{"cssClassNames":"page basicpage summit-page","allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"templateName":"quickstart-page-template","language":"en","title":"Using Snowflake Cortex And Streamlit With Geospatial Data","analyticsPageType":"quickstart-page-template","analyticsCategory":"general","analyticsSubCategory":"","excludeFromAnalytics":false,":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-ec549c0bf9","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/pushdown-banner/master/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"pushdown_banner_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-c803d1b0ca","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"pushdown_banner_copy":{"id":"pushdown-banner-de7e3999c5","contentHeadline":"Snowflake World Tour hits your city","contentDescription":"See how leading teams deploy agents at scale. Find a stop near you. Register free.","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"/en/world-tour/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Register now"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy"],":type":"snowflake-site/components/container"},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"experiencefragment-header":{"id":"experiencefragment-e897ad2fd7","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/jcr:content","configured":true,":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","id":"container-cde8f41bff","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"markup_editor":{"id":"markup-editor-ad3800b26d","title":" ","cssContent":".footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.mega-nav__sign-in.snowflake-button-container{display:none}@media screen and (min-width:768px){.mega-nav__sign-in.snowflake-button-container{display:inline-block;font-family:'Texta',sans-serif;font-weight:800 !important}}@media screen and (min-width:1024px) and (max-width:1199px){.snowflake-mega-nav-header-buttons-container .snowflake-button-blue .snowflake-button-container{font-size:13px !important}.snowflake-language-navigation .language-icon{width:18px !important;height:18px !important;margin-right:4px !important}}.mega-nav__sign-in svg{display:none}.nav-item__platform-parent-why-sf.snowflake-mega-nav-nav-item\u003Ea:hover,.nav-item__platform-parent.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent !important}.nav-platform-sidebar .snowflake-mega-nav-nav-item:hover.blue-icon .snowflake-mega-nav-nav-item-icon__inner{background-color:var(--ui-01) !important}@media screen and (min-width:1024px){.snowflake-mega-nav-navigation-dropdown{overflow:hidden}.meganav-platform-features{padding-left:64px}.meganav-platform-features::before{content:'';transform:translateX(-64px);display:block;z-index:0;width:100%;height:100%;position:absolute;top:0;background:#f7f9fa}.nav-item--si.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:transparent}.nav-item--si{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:8px}.nav-item__platform-parent{border-bottom:1px solid #ccc;margin-bottom:8px;padding-bottom:16px}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description::after{content:'What Snowflake can do for you \u003E';display:block;color:var(--ui-01);margin-top:16px}.nav-item__platform-parent .snowflake-mega-nav-nav-item-description::after{content:'View the platform \u003E';display:block;color:var(--ui-01);margin-top:16px}}@media screen and (min-width:1367px){.snowflake-mega-nav-nav-item-description{font-size:13px !important;line-height:20px !important}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{font-size:17px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-title,.nav-item__platform-parent .snowflake-mega-nav-nav-item-title{font-size:24px !important;line-height:32px !important;margin-bottom:8px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description,.nav-item__platform-parent .snowflake-mega-nav-nav-item-description{font-size:14px !important;line-height:20px !important}}html.wf-texta-n9-loading .display-1-v2{font-size:48px!important;line-height:50px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-4-v2{font-size:18px!important;line-height:24px!important;font-family:sans-serif!important}@media screen and (min-width:768px){html.wf-texta-n9-loading .display-2-v2{font-size:48px!important;line-height:50px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:55.5px!important;line-height:54px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .heading-5-v2,html.wf-lato-n4-loading .snowflake-card-v2-advanced-text .snowflake-text p{font-size:15.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:34px!important;line-height:38px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-4,html.wf-texta-n8-loading .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-regular .snowflake-button-container{font-size:13px!important;line-height:20px!important;letter-spacing:.25px!important;font-family:sans-serif!important}}@media screen and (min-width:1024px){html.wf-lato-n4-loading .snowflake-mega-nav-nav-item-description{font-size:11.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .snowflake-button-compact .snowflake-button-container{font-size:12px!important;letter-spacing:0!important;line-height:18px!important}}@media screen and (min-width:1367px){html.wf-lato-n4-loading .hp-hero__eyebrow a\u003Eb:first-child{font-size:11px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .hp-hero__eyebrow a{font-size:13px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-2-v2{font-size:61px!important;line-height:60px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:74.5px!important;line-height:74px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:41px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-3-v2{font-family:sans-serif!important;letter-spacing:-.75px!important;font-size:33.75px!important}html.wf-texta-n9-loading .heading-4-v2{font-size:19.5px!important;line-height:26px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2{font-size:12px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:14px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-1,html.wf-lato-n4-loading .cq-Editable-dom[data-cq-data-path*=text] ol\u003Eli,html.wf-lato-n4-loading .snowflake-text li,html.wf-lato-n4-loading .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text li,html.wf-lato-n4-loading .text-size-large .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-large.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom span[data-testid=text-content],html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Ep,html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Eul\u003Eli{font-size:17.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content],html.wf-texta-n8-loading .snowflake-button-link .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-link-back .snowflake-button-container{font-size:15.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-3,html.wf-lato-n4-loading .text-size-small .snowflake-text li,html.wf-lato-n4-loading .text-size-small .snowflake-text p,html.wf-lato-n4-loading .text-size-small .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-small.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}}#industryPlatformSection,.sc-hero{background-position:top left;background-size:20% auto}.bwalignc,.bwalignr{list-style-position:inside}.snowflake-text p sup{font-size:10px}#industryPlatformSection .industry-platform__row .snowflake-flexible-column-container-items,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container,.snowflake-hero-system-content-container{gap:16px}.agenda-item p,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.partner-details p{margin:0!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::after,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::before,.hide-logo .snowflake-case-study-card-logo,.partner-page__powered-by-logo,.sc-hero div.code-toolbar\u003E.toolbar,.snowflake-card-v2-advanced.no-link .snowflake-card-v2-advanced-button,.snowflake-partner-hero-card-badge-container{display:none!important}.section--card-mobile-carousel .snowflake-flexible-column-container-items-with-carousel{max-width:100%!important}@media screen and (min-width:768px){.button-group-pair .snowflake-button-container.inline-button--desktop,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;display:inline-block!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:flex-start!important}.button-group-pair.center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center!important}.section--card-mobile-carousel{margin-left:var(--tablet-portrait-margin,48px)!important;margin-right:var(--tablet-portrait-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-portrait-margin) * 2)!important}}@media screen and (min-width:1024px){.section--card-mobile-carousel{margin-left:var(--tablet-horizontal-margin,48px)!important;margin-right:var(--tablet-horizontal-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-horizontal-margin) * 2)!important}.snowflake-mega-nav-header-mobile-icon{display:none!important}}@media screen and (min-width:1367px){.section--card-mobile-carousel{margin-left:var(--desktop-margin,6.5%)!important;margin-right:var(--desktop-margin,6.5%);width:87%!important;width:calc(100% - var(--desktop-margin) * 2)!important}.logo-container{min-width:143px}.sc-hero__headline .heading-1-v2{font-size:60px}.snowflake-mega-nav-navigation-title{font-size:17px}.snowflake-mega-nav-dropdown-footer-wrapper .snowflake-title-v2 .snowflake-title-v2-line:first-child{font-size:16px!important;line-height:24px!important}}.hero--home{overflow:hidden;background-color:var(--ui-01);z-index:2}.hp-hero__subheadline{width:90%}.hero--home .snowflake-button-container{transition:.3s}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-secondary a:hover,.hero--home .snowflake-button-white a:hover{transition:.3s;background-color:var(--ui-02)!important;color:var(--ui-05)!important}.hero--home .snowflake-button-secondary a:hover{border-color:var(--ui-05)!important}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-white a:hover{border-color:var(--ui-02)!important}.bwalignc,.hp-hero__eyebrow{text-align:center}.hp-hero__eyebrow a{display:inline-flex;flex-direction:column;justify-content:center;cursor:pointer;padding:8px;border-radius:var(--spacing-01);gap:8px;align-items:center;background-color:#45aee3;color:var(--ui-03);font-family:Texta,sans-serif;font-weight:800;font-size:16px;line-height:22px;transition:background-color .3s}.hp-hero__eyebrow a:hover{background-color:#7fc6ea;text-decoration:none;transition:background-color .3s}.hp-hero__eyebrow a\u003Eb:first-child{text-transform:uppercase;white-space:nowrap;display:inline-block;background-color:var(--ui-02);color:var(--ui-05);font-size:12px!important;line-height:16px!important;font-family:Lato,sans-serif;font-weight:500!important;padding:3px 6px;border-radius:2px;letter-spacing:1px}@media screen and (min-width:767px){.hp-hero__eyebrow{text-align:left}.hp-hero__eyebrow a{flex-direction:row;text-align:left}}.hero--home__inner .offset-video,.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{max-height:200px;overflow:hidden}.hero--home__inner .offset-video .wistia-responsive-padding{padding-top:100%}.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{position:absolute!important;top:0;left:0;width:100%}.offset-video__bg-image{z-index:-1}@media screen and (min-width:768px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{position:absolute!important;max-height:none;top:0;left:0;width:250%;padding-bottom:250%;transform:translate(0,-50%);height:0}.workloads_7.unistore{max-width:317px}}.promo-banner--homepage{z-index:2}.homepage-banner-offset-container::after{content:\"\";display:block;position:absolute;bottom:0;z-index:1;left:0;width:100%;height:80%;background:#fff}.section--quicklinks .snowflake-button-full-width a{padding-left:24px!important;padding-right:24px!important;transition:box-shadow .25s cubic-bezier(.4,0,.2,1);text-align:left;display:flex;justify-content:center;align-items:center}.section--quicklinks .snowflake-button-full-width a:hover{box-shadow:0 16px 16px 0 rgb(0 0 0 / .16);transition:box-shadow .25s cubic-bezier(.4,0,.2,1)}.section--quicklinks .snowflake-button-container:focus-visible a::before,.section--quicklinks .snowflake-button-full-width a::before{content:\"\";width:23px;height:23px;flex-shrink:0;margin-right:12px;display:inline-block;background-size:cover;background-repeat:no-repeat;background-position:center}#industryPartnerSlider .snowflake-navigation-icon.swiper-button-disabled,#partnerResources .section--resource-hub a svg,.button-tabs span.snowflake-tabs-navigation-item:after,.customer-card--hide-cta .snowflake-case-study-card-button,.dot-tabs span.snowflake-tabs-navigation-item::after,.partner-sidebar__mobile-expand,html:not(.aem-AuthorLayer-initial):not(.aem-AuthorLayer-Edit) .tab-content:not(.is-active){display:none}.section--quicklinks .snowflake-button-full-width a.pricing::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/decorative-icons/pricing-icon.svg)}.section--quicklinks .snowflake-button-full-width a.snowflake_on_snowflake::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon_snowflake-bug.svg)}.section--quicklinks .snowflake-button-full-width a.virtual_hands_on_labs::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__training.svg)}.section--quicklinks .snowflake-button-full-width a.weekly_demo::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__webinars.svg)}@media screen and (min-width:1024px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{left:-50%}.section--quicklinks .snowflake-flexible-column-container-items{gap:24px}.snowflake-quote-item-inner{padding:32px 24px 24px!important}}#communitiesOuter_overflowBottomGray::after{max-height:100px}#caseStudyOuter_overflowBottomMidBlue::after{max-height:180px}#caseStudyInner .snowflake-case-study-card .snowflake-wistia-video{border-radius:0!important}#caseStudyInner .snowflake-case-study-card{box-shadow:none!important;border-radius:0}#caseStudyInner{max-width:1200px;margin:0 auto;box-shadow:rgb(152 162 179 / .1) 0 10px 20px 0,rgb(152 162 179 / .25) 0 2px 6px 0;border-radius:8px;overflow:hidden;position:relative;z-index:1}.case-study__logo-bar\u003E.snowflake-flexible-column-container-items{background:#f7f9fa;padding:32px 16px 40px}.case-study__logo-bar .cmp-image__image{width:90%;margin:0 auto;max-width:240px}.hp-platform__text-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child),.sc-sidebar__group .snowflake-button-link{margin-top:8px}.workloads_7.unistore{margin-left:auto;margin-right:auto}#homepageFootnotesInner .snowflake-simple-stat-disclaimer .snowflake-text p{color:#fff!important}.snowflake-simple-stat-disclaimer .snowflake-text p\u003Ea{border-bottom:1px solid var(--ui-03);color:var(--text-03)}.snowflake-card-v2-advanced{color:inherit}#workloadCardGridOuter .snowflake-card-v2-base-front{gap:0}.video-modal.snowflake-modal-window-open-inner{background-color:#fff0;padding:8px;border:none}.snowflake-container-arrow-dotted-faded .snowflake-container-arrow-dotted-faded-image{width:40%!important;max-width:420px;top:4%!important}.list--blue-bullets ul{margin:0!important;padding:0!important;list-style-type:none}.list--blue-bullets li{margin:0;padding:0 0 0 32px;position:relative}.list--blue-bullets li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.list--blue-bullets li:not(:last-child){margin-bottom:1rem}.logo-tabs .snowflake-navigation-container,.snowflake-simple-stat-content:empty,.summit-speaker-card .snowflake-card-v2-advanced-text{margin-bottom:0}#techResourceInner,#techResourceOuter,div.overflow-bottom--blue,div.overflow-bottom--gray,div.overflow-bottom--mid-blue,div.overflow-bottom--white,div.overflow-top--blue,div.overflow-top--gray,div.overflow-top--mid-blue,div.overflow-top--white,div[id$=overflowBottomGray],div[id$=overflowBottomMidBlue],div[id$=overflowTopBlue],div[id$=overflowTopGray]{position:relative}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{content:\"\";display:block;position:absolute;left:0;width:100%;height:40%}div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{top:0}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after{bottom:0}div.overflow-bottom--white::after,div.overflow-top--white::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopWhite]::after{background:#fff!important}div.overflow-bottom--gray::after,div.overflow-top--gray::after,div[id$=overflowBottomGray]::after,div[id$=overflowTopGray]::after{background:#f6f9fa!important}div.overflow-bottom--mid-blue::after,div.overflow-top--mid-blue::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowTopMidBlue]::after{background:#11567f!important}div.overflow-bottom--blue::after,div.overflow-top--blue::after,div[id$=overflowBottomBlue]::after,div[id$=overflowTopBlue]::after{background:#259edc!important}.snowflake-premium-content-banner.promo-banner--no-shadow{box-shadow:none!important}#industryPartnerSlider .cmp-image__image,#industryPartnerSlider .section--partner-tabs .snowflake-image-container .cmp-image__image,#partnerSidebar,.has-shadow .cmp-image__image{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25)}.content-chip--has-desc{align-items:flex-start;padding:20px!important}.content-chip--has-desc .snowflake-content-chip-image{max-width:100px}.content-chip--has-desc .snowflake-content-chip-image__image{aspect-ratio:1}.content-chip--has-desc .snowflake-title-v2-line:first-child{font-size:18px!important}.content-chip--has-desc .snowflake-title-v2-line:nth-child(2){color:#000!important;font-weight:500!important;font-size:16px!important;line-height:22px!important;margin-top:2px!important}.content-chip--has-desc .snowflake-content-chip-button{margin-top:6px!important;font-size:18px!important;display:none}.square-image .snowflake-content-chip-image{aspect-ratio:1;max-width:120px}.section--logo-bar.smaller-logos .snowflake-image-container .cmp-image__image{max-width:200px;margin:0 auto}.snowflake-card-v2-advanced-tag,.snowflake-content-chip-tag{padding:3px 6px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-button,.snowflake-card-v2-advanced-title:first-child,.summit-pricing-block__aside ul{margin-top:0}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:40px;height:40px;display:flex;justify-content:center;align-items:center;margin:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{width:12px;height:12px;background:var(--ui-12);border-radius:100%}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p,.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{font-size:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{background:var(--ui-01)}.button-tabs .snowflake-navigation-container .swiper-wrapper{padding:8px 0}.button-tabs .snowflake-navigation-container .swiper-slide{margin:0 6px}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{padding:8px 24px;background-color:#f6f9fa;border-radius:48px;margin:0}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{text-transform:uppercase;font-family:Texta,sans-serif;font-weight:700}.button-tabs .border-top{border-top:1px solid #ccc}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{background-color:var(--ui-01);box-shadow:0 2px 6px 0 rgb(152 162 179 / .25),0 10px 20px 0 rgb(152 162 179 / .1)}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{color:#fff}.button-tabs.has-icons .snowflake-navigation-container .snowflake-tabs-navigation-item p::before{content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-position:center center;margin-right:12px;vertical-align:middle;margin-top:-3px}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:220px;padding-bottom:50%;height:0;margin:0 8px!important;background-size:cover;background-repeat:no-repeat;opacity:.5;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item:hover{opacity:.75;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{opacity:1;transition:opacity .3s}.dot-tabs .aem-container.cmp-tabs,.logo-tabs .aem-container.cmp-tabs{display:flex;flex-direction:column-reverse}.snowflake-icon.is-center{margin:0 auto;display:block}#industryPartnerSlider .snowflake-flexible-column-container-items,#partnerLogoSquare .snowflake-flexible-column-container-items{gap:24px}#techResourceOuter::after{content:\"\";display:block;position:absolute;top:0;left:0;width:100%;height:40%;background:#f6f9fa}#techResourceInner{z-index:1}.partner-tier-tag h6{display:inline-block!important;padding:2px 6px;border-radius:2px;color:#666}.partner-tier-tag.registered h6{background-color:#f6f9fa}.partner-tier-tag.elite h6{background-color:#11567f;color:#fff}.partner-tier-tag.premier h6{background-color:#b14c77;color:#fff}.partner-tier-tag.select h6{background-color:#5094a0;color:#fff}.partner-details\u003Espan{display:flex;gap:24px}.partner-details a{color:inherit!important;font-weight:400!important}.partner-details p::before{content:\"\";display:inline-block;vertical-align:middle;width:16px;height:16px;background-repeat:no-repeat;background-position:center;transform:translateY(-1px);background-size:auto 90%;margin-right:6px}.partner-details__location::before{background-image:url(\"data:image/svg+xml,%3Csvg width='13' height='18' viewBox='0 0 13 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M6.25 17.7531C6.4375 17.7531 6.6 17.6844 6.7375 17.5531C6.875 17.4219 6.95 17.2531 6.95 17.0531C6.95 16.8531 7.075 16.4281 7.3 15.7969C7.5875 15.0281 7.925 14.3156 8.30625 13.6406C8.8 12.7781 9.3125 12.1031 9.85 11.6094C10.75 10.7969 11.4125 9.96563 11.85 9.12188C12.2875 8.27813 12.5063 7.40313 12.5063 6.49063C12.5063 5.36563 12.2187 4.31563 11.6437 3.33438C11.0937 2.40313 10.3438 1.65938 9.4 1.10938C8.43125 .534376 7.375 .246876 6.24375 .246876C5.1125 .246876 4.06875 .534376 3.0875 1.10938C2.15625 1.65938 1.4125 2.40313 .862498 3.33438C.287498 4.31563 0 5.36563 0 6.49063C0 7.47188 .262499 8.42813 .787499 9.35938C1.14375 10.0031 1.65625 10.6656 2.3125 11.3344C2.75625 11.8031 3.24375 12.4781 3.78125 13.3656C4.225 14.0969 4.63125 14.8594 5 15.6656C5.35 16.3844 5.53125 16.8531 5.55625 17.0656C5.55625 17.2594 5.625 17.4156 5.7625 17.5531C5.9 17.6844 6.0625 17.7531 6.25 17.7531ZM6.16875 14.9156C5.775 14.0656 5.325 13.2469 4.825 12.4594C4.275 11.5594 3.7625 10.8719 3.28125 10.3969C2.625 9.71563 2.1375 9.05938 1.825 8.43438C1.5125 7.80313 1.35625 7.16563 1.35625 6.50313C1.35625 5.61563 1.575 4.80313 2.0125 4.05313C2.45 3.30313 3.04375 2.71563 3.7875 2.27813C4.5375 1.84063 5.35 1.62188 6.2375 1.62188C7.125 1.62188 7.9375 1.84063 8.6875 2.27813C9.4375 2.71563 10.0312 3.30313 10.475 4.04688C10.9187 4.80313 11.1375 5.62188 11.1375 6.50313C11.1375 7.90313 10.3937 9.26563 8.9125 10.5969C8.35 11.1094 7.8125 11.7906 7.3 12.6406C6.88125 13.3344 6.50625 14.0969 6.16875 14.9219V14.9156ZM6.26875 8.36563C6.65625 8.36563 7.01875 8.26563 7.35625 8.07188C7.69375 7.87813 7.95625 7.60938 8.14375 7.28438C8.3375 6.95313 8.43125 6.59063 8.43125 6.19688C8.43125 5.80313 8.33125 5.43438 8.1375 5.10313C7.9375 4.76563 7.675 4.50313 7.3375 4.31563C7 4.12813 6.6375 4.02813 6.24375 4.02813C5.85 4.02813 5.4875 4.12813 5.15625 4.32188C4.825 4.52188 4.56875 4.78438 4.375 5.12188C4.18125 5.45938 4.0875 5.82188 4.0875 6.20938C4.0875 6.59688 4.1875 6.95938 4.38125 7.29688C4.58125 7.63438 4.84375 7.89688 5.18125 8.08438C5.51875 8.27813 5.88125 8.37188 6.26875 8.37188V8.36563ZM6.24375 7.50313C5.8875 7.50313 5.575 7.37188 5.31875 7.11563C5.0625 6.85938 4.93125 6.55313 4.93125 6.19063C4.93125 5.82813 5.0625 5.52188 5.31875 5.26563C5.575 5.00938 5.88125 4.87813 6.24375 4.87813C6.60625 4.87813 6.9125 5.00938 7.16875 5.26563C7.425 5.52188 7.55625 5.82813 7.55625 6.19063C7.55625 6.55313 7.425 6.85938 7.16875 7.11563C6.9125 7.37188 6.60625 7.50313 6.24375 7.50313Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}.partner-details__website::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='16' viewBox='0 0 18 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M2.61587 2.96889C2.61587 2.75109 2.79633 2.57062 3.01413 2.57062C3.23192 2.57062 3.41238 2.75109 3.41238 2.96889C3.41238 3.18669 3.23192 3.36716 3.01413 3.36716C2.79633 3.36716 2.61587 3.18669 2.61587 2.96889ZM4.21512 2.96889C4.21512 2.75109 4.39558 2.57062 4.61338 2.57062C4.83117 2.57062 5.01163 2.75109 5.01163 2.96889C5.01163 3.18669 4.83117 3.36716 4.61338 3.36716C4.39558 3.36716 4.21512 3.18669 4.21512 2.96889ZM5.81438 2.96889C5.81438 2.75109 5.99484 2.57062 6.21264 2.57062C6.43043 2.57062 6.61089 2.75109 6.61089 2.96889C6.61089 3.18669 6.43043 3.36716 6.21264 3.36716C5.99484 3.36716 5.81438 3.18669 5.81438 2.96889ZM17.2518 .697559H1.19085C.811258 .697559 .506348 1.0025 .506348 1.38209V14.6179C.506348 14.9975 .811258 15.3024 1.19085 15.3024H17.2518C17.6314 15.3024 17.9363 14.9975 17.9363 14.6179V1.38209C17.9363 1.0025 17.6314 .697559 17.2518 .697559ZM16.5673 2.06035V3.90853H1.86914V2.06035H16.5673ZM1.86914 13.9334V4.78593H16.5673V13.9334H1.86914Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}#partnerSidebar{border-radius:4px;background-color:#fff;padding:24px 24px 32px;border-bottom:6px solid #29b5e8}#partnerSidebar h5,.newsletter-disclaimer p{font-size:14px!important}#partnerSidebar ul{margin-top:0;list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px}#partnerSidebar li{border:1px solid;border-radius:2px;padding:0 4px!important;font-size:11px!important;letter-spacing:.25px;text-transform:uppercase}div.snowflake-partner-hero-card{width:100%;margin:0}.partner-details__logo{max-width:380px;margin:0 auto}@media screen and (max-width:767px){.left-alignment .hp-hero__subheadline{margin-left:auto;margin-right:auto}.left-alignment .hp-hero__headline .snowflake-title-v2-line,.left-alignment .hp-hero__subheadline .snowflake-title-v2-line{text-align:center}.hero--home__inner .snowflake-flexible-column-container-items-top-padding-large{padding-top:var(--spacing-02)}.section--logo-bar\u003E.snowflake-flexible-column-container-items{display:flex;flex-wrap:wrap;flex-direction:row;justify-content:center;gap:8px}.section--logo-bar\u003E.snowflake-flexible-column-container-items\u003Ediv{width:calc(33.33% - 8px)}.partner-sidebar__mobile-expand{display:inline-block;color:#249edc;border-color:#249edc!important}#partnerSidebar li:nth-child(n+6),.summit-nav__links .snowflake-button-tertiary{display:none}.sc-body__sidebar{background-color:#f6f9fa;padding:24px}.sc-body__content{padding:0 24px 24px}.summit-speaker-card .snowflake-card-v2-advanced-content{padding:24px}}#partnerResources h6,.snowflake-tabs-navigation-item p.body-1{font-size:16px!important}#partnerResources .section--resource-hub{padding:0 16px}#partnerResources .section--resource-hub a,.bwalignl{text-align:left}@media screen and (max-width:1023px){.hero--workload .snowflake-hero-system-media-container{width:100%}}.section--timely-content .snowflake-content-chip,.snowflake-mega-nav-dropdown-footer-wrapper{align-items:center}.section--timely-content .snowflake-content-chip-image{max-width:94px}.section--timely-content .snowflake-content-chip-image__inner{line-height:0}.section--timely-content .snowflake-content-chip-image__image{aspect-ratio:1;height:auto}.section--workload-overview .workload-overview__headline{max-width:280px;margin:0 auto}#industryPartnerSlider .swiper-slide{margin-top:0!important;padding:0 12px}#industryPartnerSlider .snowflake-tabs-navigation-item{margin-left:0!important;margin-right:0!important}#industryPartnerSlider .snowflake-premium-content-banner-background-grad-white .snowflake-premium-content-banner{box-shadow:none}#industryPartnerSlider .logo-slider__slide .aem-container{display:flex;padding:0 8px!important;flex-wrap:wrap;gap:16px!important;justify-content:center}#industryPartnerSlider .logo-slider__slide .aem-container\u003Ediv{width:48%;max-width:200px}#useCaseTabs{padding-top:24px;padding-bottom:24px;padding-right:24px}#useCaseTabs .tab-content.is-active{display:block}#useCaseTabs .vert-tab{border-bottom:1px solid #a0bbcc;padding-bottom:16px}#useCaseTabs .vert-tab p{display:inline-block}#useCaseTabs .vert-tab p:hover{cursor:pointer}#useCaseTabs .vert-tab p,#useCaseTabs .vert-tab.is-active p.not-active{color:#249edc}#useCaseTabs .vert-tab p.is-active,#useCaseTabs .vert-tab.is-active p{color:#000}#industryPlatformSection{background-image:url(/adobe/dynamicmedia/deliver/dm-aid--db074ad5-7122-4c51-87a3-76c3aa466182/double-arrow-bg%403x.png);background-repeat:no-repeat}.snowflake-text p.featured-quote__source{font-weight:900!important;text-transform:uppercase;font-size:16px!important;margin-top:2rem!important}.snowflake-text p.featured-quote__title{margin-top:0!important;font-size:16px!important}.snowflake-case-study-card-logo img{width:auto!important;height:100px!important;transform:translateX(-15%)}.snowflake-quote-item-quote-text{font-weight:600!important}#customerStoryStatsInner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row}#customerStoryStat1,#customerStoryStat2{max-width:240px}#storyHighlights{border-radius:4px;padding:1rem}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line,.summit-pricing-block__tile .black-blue-text-color .snowflake-title-v2-line{color:#000!important}.snowflake-youtube-embedded-wrapper{border-radius:var(--small-border-radius)}#arcticNavItem::before,#offset::before,#open-source::before{color:var(--text-05);font-family:Texta,sans-serif!important}#offset,.sc-architecture-caption{margin-top:16px}.hero--press .snowflake-title-v2-line{text-transform:none!important}@media screen and (min-width:768px){.subpage-timely-content__inner\u003E.snowflake-flexible-column-container-items{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25);padding:var(--spacing-04);border-radius:4px;overflow:hidden}#partnerLogoSquare{padding:0 0 0 48px}.hero--workload .snowflake-container{max-width:1440px;margin:0 auto!important;align-items:center}#industryPartnerSlider.snowflake-flexible-column-container-2-column-40-60\u003E.snowflake-flexible-column-container-items{grid-template-columns:minmax(40%,4fr) minmax(0,6fr)}#industryPartnerSlider .swiper-slide{padding:0 