeasurements live in SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_RESULTS (shown in *Volume, Drift and Distribution*); there is no ACCOUNT_USAGE version. For \"what is failing\", read verdicts directly rather than joining measurements to expectation definitions:\n\n```sql\nSELECT MEASUREMENT_TIME, METRIC_NAME, EXPECTATION_NAME, VALUE, EXPECTATION_VIOLATED\nFROM SNOWFLAKE.LOCAL.DATA_QUALITY_MONITORING_EXPECTATION_STATUS\nWHERE TABLE_DATABASE = 'DQ_EXAMPLES'\n  AND TABLE_SCHEMA   = 'PUBLIC'\n  AND TABLE_NAME     = 'SENSOR_READINGS'\nORDER BY MEASUREMENT_TIME DESC;\n```\n\nThis returns rows only once an evaluation has been recorded, by the schedule or by the persisting evaluation shown in *Anatomy of a Check*.\n\nIn Snowsight, the table's **Data quality** tab shows the same, and the account-wide monitoring dashboard (preview) rolls it up across databases. **Impacted Assets** on the table's page shows what reads from it.\n\nOn a check's side panel, **View failed records** opens a new SQL file in your workspace containing the SYSTEM$DATA_METRIC_SCAN query for that check, ready to run or adapt.\n\n![The account-wide data quality monitoring dashboard, listing failed expectations on SENSOR_READINGS](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/dq_dashboard.png?v=6989d1d4)\n\nSee [Remediation of data quality issues](https://docs.snowflake.com/en/user-guide/data-quality-fixing) and [Using the data quality monitoring dashboard](https://docs.snowflake.com/en/user-guide/data-quality-centralized-dashboard).\n\n\u003C!-- ------------------------ --\u003E\n## Get Told, Don't Watch\n\nA notification needs a destination: email, or a webhook for Slack and similar:\n\n```sql\nCREATE NOTIFICATION INTEGRATION DQ_EMAIL_INT\n    TYPE = EMAIL\n    ENABLED = TRUE\n    ALLOWED_RECIPIENTS = ('you@example.com');\n\nCREATE OR REPLACE SECRET SLACK_WEBHOOK_SECRET\n    TYPE = GENERIC_STRING\n    SECRET_STRING = 'T00000000/B00000000/XXXXXXXXXXXXXXXXXXXXXXXX';\n\nCREATE OR REPLACE NOTIFICATION INTEGRATION DQ_SLACK_INT\n    TYPE = WEBHOOK\n    ENABLED = TRUE\n    WEBHOOK_URL = 'https://hooks.slack.com/services/SNOWFLAKE_WEBHOOK_SECRET'\n    WEBHOOK_SECRET = DQ_EXAMPLES.PUBLIC.SLACK_WEBHOOK_SECRET\n    WEBHOOK_BODY_TEMPLATE = '{\"text\": \"SNOWFLAKE_WEBHOOK_MESSAGE\"}'\n    WEBHOOK_HEADERS = ('Content-Type' = 'application/json');\n```\n\nThe email recipients must be verified users in your account. Notifications are then switched on for the database, as YAML in a dollar-quoted string:\n\n```sql\nALTER DATABASE DQ_EXAMPLES SET DATA_QUALITY_MONITORING_SETTINGS =\n$$\nnotification:\n  enabled: TRUE\n  email_recipients: [ 'you@example.com' ]\n  integrations:\n    - DQ_SLACK_INT\n  cooldown_hours: 24\n  metadata_included: TRUE\n$$;\n```\n\nEach violated expectation then arrives as its own entry in a notification email:\n\n![A data quality notification email listing violated expectations on SENSOR_READINGS](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/notification_email.png?v=6989d1d4)\n\nWithout cooldown_hours, an hourly check against a week-long violation sends 168 identical messages. metadata_included adds the table, metric and measured value. Settings apply **per database**, and a single association can opt out. Here it is the AVG association from *Volume, Drift and Distribution*:\n\n```sql\nALTER TABLE SENSOR_READINGS\n    MODIFY DATA METRIC FUNCTION SNOWFLAKE.CORE.AVG ON (TEMPERATURE_C)\n    SET DATA_QUALITY_NOTIFICATION = FALSE;\n```\n\n### An alert from a template\n\nNative notifications are settings on a database, not an object: there is nothing to schedule, scope to one table, pause or audit on its own. When you want those, create a Snowflake **alert** from the DQ_EXPECTATION_VIOLATIONS template. The template supplies the condition, which reads expectation status, and the notification; you choose the scope, the schedule and the destination. Its variables, defaults and allowed values are listed here:\n\n```sql\nSELECT SYSTEM$GET_ALERT_TEMPLATE('DQ_EXPECTATION_VIOLATIONS');\n```\n\nThis alert watches every expectation on SENSOR_READINGS and emails through the integration created above:\n\n```sql\nCREATE OR ALTER ALERT READINGS_EXPECTATION_ALERT\n    FROM TEMPLATE DQ_EXPECTATION_VIOLATIONS\n    WAREHOUSE = DQ_EXAMPLES_WH\n    SCHEDULE = '60 MINUTE'\n    COMMENT = 'Emails when any expectation on SENSOR_READINGS is violated'\n    TEMPLATE_PARAMS = '{\n      \"template_variables\": {\n        \"SCOPE_ACTIVE\": \"TABLE\",\n        \"SCOPE_DATABASE\": \"DQ_EXAMPLES\",\n        \"SCOPE_SCHEMA\": \"PUBLIC\",\n        \"SCOPE_TABLE\": \"SENSOR_READINGS\",\n        \"NOTIFICATION_MODE\": \"EMAIL\",\n        \"EMAIL_NOTIFICATION_INTEGRATION\": \"DQ_EMAIL_INT\"\n      },\n      \"notification_config\": {\n        \"notification_integration\": \"DQ_EMAIL_INT\",\n        \"email_config\": { \"toAddress\": [\"you@example.com\"] }\n      }\n    }';\n```\n\nFor email, the integration goes in two places: EMAIL_NOTIFICATION_INTEGRATION, which the alert's action reads, and notification_config, which carries the recipients. EXPECTATION_NAMES narrows the alert to a comma-separated list, and NOTIFICATION_MODE also accepts WEBHOOK and IN_APP. The alert is created suspended. To test it without waiting, first record a verdict, so the expectation status has something recent:\n\n```sql\nSELECT * FROM TABLE(SYSTEM$EVALUATE_DATA_QUALITY_EXPECTATIONS_PERSIST_RESULT('DQ_EXAMPLES.PUBLIC.SENSOR_READINGS'));\n```\n\nThe