24px}.sc-body{padding:48px}.sc-body\u003E.snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:124px}}.snowflake-button-container.has-icon{display:inline-flex;justify-content:center;align-items:center;text-align:left}.snowflake-button-container.has-icon::before{content:\"\";display:inline-block;width:20px;height:20px;margin-right:12px;background-size:contain;background-repeat:no-repeat;background-position:center}.snowflake-button-container.is-video::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M9 1.28663C13.2523 1.28663 16.7134 4.74768 16.7134 9C16.7134 13.2523 13.2523 16.7134 9 16.7134C4.74768 16.7198 1.28663 13.2588 1.28663 9C1.28663 4.74124 4.74768 1.28663 9 1.28663ZM9 0C4.0336 0 0 4.0336 0 9C0 13.9664 4.0336 18 9 18C13.9728 18 18 13.9664 18 9C18 4.0336 13.9728 0 9 0Z' fill='white'/%3E%3Cpath d='M7.75106 6.18211C7.42941 6.16925 7.16565 6.42658 7.16565 6.74823V11.2772C7.16565 11.7082 7.65457 11.9848 8.02126 11.7597L11.7975 9.4952C12.1578 9.27647 12.1578 8.74252 11.7975 8.52379L8.02126 6.25931C7.93763 6.21428 7.84756 6.18211 7.75106 6.18211Z' fill='white'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-github::before{background-image:url(\"data:image/svg+xml,%3Csvg width='20' height='21' viewBox='0 0 20 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 .651794C4.475 .651794 0 5.12679 0 10.6518C0 15.0768 2.8625 18.8143 6.8375 20.1393C7.3375 20.2268 7.525 19.9268 7.525 19.6643C7.525 19.4268 7.5125 18.6393 7.5125 17.8018C5 18.2643 4.35 17.1893 4.15 16.6268C4.0375 16.3393 3.55 15.4518 3.125 15.2143C2.775 15.0268 2.275 14.5643 3.1125 14.5518C3.9 14.5393 4.4625 15.2768 4.65 15.5768C5.55 17.0893 6.9875 16.6643 7.5625 16.4018C7.65 15.7518 7.9125 15.3143 8.2 15.0643C5.975 14.8143 3.65 13.9518 3.65 10.1268C3.65 9.03929 4.0375 8.13929 4.675 7.43929C4.575 7.18929 4.225 6.16429 4.775 4.78929C4.775 4.78929 5.6125 4.52679 7.525 5.81429C8.325 5.58929 9.175 5.47679 10.025 5.47679C10.875 5.47679 11.725 5.58929 12.525 5.81429C14.4375 4.51429 15.275 4.78929 15.275 4.78929C15.825 6.16429 15.475 7.18929 15.375 7.43929C16.0125 8.13929 16.4 9.02679 16.4 10.1268C16.4 13.9643 14.0625 14.8143 11.8375 15.0643C12.2 15.3768 12.5125 15.9768 12.5125 16.9143C12.5125 18.2518 12.5 19.3268 12.5 19.6643C12.5 19.9268 12.6875 20.2393 13.1875 20.1393C17.1375 18.8143 20 15.0643 20 10.6518C20 5.12679 15.525 .651794 10 .651794Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-quickstart::before{background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='21' viewBox='0 0 15 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M13.8489 2.79368H11.6439V2.38493C11.6439 1.71368 11.1451 .967427 10.4251 .967427H8.94762C8.80887 .359927 8.37387 .299927 7.89762 .299927H7.23012C6.85512 .299927 6.26637 .299927 6.08637 .967427H4.68387C3.94887 .967427 3.35637 1.74368 3.35637 2.38493V2.79368H1.15137C.738867 2.79368 .401367 3.13118 .401367 3.54368V20.2537C.401367 20.6662 .738867 21.0037 1.15137 21.0037H13.8489C14.2614 21.0037 14.5989 20.6662 14.5989 20.2537V3.54368C14.5989 3.13118 14.2614 2.79368 13.8489 2.79368ZM4.29387 2.38493C4.29387 2.18243 4.54137 1.90493 4.68387 1.90493H6.50262C6.76137 1.90493 6.97137 1.69493 6.97137 1.43618C6.97137 1.33868 6.97887 1.27868 6.98637 1.24118C7.05012 1.23368 7.15512 1.23368 7.23387 1.23368H7.90137C7.95012 1.23368 8.00637 1.23368 8.05137 1.23368C8.05512 1.27868 8.05887 1.34243 8.05887 1.43243C8.05887 1.69118 8.26887 1.90118 8.52762 1.90118H10.4289C10.5301 1.90118 10.7101 2.14493 10.7101 2.38118V2.78993H4.29762V2.38118L4.29387 2.38493ZM13.0989 19.4999H1.90137V4.29368H13.0989V19.5037V19.4999Z' fill='%23249EDC'/%3E%3Cpath d='M3.82512 16.0424H11.1751C11.4339 16.0424 11.6439 15.8324 11.6439 15.5736V6.88486C11.6439 6.62611 11.4339 6.41611 11.1751 6.41611H3.82512C3.56637 6.41611 3.35637 6.62611 3.35637 6.88486V15.5736C3.35637 15.8324 3.56637 16.0424 3.82512 16.0424ZM4.29387 15.1049V13.3686H10.7064V15.1049H4.29387ZM10.7101 7.35361V12.4311H4.29762V7.35361H10.7101Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 9.35989H8.83887C9.09762 9.35989 9.30762 9.14989 9.30762 8.89114C9.30762 8.63239 9.09762 8.42239 8.83887 8.42239H6.16512C5.90637 8.42239 5.69637 8.63239 5.69637 8.89114C5.69637 9.14989 5.90637 9.35989 6.16512 9.35989Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 11.3624H8.83887C9.09762 11.3624 9.30762 11.1524 9.30762 10.8937C9.30762 10.6349 9.09762 10.4249 8.83887 10.4249H6.16512C5.90637 10.4249 5.69637 10.6349 5.69637 10.8937C5.69637 11.1524 5.90637 11.3624 6.16512 11.3624Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-download::before{background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='18' viewBox='0 0 16 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M15.2017 17.1637H.798265C.364425 17.1637 0 16.7993 0 16.3655V12.3568C0 11.923 .364425 11.5585 .798265 11.5585C1.2321 11.5585 1.59653 11.923 1.59653 12.3568V15.5498H14.4035V12.3568C14.4035 11.923 14.7679 11.5585 15.2017 11.5585C15.6356 11.5585 16 11.923 16 12.3568V16.3655C16 16.7993 15.6529 17.1637 15.2017 17.1637Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.84381 12.9642 7.73969 12.9468 7.63557 12.8947C7.34056 12.7733 7.14967 12.4783 7.14967 12.1485L7.18437 .938127C7.18437 .504287 7.5488 .139862 7.98264 .139862C8.41648 .139862 8.7809 .504287 8.7809 .938127L8.7462 10.257L12.8416 6.33509C13.154 6.02273 13.6746 6.04008 13.9696 6.35244C14.282 6.66481 14.2646 7.18542 13.9523 7.48043L8.50325 12.7386C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.73969 12.9642 7.54881 12.8947 7.39262 12.7386L2.03037 7.53249C1.718 7.22012 1.70065 6.71687 2.01301 6.40451C2.32538 6.09214 2.82863 6.07479 3.141 6.38715L8.50325 11.5932C8.81562 11.9056 8.83297 12.4088 8.52061 12.7212C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-expand::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.64375 10.9125C6.9375 11.2062 6.93125 11.6812 6.64375 11.9687L2.57502 16H3.79375C4.20625 16 4.54376 16.3375 4.54376 16.75C4.54376 17.1625 4.20625 17.5 3.79375 17.5H.756264C.556264 17.5 .36876 17.4187 .22501 17.2812C.22501 17.2812 .206248 17.25 .193748 17.2375C.143748 17.1812 .100004 17.1125 .0625038 17.0437C.0375038 16.9687 .0187492 16.8937 .0187492 16.8187C.0187492 16.8 .0062561 16.7813 .0062561 16.7625V13.725C.0187561 13.3125 .356257 12.9875 .768757 12.9937C1.16876 13 1.48752 13.325 1.50002 13.725V14.9688L5.5875 10.9187C5.88125 10.6312 6.35 10.6312 6.64375 10.9187V10.9125ZM17.5063 .743732C17.5063 .543732 17.425 .356235 17.2875 .218735C17.2875 .218735 17.2562 .199998 17.2437 .193748C17.1875 .137498 17.1188 .0937347 17.0438 .0624847C16.9688 .0374847 16.8938 .0187492 16.8188 .0187492C16.8 .0187492 16.7813 .00623703 16.7625 .00623703H13.725C13.3125 .00623703 12.975 .343745 12.975 .756245C12.975 1.16874 13.3125 1.50623 13.725 1.50623H14.9688L11.1312 5.37498C10.8437 5.67498 10.8563 6.14999 11.1563 6.43124C11.45 6.71249 11.9063 6.70624 12.1938 6.43124L16.0125 2.575V3.79375C16.0125 4.20625 16.35 4.54372 16.7625 4.54372C17.175 4.54372 17.5125 4.20625 17.5125 3.79375V.756245L17.5063 .743732ZM16.7562 12.9688C16.3437 12.9688 16.0063 13.3063 16.0063 13.7188V14.8937L12.1938 10.925C11.9063 10.625 11.4375 10.6188 11.1375 10.9063C10.8375 11.1938 10.8313 11.6625 11.1188 11.9625L15 16.0062H13.7188C13.3063 16.0062 12.9688 16.3437 12.9688 16.7562C12.9688 17.1687 13.3063 17.5063 13.7188 17.5063H16.7562C16.85 17.5063 16.95 17.4875 17.0375 17.45C17.0875 17.425 17.1313 17.3937 17.175 17.3625C17.2063 17.3437 17.2438 17.325 17.275 17.3C17.3313 17.2375 17.375 17.1687 17.4125 17.1C17.4188 17.0875 17.4375 17.075 17.4438 17.0562C17.45 17.025 17.4563 16.9938 17.4625 16.9625C17.4813 16.9 17.5 16.8375 17.5 16.7687V13.725C17.5 13.3125 17.1687 12.975 16.7562 12.975V12.9688ZM.750008 4.53125C1.16251 4.53125 1.50002 4.19374 1.50002 3.78124V2.5L5.59376 6.43124C5.89376 6.71874 6.36251 6.70626 6.65001 6.41251C6.93751 6.11876 6.92501 5.64375 6.63126 5.35625L2.61251 1.49998H3.7875C4.2 1.49998 4.53751 1.16249 4.53751 .749989C4.53751 .337489 4.2 0 3.7875 0H.743752C.668752 0 .600004 .0187355 .531254 .0437355C.506254 .0499855 .481263 .0437477 .462513 .0562477C.443763 .0687477 .425015 .0812462 .406265 .0937462C.337515 .124996 .275004 .168741 .218754 .224991H.212498C.212498 .224991 .175 .28125 .15625 .3125C.11875 .3625 .0812477 .4125 .0562477 .46875C.0374977 .525 .0249992 .587499 .0187492 .643749C.0124992 .674999 0 .712482 0 .743732V3.78124C0 4.19374 .337508 4.53125 .750008 4.53125Z' fill='white'/%3E%3C/svg%3E%0A\")}@keyframes slow-scroll{100%{transform:translateY(-50%)}}.sc-hero{overflow:hidden;background-color:#212d35;background-repeat:repeat-y;background-image:url(\"data:image/svg+xml,%3Csvg width='389' height='17' viewBox='0 0 389 17' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M.638672 7.80824L.638672 9.2566C.638672 9.52364 .85538 9.74024 1.12262 9.74024H2.57204C2.83928 9.74024 3.05598 9.52364 3.05598 9.2566V7.80824C3.05598 7.54119 2.83928 7.32472 2.57204 7.32472L1.12262 7.32472C.85538 7.32472 .638672 7.54119 .638672 7.80824Z' fill='url(%23paint0_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.9639 7.80824V9.2566C10.9639 9.52364 11.1806 9.74024 11.4478 9.74024L12.8972 9.74024C13.1645 9.74024 13.3812 9.52364 13.3812 9.2566V7.80824C13.3812 7.54119 13.1645 7.32471 12.8972 7.32471L11.4478 7.32471C11.1806 7.32471 10.9639 7.54119 10.9639 7.80824Z' fill='url(%23paint1_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M21.2891 7.80823V9.2566C21.2891 9.52364 21.5058 9.74024 21.773 9.74024L23.2224 9.74024C23.4897 9.74024 23.7064 9.52364 23.7064 9.2566V7.80823C23.7064 7.54119 23.4897 7.32471 23.2224 7.32471L21.773 7.32471C21.5058 7.32471 21.2891 7.54119 21.2891 7.80823Z' fill='url(%23paint2_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M31.6143 7.80823V9.2566C31.6143 9.52364 31.831 9.74024 32.0982 9.74024H33.5476C33.8149 9.74024 34.0316 9.52364 34.0316 9.2566V7.80823C34.0316 7.54119 33.8149 7.32471 33.5476 7.32471L32.0982 7.32471C31.831 7.32471 31.6143 7.54119 31.6143 7.80823Z' fill='url(%23paint3_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M41.9395 7.80823V9.2566C41.9395 9.52364 42.1562 9.74024 42.4234 9.74024H43.8728C44.1401 9.74024 44.3568 9.52364 44.3568 9.2566V7.80823C44.3568 7.54119 44.1401 7.32471 43.8728 7.32471L42.4234 7.32471C42.1562 7.32471 41.9395 7.54119 41.9395 7.80823Z' fill='url(%23paint4_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M52.5076 7.80823V9.2566C52.5076 9.52364 52.7243 9.74024 52.9916 9.74024H54.441C54.7082 9.74024 54.9249 9.52364 54.9249 9.2566V7.80823C54.9249 7.54119 54.7082 7.32471 54.441 7.32471L52.9916 7.32471C52.7243 7.32471 52.5076 7.54119 52.5076 7.80823Z' fill='url(%23paint5_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M62.8331 7.80823V9.2566C62.8331 9.52364 63.0493 9.74024 63.3165 9.74024H64.7664C65.0332 9.74024 65.2504 9.52364 65.2504 9.2566V7.80823C65.2504 7.54119 65.0332 7.32471 64.7664 7.32471L63.3165 7.32471C63.0493 7.32471 62.8331 7.54119 62.8331 7.80823Z' fill='url(%23paint6_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M73.1583 7.80823V9.2566C73.1583 9.52364 73.3745 9.74024 73.6417 9.74024H75.0916C75.3584 9.74024 75.5756 9.52364 75.5756 9.2566V7.80823C75.5756 7.54119 75.3584 7.32471 75.0916 7.32471L73.6417 7.32471C73.3745 7.32471 73.1583 7.54119 73.1583 7.80823Z' fill='url(%23paint7_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M83.4835 7.80823V9.2566C83.4835 9.52364 83.6997 9.74024 83.9669 9.74024H85.4168C85.6836 9.74024 85.9008 9.52364 85.9008 9.2566V7.80823C85.9008 7.54119 85.6836 7.32471 85.4168 7.32471L83.9669 7.32471C83.6997 7.32471 83.4835 7.54119 83.4835 7.80823Z' fill='url(%23paint8_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M93.8087 7.80823V9.2566C93.8087 9.52364 94.0249 9.74024 94.2921 9.74024H95.742C96.0088 9.74024 96.226 9.52364 96.226 9.2566V7.80823C96.226 7.54119 96.0088 7.32471 95.742 7.32471L94.2921 7.32471C94.0249 7.32471 93.8087 7.54119 93.8087 7.80823Z' fill='url(%23paint9_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M104.134 7.80823V9.2566C104.134 9.52364 104.35 9.74024 104.617 9.74024H106.067C106.334 9.74024 106.551 9.52364 106.551 9.2566V7.80823C106.551 7.54119 106.334 7.32471 106.067 7.32471L104.617 7.32471C104.35 7.32471 104.134 7.54119 104.134 7.80823Z' fill='url(%23paint10_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M114.702 7.80823V9.2566C114.702 9.52364 114.918 9.74024 115.185 9.74024L116.635 9.74024C116.902 9.74024 117.119 9.52364 117.119 9.25659V7.80823C117.119 7.54119 116.902 7.32471 116.635 7.32471L115.185 7.32471C114.918 7.32471 114.702 7.54119 114.702 7.80823Z' fill='url(%23paint11_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M125.027 7.80823V9.25659C125.027 9.52364 125.243 9.74024 125.511 9.74024L126.961 9.74024C127.227 9.74024 127.445 9.52364 127.445 9.25659V7.80823C127.445 7.54119 127.227 7.32471 126.961 7.32471L125.511 7.32471C125.243 7.32471 125.027 7.54119 125.027 7.80823Z' fill='url(%23paint12_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M135.352 7.80823V9.25659C135.352 9.52364 135.569 9.74024 135.836 9.74024H137.286C137.553 9.74024 137.77 9.52364 137.77 9.25659V7.80823C137.77 7.54119 137.553 7.32471 137.286 7.32471L135.836 7.32471C135.569 7.32471 135.352 7.54119 135.352 7.80823Z' fill='url(%23paint13_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M145.678 7.80823V9.25659C145.678 9.52364 145.894 9.74024 146.161 9.74024H147.611C147.878 9.74024 148.095 9.52364 148.095 9.25659V7.80823C148.095 7.54119 147.878 7.32471 147.611 7.32471L146.161 7.32471C145.894 7.32471 145.678 7.54119 145.678 7.80823Z' fill='url(%23paint14_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M156.003 7.80823V9.25659C156.003 9.52364 156.219 9.74024 156.486 9.74024H157.936C158.203 9.74024 158.42 9.52364 158.42 9.25659V7.80823C158.42 7.54119 158.203 7.32471 157.936 7.32471L156.486 7.32471C156.219 7.32471 156.003 7.54119 156.003 7.80823Z' fill='url(%23paint15_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M166.328 7.80823V9.25659C166.328 9.52363 166.544 9.74024 166.811 9.74024H168.261C168.528 9.74024 168.745 9.52363 168.745 9.25659V7.80823C168.745 7.54119 168.528 7.32471 168.261 7.32471L166.811 7.32471C166.544 7.32471 166.328 7.54119 166.328 7.80823Z' fill='url(%23paint16_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M176.896 7.80823V9.25659C176.896 9.52363 177.112 9.74023 177.38 9.74023H178.83C179.096 9.74023 179.313 9.52363 179.313 9.25659V7.80823C179.313 7.54119 179.096 7.32471 178.83 7.32471L177.38 7.32471C177.112 7.32471 176.896 7.54119 176.896 7.80823Z' fill='url(%23paint17_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M187.221 7.80823V9.25659C187.221 9.52363 187.438 9.74023 187.705 9.74023H189.155C189.421 9.74023 189.639 9.52363 189.639 9.25659V7.80823C189.639 7.54119 189.421 7.32471 189.155 7.32471L187.705 7.32471C187.438 7.32471 187.221 7.54119 187.221 7.80823Z' fill='url(%23paint18_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M199.639 7.80824V9.2566C199.639 9.52364 199.855 9.74024 200.123 9.74024H201.572C201.839 9.74024 202.056 9.52364 202.056 9.2566V7.80824C202.056 7.54119 201.839 7.32472 201.572 7.32472L200.123 7.32472C199.855 7.32472 199.639 7.54119 199.639 