verdict takes a few seconds to reach the status. Then run the alert once:\n\n```sql\nEXECUTE ALERT READINGS_EXPECTATION_ALERT;\n```\n\nA minute later, check the run and the email:\n\n```sql\nSELECT SCHEDULED_TIME, STATE, SQL_ERROR_MESSAGE\nFROM TABLE(INFORMATION_SCHEMA.ALERT_HISTORY(\n    SCHEDULED_TIME_RANGE_START =\u003E DATEADD('HOUR', -1, CURRENT_TIMESTAMP())))\nWHERE NAME = 'READINGS_EXPECTATION_ALERT'\nORDER BY SCHEDULED_TIME DESC;\n\nSELECT CREATED, STATUS, ERROR_MESSAGE\nFROM TABLE(INFORMATION_SCHEMA.NOTIFICATION_HISTORY(\n    START_TIME =\u003E DATEADD('HOUR', -1, CURRENT_TIMESTAMP()),\n    INTEGRATION_NAME =\u003E 'DQ_EMAIL_INT'))\nORDER BY CREATED DESC;\n```\n\nThe alert should read TRIGGERED, because several expectations on SENSOR_READINGS are violated, and the email SUCCESS. The template's condition fires on any violation measured since its last successful run (on the first run, the past hour), whether recorded by the schedule or by the call above. ALTER ALERT READINGS_EXPECTATION_ALERT RESUME starts the hourly schedule.\n\n![The email sent by READINGS_EXPECTATION_ALERT, listing violated expectations on SENSOR_READINGS](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/comprehensive-guide-to-data-quality-monitoring/alert_email.png?v=6989d1d4)\n\nThe alert's owner role needs EXECUTE ALERT on the account, USAGE on the warehouse (or EXECUTE MANAGED ALERT for a serverless alert, which omits WAREHOUSE), USAGE on the email integration, and the SNOWFLAKE.DATA_QUALITY_MONITORING_VIEWER application role to read expectation status. Snowflake publishes templates for anomaly detection (DQ_ANOMALY_DETECTION) and for other products; SYSTEM$LIST_ALERT_TEMPLATES lists them.\n\nSee [Sending notifications for data quality issues](https://docs.snowflake.com/en/user-guide/data-quality-notifications) and [Creating an alert from a template with SQL](https://docs.snowflake.com/en/user-guide/alerts#label-alerts-create-from-template).\n\n\u003C!-- ------------------------ --\u003E\n## Operating Checks in Production\n\n### Where to put your checks\n\nThe best place to check is where the defect exists, not where it hurts. Reading 1017 would be dropped by a silver-layer inner join, so a gold check passes *because* the bad row was discarded.\n\n| Layer | Check for | Why here |\n|:------|:----------|:---------|\n| Bronze (raw) | Nulls, formats, duplicates, orphans, clock skew, volume, freshness | The only place the defect still exists |\n| Silver (conformed) | Referential integrity, grain uniqueness, row counts against bronze | Where joins and dedup happen, so where rows go missing |\n| Gold (serving) | Business invariants and totals | Catches logic errors row-level checks cannot see |\n\n### Checks as code\n\nFor anything you intend to keep, checks can live in the same version-controlled definition as the table. Snowflake's declarative deployment model (DCM Projects) supports data metric functions and expectations as first-class definitions. Attaching a metric to a table you do not own needs an explicit executing role, and the deployment tooling can run every expectation as a **test step**, so a deployment fails when the data does not satisfy its own rules. A threshold change then shows up as a reviewable diff. For a worked example, with expectations declared with ATTACH DATA METRIC FUNCTION and run with TEST ALL as part of deployment, see [Build Data Pipelines with Snowflake DCM Projects](https://www.snowflake.com/en/developers/guides/build-data-pipelines-with-snowflake-dcm-projects/). For the syntax, see the [DCM Projects file reference](https://docs.snowflake.com/en/user-guide/dcm-projects/dcm-projects-files).\n\n\u003C!-- ------------------------ --\u003E\n## What It Costs\n\nScheduled evaluations run on serverless compute and bill under the **Data Quality Monitoring** category. Cost scales with associations × evaluation frequency × work per evaluation; a schema-level attachment across two hundred tables on an hourly schedule is 4,800 evaluations a day. Collecting the results into the event table bills separately, as **Logging**. Creating a metric and suspended schedules are not billed, and a direct call in a SELECT bills only the warehouse that runs it. Spend is visible here:\n\n```sql\nSELECT * FROM SNOWFLAKE.ACCOUNT_USAGE.DATA_QUALITY_MONITORING_USAGE_HISTORY\nORDER BY START_TIME DESC;\n```\n\nAcross an organization, METERING_DAILY_HISTORY carries the same spend with SERVICE_TYPE = 'DATA_QUALITY_MONITORING'. Costs stay low when cadence matches how fast you would act, TRIGGER_ON_CHANGES is used where evaluating on data arrival fits, and schedules are suspended with an empty string rather than UNSET. Alerts, like the template alert, bill their own warehouse each time they run.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nIf you tried the examples, dropping the database removes its tables, their associations, custom metrics, the secret, the alert, and the database's monitoring settings. The warehouse and the notification integrations live at account level and outlive the database:\n\n```sql\nDROP DATABASE IF EXISTS DQ_EXAMPLES;\nDROP WAREHOUSE IF EXISTS DQ_EXAMPLES_WH;\nDROP INTEGRATION IF EXISTS DQ_EMAIL_INT;\nDROP INTEGRATION IF EXISTS DQ_SLACK_INT;\n```\n\nTo keep the objects but stop the billing, suspend everything that runs on a schedule: the alert and the table and schema schedules.