7.80824Z' fill='url(%23paint19_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M209.964 7.80824V9.2566C209.964 9.52364 210.181 9.74024 210.448 9.74024L211.897 9.74024C212.164 9.74024 212.381 9.52364 212.381 9.2566V7.80824C212.381 7.54119 212.164 7.32471 211.897 7.32471L210.448 7.32471C210.181 7.32471 209.964 7.54119 209.964 7.80824Z' fill='url(%23paint20_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M220.289 7.80823V9.2566C220.289 9.52364 220.506 9.74024 220.773 9.74024L222.222 9.74024C222.49 9.74024 222.706 9.52364 222.706 9.2566V7.80823C222.706 7.54119 222.49 7.32471 222.222 7.32471L220.773 7.32471C220.506 7.32471 220.289 7.54119 220.289 7.80823Z' fill='url(%23paint21_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M230.614 7.80823V9.2566C230.614 9.52364 230.831 9.74024 231.098 9.74024H232.548C232.815 9.74024 233.032 9.52364 233.032 9.2566V7.80823C233.032 7.54119 232.815 7.32471 232.548 7.32471L231.098 7.32471C230.831 7.32471 230.614 7.54119 230.614 7.80823Z' fill='url(%23paint22_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M240.939 7.80823V9.2566C240.939 9.52364 241.156 9.74024 241.423 9.74024H242.873C243.14 9.74024 243.357 9.52364 243.357 9.2566V7.80823C243.357 7.54119 243.14 7.32471 242.873 7.32471L241.423 7.32471C241.156 7.32471 240.939 7.54119 240.939 7.80823Z' fill='url(%23paint23_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M251.508 7.80823V9.2566C251.508 9.52364 251.724 9.74024 251.992 9.74024H253.441C253.708 9.74024 253.925 9.52364 253.925 9.2566V7.80823C253.925 7.54119 253.708 7.32471 253.441 7.32471L251.992 7.32471C251.724 7.32471 251.508 7.54119 251.508 7.80823Z' fill='url(%23paint24_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M261.833 7.80823V9.2566C261.833 9.52364 262.049 9.74024 262.317 9.74024H263.766C264.033 9.74024 264.25 9.52364 264.25 9.2566V7.80823C264.25 7.54119 264.033 7.32471 263.766 7.32471L262.317 7.32471C262.049 7.32471 261.833 7.54119 261.833 7.80823Z' fill='url(%23paint25_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M272.158 7.80823V9.2566C272.158 9.52364 272.374 9.74024 272.642 9.74024H274.092C274.358 9.74024 274.576 9.52364 274.576 9.2566L274.576 7.80823C274.576 7.54119 274.358 7.32471 274.092 7.32471L272.642 7.32471C272.374 7.32471 272.158 7.54119 272.158 7.80823Z' fill='url(%23paint26_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M282.483 7.80823V9.2566C282.483 9.52364 282.7 9.74024 282.967 9.74024H284.417C284.684 9.74024 284.901 9.52364 284.901 9.2566V7.80823C284.901 7.54119 284.684 7.32471 284.417 7.32471L282.967 7.32471C282.7 7.32471 282.483 7.54119 282.483 7.80823Z' fill='url(%23paint27_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M292.809 7.80823L292.809 9.2566C292.809 9.52364 293.025 9.74024 293.292 9.74024H294.742C295.009 9.74024 295.226 9.52364 295.226 9.2566V7.80823C295.226 7.54119 295.009 7.32471 294.742 7.32471L293.292 7.32471C293.025 7.32471 292.809 7.54119 292.809 7.80823Z' fill='url(%23paint28_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M303.134 7.80823L303.134 9.2566C303.134 9.52364 303.35 9.74024 303.617 9.74024H305.067C305.334 9.74024 305.551 9.52364 305.551 9.2566V7.80823C305.551 7.54119 305.334 7.32471 305.067 7.32471L303.617 7.32471C303.35 7.32471 303.134 7.54119 303.134 7.80823Z' fill='url(%23paint29_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M313.702 7.80823L313.702 9.2566C313.702 9.52364 313.918 9.74024 314.185 9.74024L315.635 9.74024C315.902 9.74024 316.119 9.52364 316.119 9.25659V7.80823C316.119 7.54119 315.902 7.32471 315.635 7.32471L314.185 7.32471C313.918 7.32471 313.702 7.54119 313.702 7.80823Z' fill='url(%23paint30_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M324.027 7.80823V9.25659C324.027 9.52364 324.243 9.74024 324.511 9.74024L325.961 9.74024C326.227 9.74024 326.445 9.52364 326.445 9.25659V7.80823C326.445 7.54119 326.227 7.32471 325.961 7.32471L324.511 7.32471C324.243 7.32471 324.027 7.54119 324.027 7.80823Z' fill='url(%23paint31_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M334.352 7.80823V9.25659C334.352 9.52364 334.569 9.74024 334.836 9.74024H336.286C336.553 9.74024 336.77 9.52364 336.77 9.25659L336.77 7.80823C336.77 7.54119 336.553 7.32471 336.286 7.32471L334.836 7.32471C334.569 7.32471 334.352 7.54119 334.352 7.80823Z' fill='url(%23paint32_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M344.678 7.80823V9.25659C344.678 9.52364 344.894 9.74024 345.161 9.74024H346.611C346.878 9.74024 347.095 9.52364 347.095 9.25659L347.095 7.80823C347.095 7.54119 346.878 7.32471 346.611 7.32471L345.161 7.32471C344.894 7.32471 344.678 7.54119 344.678 7.80823Z' fill='url(%23paint33_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M355.003 7.80823V9.25659C355.003 9.52364 355.219 9.74024 355.486 9.74024H356.936C357.203 9.74024 357.42 9.52364 357.42 9.25659L357.42 7.80823C357.42 7.54119 357.203 7.32471 356.936 7.32471L355.486 7.32471C355.219 7.32471 355.003 7.54119 355.003 7.80823Z' fill='url(%23paint34_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M365.328 7.80823V9.25659C365.328 9.52363 365.544 9.74024 365.811 9.74024H367.261C367.528 9.74024 367.745 9.52363 367.745 9.25659V7.80823C367.745 7.54119 367.528 7.32471 367.261 7.32471L365.811 7.32471C365.544 7.32471 365.328 7.54119 365.328 7.80823Z' fill='url(%23paint35_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M375.896 7.80823V9.25659C375.896 9.52363 376.112 9.74023 376.38 9.74023H377.83C378.096 9.74023 378.313 9.52363 378.313 9.25659V7.80823C378.313 7.54119 378.096 7.32471 377.829 7.32471L376.38 7.32471C376.112 7.32471 375.896 7.54119 375.896 7.80823Z' fill='url(%23paint36_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M386.221 7.80823V9.25659C386.221 9.52363 386.438 9.74023 386.705 9.74023H388.155C388.421 9.74023 388.639 9.52363 388.639 9.25659V7.80823C388.639 7.54119 388.421 7.32471 388.155 7.32471L386.705 7.32471C386.438 7.32471 386.221 7.54119 386.221 7.80823Z' fill='url(%23paint37_linear_8295_70635)'/%3E%3Cdefs%3E%3ClinearGradient id='paint0_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint1_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint2_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint3_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint4_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint5_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint6_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint7_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint8_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint9_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint10_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint11_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint12_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint13_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint14_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint15_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint16_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint17_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint18_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint19_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint20_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint21_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint22_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint23_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint24_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint25_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint26_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint27_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint28_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint29_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint30_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint31_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint32_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint33_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint34_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint35_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint36_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint37_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3C/defs%3E%3C/svg%3E%0A\")}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:first-child{position:relative;z-index:3}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:absolute;height:100%;width:100%;top:0;left:-24px}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{content:\"\";display:block;z-index:1;position:absolute;top:-64px;left:0;width:150%;height:calc(100% + 160px);background-color:rgb(32 44 53 / .9)}.sc-body__content .heading-3-v2,.sc-hero__headline .heading-1-v2{text-transform:none}.sc-body__content span.snowflake-image-caption{display:block!important;font-style:italic}.sc-body__content .snowflake-text p+ul{margin-top:24px!important;padding-left:16px!important}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#e9eaeb!important;font-size:16px}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification.is-large .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#fff!important;font-size:18px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child{display:flex;justify-content:flex-start;align-items:center;gap:8px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child::before{content:\"\";display:inline-block;width:16px;height:16px;background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='16' viewBox='0 0 16 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M8 0C3.58146 0 0 3.58146 0 8C0 12.4185 3.58146 16 8 16C12.4185 16 16 12.4185 16 8C16 3.58146 12.4185 0 8 0ZM12.7184 5.91984L7.33471 11.3026C7.31293 11.3244 7.31293 11.3454 7.29198 11.3454L7.20653 11.4308C6.94933 11.688 6.54132 11.7525 6.21962 11.6235C6.11238 11.5808 6.00514 11.5163 5.9197 11.4308L5.83425 11.3454C5.83425 11.3454 5.83425 11.3236 5.81246 11.3236L3.28149 8.79347C2.93799 8.44997 2.93799 7.87107 3.28149 7.50664L3.36694 7.42119C3.71044 7.07769 4.28934 7.07769 4.65377 7.42119L6.58401 9.35143L11.3877 4.5477C11.7312 4.2042 12.3101 4.2042 12.6746 4.5477L12.76 4.63315C13.0826 4.99758 13.0828 5.55541 12.7184 5.91984Z' fill='%230E8A16'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-color:#fff;border-radius:100%}.sc-hero__byline{padding-top:8px}.sc-hero__byline p{color:#e2e2e2;margin-top:0!important}.sc-hero pre[class*=language-]{overflow:visible}.snowflake-code-snippet,.snowflake-code-snippet code,.snowflake-code-snippet pre{font-size:16px}.sc-hero__code-snippet:not(pre)\u003Ecode[class*=language-],.sc-hero__code-snippet pre[class*=language-]{background:0 0}.sc-hero__code-snippet{opacity:.8;background-color:transparent!important;position:absolute;top:0;right:0;width:100%;animation:240s linear 1s forwards slow-scroll}.sc-hero__button-container .snowflake-flexible-column-container-items{padding:0 0 24px;margin-top:-8px;margin-left:24px}.sc-sidebar__partner-logo{width:100%;max-width:140px;margin-top:8px}.sc-sidebar__partner-logo .cmp-image__image{border-radius:0}.sc-tag-cluster.snowflake-text ul{list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px;margin:0}.sc-tag-cluster.snowflake-text li{color:#373f41;border-radius:4px;display:inline-block;padding:6px;text-transform:uppercase;letter-spacing:1px;font-size:12px!important;line-height:12px!important;margin:0!important;background-color:#f3f3f3}.sc-body .share-icon svg{height:24px;cursor:pointer}.sc-body .share-icon svg:hover path{fill:var(--ui-02)}.sc-overview__webinar-promo-banner{align-items:center;border:1px solid #ccc;padding:var(--spacing-02)}.sc-overview__webinar-promo-banner .snowflake-content-chip-image{max-width:32px;margin-right:var(--spacing-02);line-height:0}.sc-overview__webinar-promo-banner .snowflake-content-chip-image__image,.summit-speaker-card .snowflake-card-v2-advanced-image__image{aspect-ratio:1}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{font-size:14px;font-family:Lato,sans-serif}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child){font-weight:400}.sc-overview__webinar-promo-banner .snowflake-content-chip-button .snowflake-button-container{font-size:14px!important}.diagram-group__button{position:absolute;bottom:24px;right:24px;background-color:#212c35!important}.section--mountains-bottom,.summit-hp-hero{position:relative}.sc-cert-banner{background-color:#212d35;border-radius:8px;padding:24px;overflow:hidden}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;align-items:center}:root{--text-secondary:#706f6f;--summit-bg-ltblue:#eaf8fd;--summit-bg-blue:#249edc;--summit-border:#d2d1d4;--summit-border-radius:8px;--summit-card-padding:32px;--summit-card-padding-sm:28px}.section--mountains-bottom::after,.section--mountains-bottom::before{content:\"\";display:block;position:absolute;bottom:-1px;max-width:400px;background-size:100% auto;height:100%;width:30%;line-height:0;background-repeat:no-repeat}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center;align-items:center}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;margin:0 8px!important}.button-group .snowflake-button-container{font-family:Texta,sans-serif}.section--summit-bg-ltblue{background-color:var(--summit-bg-ltblue)}.section--summit-bg-blue,.summit-hero-secondary{background-color:var(--summit-bg-blue)}.section--mountains-bottom::before{left:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M401.523 308.761H0V0L181.63 182.431L228.479 135.531L401.523 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom left}.section--mountains-bottom::after{right:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 308.761H401.523V0L219.893 182.431L173.044 135.531L0 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom right}.summit-hp-hero{overflow:hidden}.summit-hero__bg-video{position:absolute;top:50%;left:50%;width:120%;height:100%;opacity:.3;transform:translate(-50%,-50%)}.summit-hero__bg-svg,.summit-prefooter__bg-image,.summit-secondary-hero__bg-image{position:absolute;bottom:0;left:0;width:100%}.summit-hp-promo-banner__headline .heading-4-v2{font-weight:900}.summit-hero-secondary .hero-lottie__left{position:absolute;bottom:0;left:0;width:30%;line-height:0}.summit-timeline__card::after,.summit-timeline__card::before{bottom:0;left:50%;position:absolute;display:block;background-color:var(--ui-01);content:\"\"}.summit-hero-secondary .snowflake-text p{font-size:24px!important;line-height:32px!important;max-width:720px;margin:0 auto}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:center}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;max-width:25%}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid #fff}.summit-timeline__card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding);position:relative;background-color:#fff}.summit-timeline__card::before{width:20px;height:20px;border-radius:100%;transform:translate(-50%,50%)}.summit-timeline__card::after{width:3px;height:50px;transform:translate(-50%,100%)}.summit-timeline-card__icon{width:48px;height:48px}.summit-timeline-card__headline .heading-3-v2{font-size:32px}.faq-group{border:1px solid var(--ui-12);border-radius:4px;background-color:#fff}.faq-group__question{padding:24px}.faq-group__question:hover{color:var(--ui-01);cursor:pointer}.faq-group__question .heading-4-v2,.faq-group__question .heading-5-v2{position:relative;padding-right:64px}.faq-group__question .heading-4-v2::after,.faq-group__question .heading-5-v2::after{content:\"\";display:block;width:32px;height:32px;background-image:url(\"data:image/svg+xml,%3Csvg width='29' height='16' viewBox='0 0 29 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M14.16 14.6807C14.2537 14.7957 14.3719 14.8884 14.506 14.952C14.64 15.0157 14.7866 15.0487 14.935 15.0487C15.0834 15.0487 15.2299 15.0157 15.3639 14.952C15.498 14.8884 15.6162 14.7957 15.71 14.6807V14.6807L28.51 2.00068C29.07 1.43068 29.07 .92068 28.51 .44068C27.95 -.0393204 27.43 -.11932 26.96 .44068L14.94 12.0007L2.99996 .45068C2.90725 .322624 2.7855 .218374 2.6447 .146483C2.50389 .0745926 2.34805 .0371094 2.18996 .0371094C2.03187 .0371094 1.87603 .0745926 1.73522 .146483C1.59442 .218374 1.47267 .322624 1.37996 .45068C.819961 .93068 .819961 1.45068 1.37996 2.01068L14.16 14.6807Z' fill='black'/%3E%3C/svg%3E%0A\");background-size:80% auto;background-repeat:no-repeat;background-position:center;position:absolute;top:-2px;right:0;transition:.3s 150ms}.faq-group__question .heading-5-v2::after{top:-4px}.faq-group__answer{max-height:0;overflow:hidden;width:95%;padding:0 24px;transition:.5s}.faq-group__answer\u003Espan{display:block;padding-bottom:24px}.is-open .faq-group__answer{max-height:600px;transition:1s}.is-open .faq-group__question .heading-4-v2::after,.is-open .faq-group__question .heading-5-v2::after{transform:rotate(180deg);transition:.3s}.summit-agenda{box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);border-radius:8px;background-color:#fff;max-width:980px;margin-left:auto;margin-right:auto;padding:40px;width:90%}.agenda-item{border-radius:8px;background-color:#d4f0fa;padding:16px;border-left:4px solid var(--ui-01);position:relative}.summit-pricing-block__tile.is-past,.summit-pricing-block__tile.is-upcoming{pointer-events:none;border-color:#d2d1d4}p.agenda-item__time{width:25%;font-family:Texta!important;font-size:32px!important;font-weight:900!important;text-transform:uppercase!important;max-width:140px}@media screen and (max-width:991px){#partnerResources .section--resource-hub .snowflake-button-link .snowflake-button-container{font-size:14px!important;line-height:20px!important;margin-top:4px}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items{display:flex;flex-direction:column}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items\u003Ediv{width:100%}.sc-cert-banner__left{text-align:center}.sc-cert-banner__left .solution-center-hero__certification .snowflake-title-v2-line{justify-content:center}.summit-hero__bg-video{width:200%}.summit-leadership-grid .snowflake-flexible-column-container-items{grid-template-columns:repeat(2,1fr)}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:50%!important;max-width:50%!important}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:none!important}.summit-agenda{padding:24px}p.agenda-item__time{font-size:24px!important;width:auto;white-space:nowrap;padding-right:24px}}.agenda-item\u003Espan{display:flex;align-items:center}.summit-add-on-block,.summit-pricing-block{border:1px solid #d2d1d4;border-radius:8px;overflow:hidden;box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);background-color:#fff}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 20px 20px}.summit-pricing-block__tile{padding:24px 20px;border-radius:4px;background:#fff;border:1px solid var(--ui-01);position:relative;transition:background-color .3s}.summit-pricing-block__tile:hover{background-color:var(--ui-01);transition:background-color .3s}.summit-pricing-block__tile.is-past{background-color:#d4f0fa}.summit-pricing-block__tile:hover .black-blue-text-color .snowflake-title-v2-line{color:#fff!important;transition:color .3s}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::after,.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::after,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-pricing-block__tile.is-past .snowflake-content-chip-button,.summit-pricing-block__tile.is-upcoming .snowflake-content-chip-button,.summit-speaker-card .snowflake-card-v2-advanced-tag-indicator{display:none}.summit-pricing-block__tile.is-past .black-blue-text-color .snowflake-title-v2-line{color:#7cc7eb!important}.summit-pricing-block__tile.is-upcoming .black-blue-text-color .snowflake-title-v2-line{color:#8c8c8c!important}.summit-pricing-block__aside{background-color:#d4f0fa;border:1px solid #d2d1d4;border-radius:8px;padding:24px;width:100%}.summit-pricing-block__aside li::marker{color:var(--ui-01)}.summit-pricing-block__aside-headline .heading-5-v2{font-weight:900;margin-bottom:12px}.summit-pricing-block__header{background:#000;padding:24px 40px}.summit-pricing-block__header .heading-4-v2{font-weight:900;letter-spacing:.5px}.bwwidth100,.snowflake-mega-nav-dropdown-footer-content,.summit-pricing-block__tile .black-blue-text-color{width:100%}.summit-pricing-block__tile .heading-5-v2{position:static}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:first-child{text-transform:uppercase;font-weight:900!important;letter-spacing:.25px;font-size:24px!important}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:nth-child(2){margin-top:8px;font-family:Lato,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:16px}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{font-weight:900!important;font-size:40px!important}.snowflake-mega-nav-nav-item\u003Ea:hover .snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title,.summit-pricing-block__tile:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:var(--ui-01)!important}.summit-pricing-block__tile:hover:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:#fff!important}.summit-pricing-block__tile.is-past .heading-5-v2 span.snowflake-title-v2-line:last-child{text-decoration:line-through}.summit-pricing-block__tile .snowflake-content-chip-button{margin-top:0;white-space:nowrap;display:none}.snowflake-card-v2-advanced.no-link{pointer-events:none!important}.snowpro-card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding-sm);display:flex;height:100%}.snowpro-card__headline{margin:24px 0 12px}.snowpro-card__pricing{margin-top:48px}.snowpro-card .snowflake-text .snowpro-card__price{color:var(--ui-01);font-weight:900;font-size:40px!important;font-family:Texta,sans-serif}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid var(--summit-border)}.summit-stat-card{padding:0 40px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:first-child{font-size:64px;line-height:52px;margin-bottom:8px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:last-child{font-size:32px;line-height:30px;margin-bottom:16px}.summit-speaker-card .snowflake-card-v2-advanced-title{margin-bottom:var(--spacing-01)}.summit-add-on-card{padding:24px;border:1px solid #d2d1d4;border-radius:8px}.summit-add-on__subhead{padding-left:40px;padding-right:40px}.partner-card__logo-grid,.partner-card__logo-single{padding:40px}.partner-card__logo-grid .snowflake-image-container .cmp-image__image,.partner-card__logo-single .snowflake-image-container .cmp-image__image{border-radius:0;max-width:240px;margin:0 auto}.partner-card\u003E.container,.partner-card\u003E.container\u003E.aem-container,.partner-card\u003E.container\u003E.cmp-container{height:100%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;gap:24px;align-items:stretch}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap;gap:40px 24px;justify-content:center;align-items:center}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important}.partner-card{border-radius:8px;border:1px solid #d2d1d4;overflow:hidden;height:100%;background-color:#fff}.partner-card__header{padding:16px 24px;border-bottom:1px solid #d2d1d4}.partner-card__header.is-purple{background-color:#7d44cf}.partner-card__header h4{display:flex;flex-direction:row!important;align-items:center;gap:12px}.partner-card__header h4::before{vertical-align:middle;content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='black'/%3E%3C/svg%3E%0A\")}.partner-card__header.is-purple h4::before{background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='white'/%3E%3C/svg%3E%0A\")}.sf-blue-mountains{background-size:90% auto;background-repeat:no-repeat;background-position:center bottom;background-image:url(\"data:image/svg+xml,%3Csvg width='1361' height='410' viewBox='0 0 1361 410' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1360.25 410L1065.53 114.309L976.256 203.875L773.049 0L364.393 410H1360.25Z' fill='%233AA8DF'/%3E%3Cpath d='M274.778 410L137.467 272.238L.15625 410H274.778Z' fill='%233AA8DF'/%3E%3C/svg%3E%0A\")}.bwalignr,.main-pr-body .bwalignr{text-align:right}.bwblockalignl{margin-left:0;margin-right:auto}.bwcellpmargin{margin-top:0;margin-bottom:0}.bwlistdisc{list-style-type:disc}.bwpadb3{padding-bottom:4px}.bwpadb4{padding-bottom:5px}.bwpadl0{padding-left:0}.bwpadl3{padding-left:15px}.bwpadl6{padding-left:30px}.bwpadl9{padding-left:45px}.bwpadl12{padding-left:60px}.bwpadr0{padding-right:0}.bwtablemarginb{margin-bottom:10px}.bwvertalignb{vertical-align:bottom}.bwvertalignt{vertical-align:top}.bwsinglebottom{border-bottom:1pt solid #000}.bwdoublebottom{border-bottom:2.25pt double #000}.bwwidth1{width:1%}.bwwidth2{width:2%}.bwwidth6{width:6%}.bwwidth7{width:7%}.bwwidth8{width:8%}.bwwidth10{width:10%}.bwwidth12{width:12%}.bwwidth32{width:32%}.bwwidth44{width:44%}.bwwidth72{width:72%}.bwwidth97{width:97%}.main-pr-body{font-size:18px;line-height:26px}.main-pr-body img{display:block;width:100%;height:auto!important;border-radius:var(--small-border-radius)}.main-pr-body table{width:100%;display:block}.main-pr-body tbody{background-color:#f7f7f7}.main-pr-body .bwsinglebottom{border-bottom:1pt solid #000!important}.main-pr-body td.bwwidth44{padding-right:40px}.main-pr-body .bw-release-story{font-family:Lato,sans-serif}.main-pr-body .bw-release-story sup,.snowflake-mega-nav-dropdown-header-content-right a{white-space:nowrap}.main-pr-body .bw-release-story\u003E*,.main-pr-body\u003Espan\u003E*{margin-bottom:2rem!important}.snowflake-text.main-pr-body tbody,.snowflake-text.main-pr-body tbody p{font-size:14px!important;line-height:20px!important;width:100%;display:block}.press-body .snowflake-flexible-column-container-items{gap:var(--spacing-08)}.about-snowflake{border:1px solid #ccc;background-color:var(--ui-background-05);padding:24px;border-radius:8px;margin-top:0}.about-snowflake__logo{max-width:140px;margin-top:16px}.hero--press .snowflake-hero-system-inner{max-width:1408px;margin:0 auto!important}#arcticNavItem{flex-direction:column}#arcticNavItem::before{content:\"Featured Open Source Technologies\";display:block;margin-top:48px;margin-bottom:24px;font-size:16px!important;line-height:16px!important;font-weight:800!important;text-transform:uppercase}@media screen and (min-width:768px){.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:relative;height:100%;top:auto;left:auto;width:auto}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{background:linear-gradient(180deg,#202c35 -7.5%,#fff0 51.25%,#202c35 107.69%)}.sc-hero__byline\u003Espan{display:flex;flex-wrap:wrap}.sc-hero__byline p:not(:last-child)::after{content:\"|\";margin:0 12px;opacity:.5}.sc-hero__button-container .snowflake-flexible-column-container-items{position:absolute;bottom:0;padding:0;margin:0 24px 0 0}.sc-hero__button-container .hero-watch-the-demo{padding:12px 16px!important;float:right;margin-bottom:48px;background-color:rgb(35 45 54 / .8)}.summit-overview-stat{padding:0 40px}.summit-timeline{border-bottom:3px solid var(--ui-01);margin-bottom:64px}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 40px 40px}#arcticNavItem::before{font-size:12px!important;margin-bottom:8px;margin-top:16px}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{line-height:20px!important}.snowflake-card .heading-2.snowflake-title-line{font-size:24px!important;line-height:28px!important}}@media screen and (min-width:992px){.hp-hero__eyebrow a{gap:12px;margin-left:0;margin-right:0}.hp-hero__eyebrow a::after{content:\"\";background-image:url(\"data:image/svg+xml,%3Csvg width='6' height='11' viewBox='0 0 6 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M5.49134 5.79438C5.53447 5.75922 5.56923 5.71489 5.5931 5.66463C5.61697 5.61436 5.62935 5.55941 5.62935 5.50376C5.62935 5.44811 5.61697 5.39316 5.5931 5.34289C5.56923 5.29263 5.53447 5.2483 5.49134 5.21314L.736339 .413136C.522589 .203135 .331339 .203135 .151339 .413136C-.0286612 .623135 -.0586612 .818135 .151339 .994386L4.48634 5.50188L.155089 9.97938C.107068 10.0142 .0679743 10.0598 .0410153 10.1126C.0140562 10.1654 0 10.2238 0 10.2831C0 10.3424 .0140562 10.4009 .0410153 10.4537C.0679743 10.5065 .107068 10.5521 .155089 10.5869C.335089 10.7969 .530089 10.7969 .740089 10.5869L5.49134 5.79438Z' fill='black'/%3E%3C/svg%3E%0A\");display:inline-block;width:12px;height:12px;background-repeat:no-repeat;background-size:auto 100%;background-position:left center}.promo-banner--homepage{padding-top:32px}.homepage-banner-offset-container::after{height:50%}#storyHighlights{padding:2rem}.body-display-v2.snowflake-quote-item-quote-text{line-height:28px!important}.snowflake-hero-system-headline .heading-1-v2{line-height:48px;font-size:54px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-content{flex-direction:row;justify-content:space-between;align-items:center;width:100%}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{flex-direction:row}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child)::before{content:\"|\";margin:0 6px}.sc-cert-banner{padding:40px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{margin:0!important;width:50%}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;padding-right:24px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:240px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{width:70%;padding-left:40px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{width:30%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important;display:flex}.summit-pricing-block__tile .snowflake-content-chip-content{display:flex;flex-direction:row;align-items:center;width:calc(100% - 200px)}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{position:absolute;top:50%;transform:translate(0,-50%);right:40px}.press-body\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:sticky;top:120px}.snowflake-mega-nav-navigation-title:hover{color:var(--ui-01)}}@media screen and (min-width:1024px){.about-snowflake{padding:28px}.about-snowflake__logo{max-width:none;padding:0 0 0 48px;margin-bottom:0}.hero--press .snowflake-hero-system-layout-70-30 .snowflake-hero-system-content-container{width:85%}.snowflake-hero-system{padding-bottom:var(--spacing-04);padding-top:var(--spacing-07)}.hero--press .display-2-v2{font-size:64px;line-height:56px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container{flex-direction:row;flex-wrap:nowrap;align-items:center}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:280px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;margin-bottom:0!important}#polarisNavItem{margin-top:40px}.snowflake-mega-nav-nav-item-description{line-height:18px!important}.snowflake-mega-nav-column-items{gap:var(--spacing-01);grid-gap:var(--spacing-01)}.snowflake-mega-nav-navigation-title{text-transform:none}}div[id*=blueIcon] .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01);padding:8px}div[id*=blueIcon]:hover .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01)!important}.snowflake-mega-nav-nav-item-icon__inner{border-radius:4px;background:var(--ui-background-05);padding:6px}.snowflake-mega-nav-nav-item:hover .snowflake-mega-nav-nav-item-icon__inner{background:#fff!important}.snowflake-mega-nav-nav-item-icon.snowflake-image-container{height:40px;width:40px}.snowflake-mega-nav-dropdown-footer-links\u003E.snowflake-button-link\u003E.snowflake-button-container{font-size:16px!important;font-family:Texta!important;font-weight:800!important}.snowflake-mega-nav-dropdown-footer-icon.snowflake-image-container{margin-right:8px;width:40px!important;height:40px!important}#viewAllCapabilities a:hover{background:0 0!important}#platformFooter .snowflake-title-v2 .snowflake-title-v2-line:last-child{font-family:Lato;font-size:14px;font-weight:500}#platformFooter .snowflake-mega-nav-dropdown-footer-links{flex-grow:1;justify-content:flex-end;align-items:center}#platformFooter .snowflake-mega-nav-dropdown-footer-content{flex-direction:row}#offset,#open-source{flex-direction:column;border-top:1px solid #ccc}#offset::before,#open-source::before{content:\" \";display:block;width:100%;font-weight:800!important;font-size:12px!important;line-height:14px;text-transform:uppercase;white-space:nowrap;margin-top:16px;margin-bottom:8px}#open-source::before{content:\"Open Source Technologies\"}.snowflake-mega-nav-dropdown-menu-close-button{margin:var(--spacing-04) 0 var(--spacing-03)}.snowflake-mega-nav-column{gap:var(--spacing-02)!important}.snowflake-mega-nav-nav-item\u003Ea{width:100%;margin-left:-8px;padding:8px;border-radius:4px}.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:var(--ui-background-05)}.snowflake-mega-nav-nav-item-description{margin-top:2px;display:block}#promobanner_overflowBottomDarkBlue::before{content:'';display:block;position:absolute;bottom:0;left:0;width:100%;height:50%;background:#212d35}#promobanner_overflowTopDarkBlue::before{content:'';display:block;position:absolute;top:0;left:0;width:100%;height:50%;background:#212d35}.overview-card\u003Ediv{box-shadow:0 0 14px 0 rgba(0,0,0,.10);background-color:#fff;border-radius:16px;overflow:hidden}.overview-card-text{padding:40px}.overview-card-image img{border-radius:0 !important}.overview-card-text h3,.overview-card-text .heading-3-v2{font-size:18px;line-height:1.1;margin-top:0}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"mega_header":{"additionalClasses":"heap-nav-header","id":"container-c34ee66432","layout":"SIMPLE","appliedCssClassNames":"snowflake-header-container white",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-495e4cfa95",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-127411c47f","enableDropdown":true,"nav_column_container":{"id":"container-55f3c381ed","layout":"SIMPLE",":items":{"nav_column":{"additionalClasses":"nav-platform-sidebar","numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","id":"container-b0999bc080","layout":"SIMPLE",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-8c646cfa23","additionalClasses":"nav-item__platform-parent is-platform","linkDescription":"Develop AI products, apps and more on a fully managed platform that securely connects businesses globally — across any type or scale of data.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/platform/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The Snowflake Platform"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-22421a2473","additionalClasses":"nav-item nav-item--si is-si","linkDescription":"All your knowledge. One trusted enterprise agent.","flag":"NOW GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-cowork/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoWork"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_836345186":{"id":"nav-item-52a277038e","additionalClasses":"blue-icon is-analytics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/analytics/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Analytics"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2":{"id":"nav-item-9eabc9e008","additionalClasses":"blue-icon is-ai","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/ai/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1314771042":{"id":"nav-item-37e89e2a4c","additionalClasses":"blue-icon is-data-eng","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/data-engineering/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Data Engineering"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634":{"id":"nav-item-f3c621b9a6","additionalClasses":"blue-icon is-apps-collab","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/applications-and-collaboration/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Applications & Collaboration"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_2013333117":{"id":"nav-item-eea1ed5ca4","additionalClasses":"blue-icon is-transactions","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/transactions/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Transactions"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646","nav_item","nav_item_copy_copy_2_836345186","nav_item_copy_copy_2","nav_item_copy_copy_2_1314771042","nav_item_copy_144634","nav_item_copy_144634_2013333117"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","navColumnTitle":"Featured Capabilities","numberOfSubColumns":"one-column","id":"container-a41c26438d","layout":"SIMPLE",":items":{"nav_item_copy_212715":{"id":"nav-item-030291610e","additionalClasses":"is-cortex-code","linkDescription":"Snowflake-native AI coding agent ","flag":"New","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/snowflake-coco/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake CoCo"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-09cce737d8","additionalClasses":"is-cortex-ai","linkDescription":"Instant access to industry-leading LLMs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/cortex/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Cortex AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590635":{"id":"nav-item-86bda42e1b","additionalClasses":"is-marketplace","linkDescription":"Third-party data sources connected within minutes","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/marketplace/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketplace"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-61e0f18560","additionalClasses":"is-snowpark","linkDescription":"Libraries and code execution environments that run Python and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/snowpark/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowpark"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_983061516":{"id":"nav-item-c7f246f782","additionalClasses":"is-streamlit","linkDescription":"Framework for transforming Python scripts into web apps","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Streamlit"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_212715","nav_item","nav_item_copy_660590635","nav_item_copy_660590","nav_item_copy_660590_983061516"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_692142673":{"navColumnTitle":" ","numberOfSubColumns":"one-column","id":"container-cc7fa1047e","layout":"SIMPLE",":items":{"nav_item_copy_660590_1739526127":{"id":"nav-item-32d911eb4e","additionalClasses":"is-postgres","linkDescription":"Fully compatible open source Postgres running on Snowflake","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/postgres/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Postgres"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-686c0c9744","additionalClasses":"is-dcr","linkDescription":"Streamlined model development and MLOps from a centralized UI","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/end-to-end-ml-workflows/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake ML"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_212715":{"id":"nav-item-5cb0135384","additionalClasses":"is-openflow","linkDescription":"Effortless data movement for integrations","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/openflow/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Openflow"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-2d2921c71b","additionalClasses":"is-notebooks","linkDescription":"Interactive dev environment for data and AI teams","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/notebooks/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Notebooks"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-1299dda215","propertiesId":"workload-nav-1","additionalClasses":"is-native-apps","linkDescription":"End-to-end, Snowflake-native app creation and distribution","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/native-apps/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Native Apps"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_660590_1739526127","nav_item_copy_185565","nav_item_copy_212715","nav_item_copy_660590","nav_item_258535199"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_782221091":{"navColumnTitle":" ","numberOfSubColumns":"one-column","id":"container-20dd916cfa","layout":"SIMPLE",":items":{"nav_item_copy":{"id":"nav-item-0b88e9b85b","additionalClasses":"is-light-gray-icon is-horizon-catalog","linkDescription":"Universal AI catalog","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Catalog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_1293798742":{"id":"nav-item-ef7165be8a","additionalClasses":"is-snowflake-ml","linkDescription":"Governed context layer that keeps AI, BI and data apps working from one truth","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/horizon-context/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Horizon Context"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c":{"id":"nav-item-d0f113d0d7","additionalClasses":"is-unistore","linkDescription":"Unify transactional and analytical workloads in Snowflake for enhanced simplicity","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/unistore/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Unistore"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1443811525":{"id":"nav-item-26d211ca5a","additionalClasses":"is-observe","linkDescription":"AI-powered observability for faster production troubleshooting","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/observe/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Observe"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_511717659_c_1006104884":{"id":"nav-item-abc021769a","additionalClasses":"is-observe","linkDescription":"Use any engine on a single governed data copy","flag":"Now GA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/use-cases/interoperable-lakehouse/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Interoperable Lakehouse"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy","nav_item_copy_660590_1293798742","nav_item_511717659_c","nav_item_511717659_c_1443811525","nav_item_511717659_c_1006104884"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy_copy","nav_column_692142673","nav_column_782221091"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Product"},"nav_dropdown_menu_2":{"id":"nav-dropdown-menu-9c099b0a8d","enableDropdown":true,"nav_column_container":{"id":"container-c5f5ed8d52","layout":"SIMPLE",":items":{"nav_column":{"navColumnTitle":"INDUSTRIES","numberOfSubColumns":"one-column","minWidth":"280","id":"container-bec25c7b55","layout":"SIMPLE","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"nav_item_copy_361384_2056203141":{"id":"nav-item-58c132ba9b","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"All Industries"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-d19ade2acd","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/advertising-media-entertainment/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Advertising, Media & Entertainment"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-331a992ba9","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/financial-services/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Financial Services"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-69b900ef30","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/healthcare-and-life-sciences/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Healthcare & Life Sciences"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1533429516":{"id":"nav-item-e8275c5f42","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/manufacturing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Manufacturing"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1444458226":{"id":"nav-item-3339256e3d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/public-sector/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Public Sector"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1149488919":{"id":"nav-item-83c597a726","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Retail & Consumer Goods"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_57417040":{"id":"nav-item-dc93281666","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Technology"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384674":{"id":"nav-item-6c3032421d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/telecom/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Telecom"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384":{"id":"nav-item-44a6ee7b85","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/travel-hospitality/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Travel & Hospitality"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_361384_2056203141","nav_item","nav_item_copy","nav_item_copy_1970515619","nav_item_copy_1533429516","nav_item_copy_1444458226","nav_item_copy_1149488919","nav_item_copy_57417040","nav_item_copy_361384674","nav_item_copy_361384"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy":{"navColumnTitle":"DEPARTMENTS","numberOfSubColumns":"one-column","minWidth":"160","id":"container-14bb3948f5","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-50747f3314","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/finance/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Finance"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-421b957372","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/information-technology/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"IT"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-1d2d20164c","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/solutions/departments/marketing/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Marketing"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_833417450":{"navColumnTitle":"Enablement Solutions","numberOfSubColumns":"one-column","id":"container-43fa38c8ba","layout":"SIMPLE",":items":{"nav_item_copy_107772":{"id":"nav-item-387130b962","linkDescription":"Confident migration to a unified platform","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/migrate-to-the-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Migrate to the AI Data Cloud"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Cloud icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_107772/icon.coreimg.svg/1723828484100/nav-icon-cloud.