\n\n```sql\nALTER ALERT DQ_EXAMPLES.PUBLIC.READINGS_EXPECTATION_ALERT SUSPEND;\nALTER TABLE DQ_EXAMPLES.PUBLIC.SENSOR_READINGS SET DATA_METRIC_SCHEDULE = '';\nALTER TABLE DQ_EXAMPLES.PUBLIC.SENSOR_READINGS_V2 SET DATA_METRIC_SCHEDULE = '';\nALTER SCHEMA DQ_EXAMPLES.PUBLIC SET DATA_METRIC_SCHEDULE = '';\n```\n\nIf you revoked CORTEX_USER from PUBLIC in *Let Snowflake Propose the Checks* and want the default back:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\nGRANT DATABASE ROLE SNOWFLAKE.CORTEX_USER TO ROLE PUBLIC;\nDROP ROLE IF EXISTS CORTEX_ACCESS_ROLE;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion and Resources\n\nData quality monitoring in Snowflake is a small vocabulary applied with judgment. A metric measures, an expectation judges, a schedule decides how often, and everything else is choosing well.\n\n### What to take away\n\n- **Pick the metric from the column's shape.** The decision table in *Choosing a Check* is the whole method.\n- **Know which metrics can be called directly.** Column-argument metrics answer instantly in a SELECT; zero-argument ones must be attached.\n- **One association per metric and column.** Change a check with MODIFY; adding it again is not an update.\n- **One check is rarely enough.** Freshness alone was defeated by a single future-dated row.\n- **Check where the defect exists**, not where it hurts. Gold passing is not evidence.\n- **Scope with FILTER, don't loosen thresholds.**\n- **Assert rules; learn ranges.** Expectations for what is true by definition, anomaly detection for normal ranges.\n- **Notify with a cooldown**, or people will filter the alerts.\n- **Use a template when you need an alert object.** It brings its own scope, schedule and history, without writing the condition yourself.\n- **Keep checks in code**, so a threshold change is reviewable.\n\n### Resources\n\n- [Introduction to data quality checks](https://docs.snowflake.com/en/user-guide/data-quality-intro)\n- [System data metric functions](https://docs.snowflake.com/en/user-guide/data-quality-system-dmfs)\n- [Custom data metric functions](https://docs.snowflake.com/en/user-guide/data-quality-custom-dmfs)\n- [Use SQL to set up data quality checks](https://docs.snowflake.com/en/user-guide/data-quality-working)\n- [Use SQL to work with expectations](https://docs.snowflake.com/en/user-guide/data-quality-expectations)\n- [Apply data quality checks to a subset of rows](https://docs.snowflake.com/en/user-guide/data-quality-filter)\n- [Apply data quality checks by group](https://docs.snowflake.com/en/user-guide/data-quality-group-by)\n- [Monitor the data quality of a schema](https://docs.snowflake.com/en/user-guide/data-quality-schema-level)\n- [Detecting anomalies in data quality](https://docs.snowflake.com/en/user-guide/data-quality-anomaly)\n- [Sending notifications for data quality issues](https://docs.snowflake.com/en/user-guide/data-quality-notifications)\n- [Remediation of data quality issues](https://docs.snowflake.com/en/user-guide/data-quality-fixing)\n- [Use Snowsight to set up data quality checks](https://docs.snowflake.com/en/user-guide/data-quality-ui-setup)\n- [Using the data quality monitoring dashboard](https://docs.snowflake.com/en/user-guide/data-quality-centralized-dashboard)\n- [Access control for data quality](https://docs.snowflake.com/en/user-guide/data-quality-access-control)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-dfbe08c293","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-1b0da45a01",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-62bd129b56","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-12",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a73559211f","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-a20de095da",":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-4616d2b85f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-948a1aad6a","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-a4f7766fb9","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/comprehensive-guide-to-data-quality-monitoring",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EThe Example Data\u003C/h2\u003E","\u003Ch2\u003EAnatomy of a Check\u003C/h2\u003E","\u003Ch2\u003EProfile Before You Check\u003C/h2\u003E","\u003Ch2\u003EChoosing a Check\u003C/h2\u003E","\u003Ch2\u003ECompleteness and Validity\u003C/h2\u003E","\u003Ch2\u003ETimeliness\u003C/h2\u003E","\u003Ch2\u003EIntegrity and Uniqueness\u003C/h2\u003E","\u003Ch2\u003EVolume, Drift and Distribution\u003C/h2\u003E","\u003Ch2\u003EScoping Checks\u003C/h2\u003E","\u003Ch2\u003EWrite Your Own Check\u003C/h2\u003E","\u003Ch2\u003ECover a Whole Schema in One Statement\u003C/h2\u003E","\u003Ch2\u003ELet Snowflake Propose the Checks\u003C/h2\u003E","\u003Ch2\u003ELet Snowflake Learn Normal\u003C/h2\u003E","\u003Ch2\u003EFind the Bad Rows\u003C/h2\u003E","\u003Ch2\u003EGet Told, Don't Watch\u003C/h2\u003E","\u003Ch2\u003EOperating Checks in Production\u003C/h2\u003E","\u003Ch2\u003EWhat It Costs\u003C/h2\u003E","\u003Ch2\u003EClean Up\u003C/h2\u003E","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-29afc416a4","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/comprehensive-guide-to-data-quality-monitoring",":type":"snowflake-site/components/quickstart/quickstart-button","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-9511d6547a","title":"Page