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-a345af0830","linkDescription":"Snowflake experts to help you accelerate and achieve business goals","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/services-delivery/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Services Delivery"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Migrate icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_833417450/nav_item_copy_copy/icon.coreimg.svg/1768354429188/nav-icon--migrate.svg","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"navColumnTitle":"PARTNER SOLUTIONS","numberOfSubColumns":"one-column","id":"container-0d46ea1bd0","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-d58fa22ea7","linkDescription":"Programs with product, solutions and cloud partners","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Partner Network"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Partner Network icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1723828498700/nav-icon--partner-network.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-54e7d39027","linkDescription":"Partners, apps and solutions for enhanced deployment","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/all-partners/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Partner Finder"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Partner Finder icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1726173927645/nav-icon--partner-finder.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-e4a94e7307","linkDescription":"Live and virtual events","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/event-partnership-opportunities/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Event Partnership Opportunities"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Calendar icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/nav_dropdown_menu_2/nav_column_container/nav_column_copy_copy/nav_item_copy_1970515619/icon.coreimg.svg/1726173935655/nav-icon--events.svg","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-70adbaa96f","enableDropdown":true,"nav_column_container":{"id":"container-bcc2328ac6","layout":"SIMPLE",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","id":"container-a48636e966","layout":"SIMPLE",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-1eece91aa5","additionalClasses":"nav-item__platform-parent-why-sf","linkDescription":"Collaborate locally and globally to reveal new insights, create previously unforeseen business opportunities, and identify your customers with seamless experiences.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Why Snowflake"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","numberOfSubColumns":"two-columns","maxWidth":"1200","id":"container-d372390ff9","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-db09f70bb2","propertiesId":"testID","linkDescription":"Case studies and videos showcasing how global organizations use Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Customers"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Customer icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1739839279367/nav-icon--partner-network.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-a2344a7e5e","propertiesId":"workload-nav-1","linkDescription":"Learn how to connect, share and integrate the data and apps on the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/what-is-data-cloud/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"The AI Data Cloud Explained"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Cloud icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_258535199/icon.coreimg.svg/1739840490955/nav-icon-cloud.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-dfe5db7625","linkDescription":"Comprehensive security through built-in features, robust cloud infrastructure protection, and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/snowflake-security-hub/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Security Hub"},"icon":{"id":"icon","lazyEnabled":true,"alt":"User with security lock icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565/icon.coreimg.svg/1758909528089/user-security-admins-ciso-icon.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-ced5267fad","additionalClasses":"is-light-gray-icon","linkDescription":"Maximize economic value through minimizing TCO and continuously optimizing price for performance","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/pricing-options/cost-and-performance-optimization/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Cost and Performance Optimization"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Cost Optimization icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1758909542267/nav-icon-cost-optimization-performance.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_903555964":{"id":"nav-item-a0acbf8ae1","linkDescription":"Startups building applications in the AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/startup-program/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Startups"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Launch","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751_c/nav_column_container/nav_column_copy_copy/nav_item_copy_185565_903555964/icon.coreimg.svg/1758732224323/launch.svg","height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_258535199","nav_item_copy_185565","nav_item_copy","nav_item_copy_185565_903555964"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy_copy"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Why Snowflake"},"item_1719961362824":{"id":"nav-dropdown-menu-34a718e5b1","enableDropdown":true,"nav_column_container":{"id":"container-887bac5b5a","layout":"SIMPLE",":items":{"nav_column_copy":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","minWidth":"124","id":"container-75544ba042","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-acd67e5e06","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Blog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_180298689":{"id":"nav-item-567552dbca","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/events/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Events"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_1639361946":{"id":"nav-item-2fa10e3e5e","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/support/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Support"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_680912746":{"id":"nav-item-762b2e5079","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/contact/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Contact us"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_180298689","nav_item_1639361946","nav_item_680912746"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_44600420__826130542":{"navColumnTitle":"Learn","numberOfSubColumns":"two-columns","id":"container-1b7c965089","layout":"SIMPLE",":items":{"nav_item_copy":{"id":"nav-item-3f3164b329","linkDescription":"Ebooks, videos, white papers and more","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Resource Library"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Notebooks icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy/icon.coreimg.svg/1736877128196/nav-icon--notebooks.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-2be8f99781","linkDescription":"Overview of Snowflake's educational offerings","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/training/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Training"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Training icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item/icon.coreimg.svg/1722385094416/nav-icon--training.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-65fe7f26be","linkDescription":"Expert-led discussions and demos across industries and use cases","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Webinars"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Webinars icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_144634_1984107859/icon.coreimg.svg/1759424691990/nav-icon--webinars.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-4641578520","linkDescription":"Snowflake's technical industry professional certifications","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/certifications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Certifications"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Certification icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_1438098918/icon.coreimg.svg/1722382780833/nav-icon--cert.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-88a3c4d443","linkDescription":"Weekly product demos showcasing key features and live Q&A ","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/demo/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Live Demos"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Live Demo icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_143809/icon.coreimg.svg/1759424359543/nav-icon--live-demo.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-f5ff123483","linkDescription":"Training courses for all levels, on-demand or instructor-led","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://learn.snowflake.com/en/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Snowflake University"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Education icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890638/icon.coreimg.svg/1722382769808/nav-icon--education.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-cf3117bded","linkDescription":"Instructor-led virtual workshops for exploring key Snowflake features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/virtual-hands-on-lab/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Hands-On Labs"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Hands-on Labs icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_189945/icon.coreimg.svg/1759388182903/nav-icon--labs.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890":{"id":"nav-item-42c803179d","linkDescription":"Academic papers written by Snowflake researchers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/publications/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake Research Publications"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Copy","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890/icon.coreimg.svg/1756326371387/copy.svg","height":"64","width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890_930852828":{"id":"nav-item-3465a4bd8f","linkDescription":"Informative articles about AI and data topics","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/fundamentals/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Fundamentals"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Document with list","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719961362824/nav_column_container/nav_column_44600420__826130542/nav_item_copy_333890_930852828/icon.coreimg.svg/1756853637155/data-sheet.svg","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},"nav_promo_section":{"id":"nav-promo-section-db9222409a","experience_fragment_1":{"id":"experiencefragment-e74b553388","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master1/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-3567770253","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"nav_promo_card":{"id":"nav-promo-card-c2da5866f7","openInNewWindow":true,"layout":"horizontal","headline":"Dev Day Virtual - June 25","description":"Don’t just hear about AI — build it. Luminary talks and hands-on labs","linkTitle":"Learn more","linkUrl":"/en/dev-day/americas-virtual/","image":{"id":"image","lazyEnabled":true,"alt":"dev day","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--de231e36-6645-4550-abd9-0f8de758ac66/web-dev-day-26-960x540-1x.png?preferwebp=true&quality=85","height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"],":type":"snowflake-site/components/container"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"experience_fragment_2":{"id":"experiencefragment-682f50938d","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-2/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-cdfd1ef537","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"nav_promo_card":{"id":"nav-promo-card-49f2eaadf8","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","lazyEnabled":true,"alt":"roi of gen ai and agents","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0c15edae-1a97-4739-8b16-c7f3941a6d9e/web-roi-of-gen-ai-and-agents-2026-r02-960x540.png?preferwebp=true&quality=85","height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"],":type":"snowflake-site/components/container"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"experience_fragment_3":{"id":"experiencefragment-3ae5eecd7b","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/navigation-promo-card-3/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-65520930d8","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"nav_promo_card":{"id":"nav-promo-card-d8e7bbf1fc","openInNewWindow":true,"layout":"horizontal","headline":"Startup 2026: AI Agents Mean Business","description":"Venture leaders weigh in on agentic AI. ","linkTitle":"Learn more","linkUrl":"/en/lp/building-startup-ai-age/","image":{"id":"image","lazyEnabled":true,"alt":"alt","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a320b404-dca1-4477-b033-c79708538657/web-startup-2026-960x540.png?preferwebp=true&quality=85","height":"540","width":"960",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"],":type":"snowflake-site/components/container"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","cq:metadata"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},":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-b61193e2fc","enableDropdown":true,"nav_column_container":{"id":"container-4f66a25895","layout":"SIMPLE",":items":{"nav_column_copy_copy":{"navColumnTitle":"Build","numberOfSubColumns":"one-column","id":"container-2504418f2e","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-63e3d0295b","propertiesId":"testID","linkDescription":"Overview of the dev resources you need to build and scale","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Snowflake for Developers"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Developers icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item/icon.coreimg.svg/1731362494574/nav-icon--devs.