CSS","cssUrl":"/content/snowflake-site/global/en/developers/guides/comprehensive-guide-to-data-quality-monitoring/_jcr_content/root/responsivegrid/markup_editor.eb24a609da50a43b.css","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-891ae6b42e",":type":"snowflake-site/components/modal/modal-container",":items":{},":itemsOrder":[]},"experiencefragment-footer":{"id":"experiencefragment-073722342c","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-380c693817",":type":"snowflake-site/components/container",":items":{"container_copy":{"additionalClasses":"sf-footer__inner","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-b42176bb3d",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-7f50331ee7","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-2415b0e984",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_1622723482":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-59e74d8443",":type":"snowflake-site/components/container",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-133788a7c1",":type":"snowflake-site/components/container",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-ef8a9a43b1",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-e34c200931","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-v2-1c6345b0c7","marketoForm":{"edit":false,"formId":"45871","successUrl":null,"script":null,"hidden":null,"values":null},"partnerCampaignPage":false,"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":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-5e539b1d0b",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-1074813610","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=\"/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=\"/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/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-6e492855a7","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=\"/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"container_copy_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-2b8e4813af",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-3048546d38","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=\"/en/solutions/industries/technology/\"\u003ETechnology\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-a46383a429",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-d5043bbd29","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=\"/en/company/overview/about-snowflake/\"\u003EAbout Snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/company/overview/leadership-and-board/\"\u003ELeadership & Board\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://careers.snowflake.com/us/en\" rel=\"noopener noreferrer\"\u003ECareers\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://investors.snowflake.com/overview/default.aspx\" rel=\"noopener noreferrer\"\u003EInvestor Relations\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://trust.snowflake.com/\" rel=\"noopener noreferrer\"\u003ETrust Center\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://www.snowflake.com/brand-guidelines/\" rel=\"noopener noreferrer\"\u003EBrand Guidelines\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/contact/\"\u003EContact\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/news/\"\u003ENewsroom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/company/overview/esg/\"\u003EEnvironmental, Social & Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/frontier-engineering/\"\u003EFrontier Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/company/overview/snowflake-ventures/\"\u003ESnowflake Ventures\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/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 27\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E \u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy_":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-64e93b3c1b",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-9910b5d237","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=\"/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=\"/en/fundamentals/\"\u003EFundamentals\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/resources/learn/training/\"\u003ETraining\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/resources/learn/certifications/\"\u003ECertifications\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://learn.snowflake.com/en/\" rel=\"noopener noreferrer\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/developers/guides/\"\u003EDeveloper Guides\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca target=\"_blank\" href=\"https://docs.snowflake.com/\" rel=\"noopener noreferrer\"\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","columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-5af11762b4",":type":"snowflake-site/components/container",":items":{"container_112062425":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-8a85644b89",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-d93635febe","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-ad8b368326",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer__legal-container","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_16360":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-b16412be83",":type":"snowflake-site/components/container",":items":{"container":{"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-fc2f5648ca",":type":"snowflake-site/components/container",":items":{"image":{"id":"image-69778a470b","additionalClasses":"sf-footer__logo","isLcpImage":false,"src":"/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","lazyEnabled":true,"imageLink":{"valid":true,"url":"/en/"},"alt":"Snowflake logo","height":"64","width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"text_copy_copy_16360":{"id":"text-31ebbc8953","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-bfc1b2ef59","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-0b582a529c","title":"New css","cssContent":".snowflake-flexible-column-container-items:has(.snowflake-experience-fragment:empty){display:none !important}.snowflake-image-container img{background-color:transparent}div.snowflake-person-chip-avatar{width:80px !important}#snowflake-blog-template-main-container .snowflake-quote-item-card{margin-top:40px}#snowflake-blog-template-main-container .aem-GridColumn:has(.vertical-video){background-color:#000;border-radius:16px;overflow:hidden}#snowflake-blog-template-main-container .is-vertical img{max-width:400px;margin-left:auto;margin-right:auto}#snowflake-blog-template-main-container .vertical-video{max-width:240px;margin-left:auto;margin-right:auto}@media screen and (min-width:1367px){.dynamic .heading-1-v2 .snowflake-title-v2-line{font-size:72px !important;line-height:60px !important}}.snowflake-flexible-column-container-items-alignment-match-height .download-card,.snowflake-flexible-column-container-items-alignment-match-height .download-card\u003E.container{height:100%}.download-card div.code-toolbar\u003E.toolbar .copy-to-clipboard-button{background-color:white;border:1px solid #a9e1f6;margin-right:4px;top:6px;border-radius:16px;height:26px;width:40px}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:before{content:'';background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23249EDC;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23249EDC;'%3E%3C/path%3E%3C/svg%3E\");background-size:auto 65%;background-position:center;background-repeat:no-repeat;top:0;left:0;width:100%;height:100%}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:hover:before{background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23fff;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23fff;'%3E%3C/path%3E%3C/svg%3E\")}.download-card\u003Ediv{background-color:#fff;border:1px solid #ccc;border-radius:8px;padding:24px}.download-chip__headline{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:16px}.download-chip{padding:8px 12px !important;border-radius:4px;transition:300ms ease background-color}.download-chip .black-blue-text-color .snowflake-title-v2-line{color:#000 !important;padding-right:24px;font-family:'Lato',sans-serif;font-size:14px !important;font-weight:500 !important}.download-chip .black-blue-text-color .snowflake-title-v2-line:not(:first-child){opacity:.6;font-style:italic !important}.download-chip .snowflake-content-chip-button{display:none}.download-chip.is-external-link{background-size:16px 16px;background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='15' viewBox='0 0 15 15' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1.06055 13.0607L11.8605 2.26067M13.0605 10.6607V1.06067H3.46055' stroke='%23249EDC' stroke-width='2.12132' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-chip{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_883_7979)'%3E%3Cpath d='M3.375 16.875H14.625' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M9 1.125V11.25' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M4.5 7.875L9 12.375L13.5 7.875' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/g%3E%3Cdefs%3E%3CclipPath id='clip0_883_7979'%3E%3Crect width='18' height='18' fill='white'/%3E%3C/clipPath%3E%3C/defs%3E%3C/svg%3E%0A\");background-size:24px auto;background-repeat:no-repeat;background-position:calc(100% - 12px) center}.download-chip__headline{display:flex;gap:16px;flex-direction:row !important;flex-wrap:nowrap}.download-chip__headline::before{content:'';display:inline-block;width:24px;height:24px;background-position:center;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='21' viewBox='0 0 21 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 9.89999C8.13657 9.89999 8.74702 9.64713 9.19711 9.19704C9.64719 8.74696 9.90005 8.13651 9.90005 7.49999V2.69999C9.90005 2.06347 9.64719 1.45302 9.19711 1.00293C8.74702 .552844 8.13657 .299988 7.50005 .299988H2.70005C2.06353 .299988 1.45308 .552844 1.00299 1.00293C.552905 1.45302 .300049 2.06347 .300049 2.69999V7.49999C.300049 8.13651 .552905 8.74696 1.00299 9.19704C1.45308 9.64713 2.06353 9.89999 2.70005 9.89999H7.50005ZM7.50005 7.49999H2.70005V2.69999H7.50005V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 20.3C8.13657 20.3 8.74702 20.0472 9.19711 19.5971C9.64719 19.147 9.90005 18.5365 9.90005 17.9V13.1C9.90005 12.4635 9.64719 11.853 9.19711 11.403C8.74702 10.9529 8.13657 10.7 7.50005 10.7H2.70005C2.06353 10.7 1.45308 10.9529 1.00299 11.403C.552905 11.853 .300049 12.4635 .300049 13.1V17.9C.300049 18.5365 .552905 19.147 1.00299 19.5971C1.45308 20.0472 2.06353 20.3 2.70005 20.3H7.50005ZM7.50005 17.9H2.70005V13.1H7.50005V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 9.89999C18.5366 9.89999 19.147 9.64713 19.5971 9.19704C20.0472 8.74696 20.3001 8.13651 20.3001 7.49999V2.69999C20.3001 2.06347 20.0472 1.45302 19.5971 1.00293C19.147 .552844 18.5366 .299988 17.9001 .299988H13.1001C12.4636 .299988 11.8531 .552844 11.403 1.00293C10.9529 1.45302 10.7001 2.06347 10.7001 2.69999V7.49999C10.7001 8.13651 10.9529 8.74696 11.403 9.19704C11.8531 9.64713 12.4636 9.89999 13.1001 9.89999H17.9001ZM17.9001 7.49999H13.1001V2.69999H17.9001V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 20.3C18.5366 20.3 19.147 20.0472 19.5971 19.5971C20.0472 19.147 20.3001 18.5365 20.3001 17.9V13.1C20.3001 12.4635 20.0472 11.853 19.5971 11.403C19.147 10.9529 18.5366 10.7 17.9001 10.7H13.1001C12.4636 10.7 11.8531 10.9529 11.403 11.403C10.9529 11.853 10.7001 12.4635 10.7001 13.1V17.9C10.7001 18.5365 10.9529 19.147 11.403 19.5971C11.8531 20.0472 12.4636 20.3 13.1001 20.3H17.9001ZM17.9001 17.9H13.1001V13.1H17.9001V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat}.download-chip__headline.is-cli::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M4 17L10 11L4 5' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M12 19H20' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-card pre[class*=language-]{padding:8px 12px;background-color:var(--ui-background-05);overflow:hidden}.download-chip__headline.is-windows,.download-chip__headline.is-mac{gap:12px}.download-chip__headline.is-windows::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='4875' height='4875' viewBox='0 0 4875 4875' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_122_201)'%3E%3Cpath d='M0 0H2311V2310H0V0ZM2564 0H4875V2310H2564V0ZM0 2564H2311V4875H0V2564ZM2564 2564H4875V4875H2564' fill='%23000'/%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-mac::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg version='1.1' id='Layer_1' xmlns:x='ns_extend;' xmlns:i='ns_ai;' xmlns:graph='ns_graphs;' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' x='0' y='0' viewBox='0 0 41.5 51' style='enable-background:new 0 0 41.5 51;' xml:space='preserve'%3E%3Cmetadata%3E%3Csfw xmlns='ns_sfw;'%3E%3Cslices%3E%3C/slices%3E%3CsliceSourceBounds bottomLeftOrigin='true' height='51' width='41.5' x='166.1' y='-208.1'%3E%3C/sliceSourceBounds%3E%3C/sfw%3E%3C/metadata%3E%3Cg%3E%3Cpath d='M40.2,17.4c-3.4,2.1-5.5,5.7-5.5,9.7c0,4.5,2.7,8.6,6.8,10.3c-.8,2.6-2,5-3.5,7.2c-2.2,3.1-4.5,6.3-7.9,6.3s-4.4-2-8.4-2 c-3.9,0-5.3,2.1-8.5,2.1s-5.4-2.9-7.9-6.5C2,39.5,.1,33.7,0,27.6c0-9.9,6.4-15.2,12.8-15.2c3.4,0,6.2,2.2,8.3,2.2 c2,0,5.2-2.3,9-2.3C34.1,12.2,37.9,14.1,40.2,17.4z M28.3,8.1C30,6.1,30.9,3.6,31,1c0-.3,0-.7-.1-1c-2.9,.3-5.6,1.7-7.5,3.9 c-1.7,1.9-2.7,4.3-2.8,6.9c0,.3,0,.6,.1,.9c.2,0,.5,.1,.7,.1C24.1,11.6,26.6,10.2,28.3,8.1z'%3E%3C/path%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-desktop::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg opacity='.8'%3E%3Cpath d='M1.5 21H22.5V18H1.5V21Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M19.5 15C20.2956 15 21.0587 14.6839 21.6213 14.1213C22.1839 13.5587 22.5 12.7956 22.5 12V6C22.5 5.20435 22.1839 4.44129 21.6213 3.87868C21.0587 3.31607 20.2956 3 19.5 3H4.5C3.70435 3 2.94129 3.31607 2.37868 3.87868C1.81607 4.44129 1.5 5.20435 1.5 6V12C1.5 12.7956 1.81607 13.5587 2.37868 14.1213C2.94129 14.6839 3.70435 15 4.5 15H19.5ZM19.5 12H4.5V6H19.5V12Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3C/g%3E%3C/svg%3E%0A\")}.download-card .snowflake-code-snippet,.download-card .snowflake-code-snippet code,.download-card .snowflake-code-snippet pre{font-size:14px;color:#000;text-shadow:none !important}.download-chip:hover{background-color:var(--ui-background-05) !important;transition:300ms ease background-color}body:has(.snowflake-skip-to-content[style]) #subNav,.pushdown-banner-dismissed #subNav{top:var(--scroll-padding-top) !important;transition:300ms ease top}body:has(.snowflake-skip-to-content[style*=\"58\"]) #subNav{top:34px !important}body:has(.snowflake-skip-to-content[style*=\"82\"]) #subNav{top:58px !important}body:has(.snowflake-skip-to-content[style*=\"130\"]) #subNav{top:106px !important}body:has(.snowflake-skip-to-content[style*=\"138\"]) #subNav{top:114px !important}body:has(.snowflake-skip-to-content[style*=\"146\"]) #subNav{top:122px !important}.is-hidden .snowflake-person-chip-avatar{display:none}.is-small .snowflake-person-chip-avatar{width:56px;height:56px}.ai-summary ul{margin:16px 0 0 0 !important;padding:0 !important;list-style-type:none}.ai-summary li{margin:0;padding:0 0 0 32px;position:relative}.ai-summary li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.ai-summary li:not(:last-child){margin-bottom:1rem}.snowflake-content-chip-image__image{aspect-ratio:5 / 3 !important}.content-chip-new .snowflake-content-chip-image__image{height:100% !important;aspect-ratio:unset !important}.snapshot-card .snowflake-text p:not(:first-child){margin-top:var(--spacing-01)}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p:has(b){font-family:'Texta',sans-serif;margin-top:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p b{font-weight:700 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){border-bottom:1px solid #ccc;padding-bottom:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) p:first-child:has(b){font-family:'Texta',sans-serif;font-size:20px !important;margin-bottom:1rem !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li{display:inline-block}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li a{display:inline-block;text-decoration:none;padding:4px 16px !important;border:1px solid #ccc;border-radius:24px;color:#666 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) ul{list-style-type:none;display:flex;padding:0 !important;margin:0 !important;gap:12px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container img{width:90%;max-width:240px;margin:0 auto}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container{padding:40px;max-width:450px;margin:0 0 0 auto;background-color:#fff;box-shadow:0 2px 6px 0 rgba(152,162,179,.25),0 10px 20px 0 rgba(152,162,179,.10);border-radius:8px;border-top:4px solid var(--ui-01)}.ai-summary{background-color:#f3fbfe;border-left:2px solid var(--ui-01);padding:40px}.ai-summary\u003Espan p:last-child:has(i){color:#666;font-size:14px !important}.ai-summary\u003Espan p:last-child:has(i) a{color:#666 !important;text-decoration:underline !important}.ai-summary\u003Espan p:last-child:has(i) a:hover{color:var(--ui-01) !Important}.ai-summary\u003Espan p:first-child:has(b)::after{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9.3158 3.15226C8.6475 6.2258 6.22698 8.64545 3.15232 9.31587C2.94923 9.36072 2.94923 9.63928 3.15232 9.68413C6.22698 10.3522 8.6475 12.7742 9.3158 15.8477C9.36067 16.0508 9.63933 16.0508 9.6842 15.8477C10.3525 12.7742 12.773 10.3545 15.8477 9.68413C16.0508 9.63928 16.0508 9.36072 15.8477 9.31587C12.773 8.64781 10.3525 6.2258 9.6842 3.15226C9.63933 2.94925 9.36067 2.94925 9.3158 3.15226Z' fill='%23249EDC'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.3725 11.5461C16.9098 13.6739 15.2341 15.3491 13.1054 15.8132C12.9649 15.8443 12.9649 16.0371 13.1054 16.0681C15.2341 16.5307 16.9098 18.2074 17.3725 20.3353C17.4035 20.4758 17.5965 20.4758 17.6275 20.3353C18.0902 18.2074 19.7659 16.5323 21.8946 16.0681C22.0352 16.0371 22.0352 15.8443 21.8946 15.8132C19.7659 15.3507 18.0902 13.6739 17.6275 11.5461C17.5965 11.4055 17.4035 11.4055 17.3725 11.5461Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-repeat:no-repeat;background-size:contain;background-position:center;vertical-align:middle;margin-left:8px}.ai-summary\u003Espan p:first-child:has(b){color:var(--ui-01) !important;text-transform:uppercase}.border-top{border-top:1px solid rgba(0,0,0,.2)}.border-top\u003Espan{display:block;padding-top:32px}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9 !important}.content-chip-new .snowflake-content-chip-image__image{border-radius:0;object-fit:cover;height:100%}.sf-footer #ot-sdk-btn.ot-sdk-show-settings,.sf-footer #ot-sdk-btn.optanon-show-settings{color:rgba(255,255,255,.7) !important;text-underline-offset:4px;border-top:none;border-left:none;border-right:none;border-bottom:1px dotted transparent;background-color:transparent !important;background-image:none !important;transition:300ms ease text-decoration-color;padding:0 !important;font-size:12px;font-family:'Lato',sans-serif}.sf-footer #ot-sdk-btn.ot-sdk-show-settings:hover,.sf-footer #ot-sdk-btn.optanon-show-settings:hover{color:rgba(255,255,255,1) !important;border-bottom:1px dotted var(--ui-01);transition:300ms ease text-decoration-color}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{flex-shrink:0}.sf-footer__disclaimers{background-color:#042130}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p a{color:inherit;text-decoration:none !important}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p sup{margin-right:2px}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p{text-indent:-5px;padding-left:5px}.sf-footer__disclaimers-inner{border-top:1px solid rgba(255,255,255,.25);padding:40px 0}.sf-footer__disclaimers .snowflake-simple-stat{align-items:flex-start;text-align:left;color:rgba(255,255,255,.7);margin-bottom:10px}.sf-footer__social{display:flex;justify-content:center;gap:12px}.sf-footer .snowflake-footer-social-item{margin:0 !important}.sf-footer .snowflake-footer-social-item a{line-height:0;background-color:rgba(3,24,35,.8);display:inline-block;width:48px !important;height:48px;border-radius:8px;display:inline-flex;justify-content:center;align-items:center;transition:300ms ease background-color}.sf-footer .snowflake-footer-social-item a:hover{background-color:var(--ui-01) !important;transition:300ms ease background-color}.sf-footer__bottom{padding-bottom:40px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoError .mktoErrorMsg{max-width:100%;color:#fff}.sf-footer .mktoForm .mktoError .mktoErrorMsg .mktoErrorDetail{display:inline-block}.sf-footer .mktoFormRow:has(.mktoHtmlText:empty){display:none}.sf-footer .mktoFormRow .mktoHtmlText span{color:#fff !important}.sf-footer{background-color:#042130}.sf-footer .optanon-toggle-display:hover{text-decoration-color:var(--ui-01) !important;cursor:pointer !important;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:var(--ui-01);color:#fff !important;transition:300ms ease text-decoration-color;text-decoration:underline;opacity:1}.sf-footer__logo{width:40px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{row-gap:32px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;align-items:center;text-align:center;row-gap:16px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){text-align:center;flex-grow:1}.sf-footer__legal-links li button,.sf-footer__legal-links li a,.sf-footer__legal-links li{margin:0;color:rgba(255,255,255,.7) !important;font-weight:500}.sf-footer__legal-links li a:hover{color:rgba(255,255,255,1) !important}.sf-footer div.sf-footer__copyright p,.sf-footer div.sf-footer__legal-links li,.sf-footer div.sf-footer__legal-links a,.sf-footer div.sf-footer__legal-links p{font-size:12px !important}.sf-footer__legal-links ul{list-style-type:none;margin:0;padding:0;display:flex;gap:20px;row-gap:4px;justify-content:center;flex-wrap:wrap;text-align:center}.sf-footer__legal-links li:last-child{width:100%}.sf-footer .mktoFormRow:has(.mktoPlaceholder),.sf-footer .mktoFormRow:has(input[type=\"hidden\"]){display:none !important}.sf-footer .mktoFormCol{margin-bottom:0 !important}.sf-footer label[for=\"adhoc1\"]{width:auto !important;flex-grow:1;margin-left:16px}.sf-footer .mktoFieldWrap:has(label[for=\"adhoc1\"]){display:flex;flex-direction:row-reverse;margin-top:22px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoCheckboxList input[type=checkbox]{background-color:transparent !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-color:transparent !important;color:#fff !important;height:auto !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important;padding:12px 18px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select:focus{border-color:var(--ui-01) !important}.sf-footer .mktoForm *{padding:0 !important}.sf-footer .mktoForm,.sf-footer .snowflake-marketo-form-container{padding:0 !important;background:transparent;margin-bottom:0;box-shadow:none}.sf-footer .mktoHtmlText.mktoHasWidth{width:100% !important;margin:24px 0}.sf-footer .mktoFormRow{flex-direction:column}.sf-footer .mktoForm .mktoButtonWrap{margin:0 !important}.sf-footer select{background-image:url(\"data:image/svg+xml,%3Csvg width='14' height='8' viewBox='0 0 14 8' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M.981445 1.43496L6.90897 7.32496L12.9314 1.33496' stroke='white' stroke-width='1.33333' stroke-miterlimit='10' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\") !important}.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative{justify-content:flex-start}.sf-footer *::placeholder{color:#fff !important;opacity:.8}.sf-footer .mktoForm .mktoButtonWrap.mktoSimple .mktoButton{background-color:var(--ui-01) !important;color:#fff !important;width:100% !important;padding:12px 16px !important;border:1px solid var(--ui-01) !important;background-image:none !important;border-radius:48px;text-transform:uppercase;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:16px !important;line-height:1.2}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoHtmlText\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoLabel\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap label.mktoLabel{color:#fff !important}.sf-footer__newsletter-title p:not(:first-child){margin-top:8px !important}.sf-footer__newsletter-title p b{font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:22px !important;line-height:1.2}.sf-footer__newsletter-title p:last-child{font-size:14px !important;opacity:.8}.sf-footer__link-group li a[target=\"_blank\"]::after{content:'';display:inline-block;width:10px;height:10px;margin-left:5px;background-image:url(\"data:image/svg+xml,%3Csvg width='11' height='11' viewBox='0 0 11 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.72222 1.22222C6.38471 1.22222 6.11111 .948616 6.11111 .611111C6.11111 .273607 6.38471 0 6.72222 0H10.3889C10.551 0 10.7064 .0643867 10.821 .178988C10.9356 .293596 11 .449032 11 .611111V4.27778C11 4.61529 10.7264 4.88889 10.3889 4.88889C10.0514 4.88889 9.77778 4.61529 9.77778 4.27778V2.08647L4.09879 7.76545C3.86013 8.00409 3.4732 8.00409 3.23454 7.76545C2.99589 7.52681 2.99589 7.13986 3.23454 6.90122L8.91355 1.22222H6.72222ZM0 2.44444C0 1.76943 .547207 1.22222 1.22222 1.22222H4.27778C4.61529 1.22222 4.88889 1.49583 4.88889 1.83333C4.88889 2.17084 4.61529 2.44444 4.27778 2.44444H1.22222V9.77778H8.55556V6.72222C8.55556 6.38471 8.82915 6.11111 9.16667 6.11111C9.50418 6.11111 9.77778 6.38471 9.77778 6.72222V9.77778C9.77778 10.4528 9.23059 11 8.55556 11H1.22222C.547207 11 0 10.4528 0 9.77778V2.44444Z' fill='white'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-position:center}.sf-footer__link-group ul,.sf-footer__link-group li{margin:0;padding:0;list-style-type:none}.sf-footer__link-group ul{margin-top:20px !important}.sf-footer__link-group li{margin-top:15px}.sf-footer div.sf-footer__link-group\u003Espan\u003Ep\u003Ea,.sf-footer div.sf-footer__link-group\u003Espan\u003Ep{color:var(--ui-01) !important;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:20px !important;line-height:1.2}.sf-footer__link-group li a{opacity:.9;color:#fff !important;font-weight:500 !important;font-size:15px !important;line-height:1.3}.sf-footer__link-group li a:hover{opacity:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::before,.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::after{display:none}.sf-footer__column{flex-grow:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child){width:50%}@media (min-width:800px){.sf-footer__legal-links ul{justify-content:flex-start;text-align:left}.sf-footer__social{justify-content:flex-end}.sf-footer__legal-links ul{padding-left:24px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{text-align:right;flex-wrap:nowrap}.sf-footer__legal-links.align-left ul{justify-content:flex-start}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;flex-direction:row}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto !important;max-width:200px}.sf-footer-grid__inner:not(.hidden-newsletter)\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:not(.hidden-newsletter)\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{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-fd0ebce1bc","title":"Quickstarts Overrides","cssUrl":"/conf/snowflake-site/settings/wcm/templates/quickstart-page-template/structure/_jcr_content/root/markup_editor.45e3b816710d8d5a.css","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"],":hierarchyType":"page","locale":"en",":mappedPath":"/en/developers/guides/comprehensive-guide-to-data-quality-monitoring/"}