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-4a58f40772","linkDescription":"Reference architectures, use cases and best practices","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/guides/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Developer Guides"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Solution Center icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy_1855651246/icon.coreimg.svg/1761677891705/nav-icon--solution-center.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-fa955f189c","additionalClasses":"is-light-gray-icon","linkDescription":"The latest software versions, drivers, libraries and relevant docs","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/downloads/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Downloads"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Download icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy/nav_item_copy/icon.coreimg.svg/1731362660050/nav-icon-download.svg","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Learn","numberOfSubColumns":"one-column","id":"container-8a65cb33dd","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-f5d5b91793","propertiesId":"testID","linkDescription":"Reference docs, guides, tutorials and announcements","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://docs.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Documentation"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Docs icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item/icon.coreimg.svg/1731361950527/nav-icon--docs.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-132c599028","additionalClasses":"is-light-gray-icon","linkDescription":"Key projects Snowflake engineers maintain and support","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/open-source/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Open Source"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Open Source icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy/icon.coreimg.svg/1731365437016/nav-icon-open-source.svg","height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-69e8384ec7","additionalClasses":"is-light-gray-icon","linkDescription":"Online and in-person classes and workshops to upskill on Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/northstar/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Builder Education"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Northstar logo","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1367930678/nav_item_copy_copy/icon.coreimg.svg/1731362475640/nav-icon--northstar.svg","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Connect","numberOfSubColumns":"one-column","id":"container-64731b3237","layout":"SIMPLE",":items":{"nav_item":{"id":"nav-item-4d1f20c1d7","propertiesId":"testID","linkDescription":"Snowflake’s technical leaders on what, why and how they build features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/engineering-blog/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Engineering Blog"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Developers icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item/icon.coreimg.svg/1757101368571/nav-icon--developer-center.svg","height":"32","width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-a0374d7d63","linkDescription":"Tips, tricks and discussion with fellow Snowflake developers","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://community.snowflake.com/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Community"},"icon":{"id":"icon","lazyEnabled":true,"alt":"Partner Network icon","src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/mega-nav-header/master/_jcr_content/root/mega_header/nav_mega/item_1719963657751/nav_column_container/nav_column_copy_copy_1101894776/nav_item_copy_1855651246/icon.coreimg.svg/1731362644348/nav-icon--partner-network.svg","height":"64","width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column_copy_copy","nav_column_copy_copy_1367930678","nav_column_copy_copy_1101894776"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},"nav_promo_section":{"id":"nav-promo-section-429055d7fd","experience_fragment_1":{"id":"experiencefragment-31a0f0f71b","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-5/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-e728688c89","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"nav_promo_card":{"id":"nav-promo-card-8e7ebb4518","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","lazyEnabled":true,"alt":"alt","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--dc7e334a-c38b-4283-b1de-fcf829952eef/nav-promo-first-notebook.jpg?preferwebp=true&quality=85","height":"210","width":"415",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"experience_fragment_2":{"id":"experiencefragment-2a0944d093","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/nav-promo-card-4/jcr:content","configured":true,":items":{"root":{"columnClassNames":{"nav_promo_card":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-8d483179cd","layout":"RESPONSIVE_GRID","columnCount":12,":items":{"nav_promo_card":{"id":"nav-promo-card-fdb9065925","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","lazyEnabled":true,"alt":"Snowflake Northstar logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--14341ced-bc5e-4a29-9762-b7857f6cadfc/nav-promo-northstar.jpg?preferwebp=true&quality=85","height":"700","width":"1440",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-promo-card"}},":itemsOrder":["nav_promo_card"],":type":"snowflake-site/components/container"},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/nav-promo-card/master",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},":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-16b443a370","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-b70f101860","languageNavItems":[{"title":"English","path":"/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/","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-98659dca9e","heapButtonClasses":["mega-nav__sign-in"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://app.snowflake.com/"},"linkTargetContentType":"GENERIC","appliedCssClassNames":"snowflake-button-link snowflake-button-black snowflake-button-compact","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Sign in"},"button":{"id":"button-c08373ecb7","heapButtonClasses":["contact_nav","heap-nav-contact"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"/en/contact-sales/"},"linkTargetContentType":"GENERIC","appliedCssClassNames":"snowflake-button-secondary snowflake-button-blue snowflake-button-compact","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"CONTACT SALES"},"button_288358396":{"id":"button-928d10ebb6","heapButtonClasses":["start_for_free_nav","heap-nav-start-for-free"],"showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://signup.snowflake.com/"},"linkTargetContentType":"GENERIC","appliedCssClassNames":"snowflake-button-primary snowflake-button-blue snowflake-button-compact","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"start for free"}},":itemsOrder":["nav_mega","languagenavigation","button_1177328691","button","button_288358396"],":type":"snowflake-site/components/mega-header"}},":itemsOrder":["markup_editor","mega_header"],":type":"snowflake-site/components/container"},"image":{":type":"nt:unstructured"},"cq:targetMetadata":{"cq:targetStatus":"OUT_OF_SYNC","cq:exportTime":1781280015540,"cq:targetOfferId":860250,":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:targetMetadata"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"markup_editor_1950346551":{"id":"markup-editor-6508cb56ca","title":" ","cssContent":".snowflake-markdown-table code[class*=language-],.snowflake-markdown-table code[class*=language-],.snowflake-markdown .snowflake-text code[class*=language-],.snowflake-markdown .snowflake-text pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-344b1f9a23","quickstartHeroTitle":{"lines":["Using Snowflake Cortex And Streamlit With Geospatial Data"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Becky O’Connor","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Unstructured Data Analysis","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/unstructured-data-analysis","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-fd5033f24d","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/using-snowflake-cortex-and-streamlit-with-geospatial-data"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Using Snowflake Cortex And Streamlit With Geospatial Data","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/using-snowflake-cortex-and-streamlit-with-geospatial-data","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""}},"flexible_column_cont":{"id":"flexible-column-container-992b330704","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":{"id":"container-1637ea9da4","layout":"SIMPLE",":items":{"contentfragment":{"id":"contentfragment-8396b2eaad","description":"","title":"Using Snowflake Cortex And Streamlit With Geospatial Data","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EAnalyzing Location specific data in order to make decisions often requires 'niche' techniques which are often worked on by 'geography' experts.   In snowflake, everyone can be an expert in any location in which they need to understand - whether it's points of interest, transport links or government boundaries - all is feasible using standard functionality available within Snowflake.\u003C/p\u003E\n","\u003Cp\u003EAlready, there are so many location specific datasets available within the \u003Cstrong\u003ESnowflake Marketplace\u003C/strong\u003E which significantly reduces data ingestion and engineering time.  Getting access to these datasets are as simple as \u003Cstrong\u003EGet Data\u003C/strong\u003E, where you will enjoy leveraging location specific features using either SQL Queries or Snowpark Dataframes.  And these result sets are then rendered easily using \u003Cstrong\u003EStreamlit in Snowflake\u003C/strong\u003E dashboards.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESnowflake Cortex LLMs\u003C/strong\u003E - are used to save the analyst, engineer or even BI developer time - which can help a multitude of tasks - from improving the readability of location tooltips, to the generation or synthetic data for testing purposes.\u003C/p\u003E\n","\u003Cp\u003EIn this quickstart, we will be leveraging the the tools within Snowflake to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualise\u003C/strong\u003E the location of train stations within the north of england and understand where nearby restaurants are located\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDiscover\u003C/strong\u003E where the locations of Large events are and where they may impact stations and Restaurants\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnderstand\u003C/strong\u003E the weather conditions which might impact train stations\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EGenerate\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EA warning letter to the MP after discovering potential risk\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESynthezised events events which might happen and will impact services\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualise\u003C/strong\u003E  the data using Streamlit\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ELab Flow\u003C/h3\u003E\n","\u003Cp\u003EHere is a flow of what you will achieve if you complete all steps in the quickstart.  You will be reminded of where you are at key intervals.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow1.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA new free trial of Snowflake in \u003Cstrong\u003Ea region of you choice\u003C/strong\u003E*.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I001.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E:  All datasets for this dataset used are either frequently replicated or originate in AWS London.  If you choose an alternative location, you may need to wait 10 minutes for the replication process to finish.\u003C/p\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn understanding of Geospatial data in Snowflake\u003C/li\u003E\u003Cli\u003EUsing Cortex functions with Snowpark\u003C/li\u003E\u003Cli\u003ECreating a location centric application using Streamlit\u003C/li\u003E\u003Cli\u003EAn insight to UK centric Datasets such as\n\u003Cul\u003E\u003Cli\u003EPlaces of Interest\u003C/li\u003E\u003Cli\u003EWeather\u003C/li\u003E\u003Cli\u003ETrain Stations\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EUsing Notebooks and Streamlit to make discoveries and present findings\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA streamlit apps and a notebook to visualise your results\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EInitial Setup\u003C/h2\u003E\n","\u003Cp\u003EOpen up a new SQL worksheet and run the following commands. To open up a new SQL worksheet, select Projects » Worksheets, then click the blue plus button and select SQL worksheet.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE DATABASE BUILD_UK;\n\nCREATE OR REPLACE SCHEMA STREAMLITS;\n\nCREATE OR REPLACE SCHEMA NOTEBOOKS;\n\nCREATE OR REPLACE SCHEMA DATA;\n\nCREATE OR REPLACE WAREHOUSE BUILD_UK_WAREHOUSE;\n\nGRANT USAGE, OPERATE ON WAREHOUSE BUILD_UK_WAREHOUSE TO ROLE ACCOUNTADMIN;\n\nGRANT USAGE ON DATABASE BUILD_UK TO ROLE ACCOUNTADMIN;\n\n-------- use this paramater to try the LLMs using a region which does not currently support them.\n\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\n\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESnowflake Marketplace\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow0.png\" alt=\"alt text\"\u003E\nOnce logged in go to the Snowflake Marketplace - this is under Data Products > Snowflake Marketplace\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I002.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESearch for Northern Trains Station Data\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I003.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPress \u003Cstrong\u003EGet\u003C/strong\u003E to get the data from the marketplace.  Do not change the database name.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I003a.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESearch for Overture Maps - Places\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I004.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on the following dataset then press \u003Cstrong\u003EGet\u003C/strong\u003E Do not change the database name.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I004a.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESearch for the \u003Cstrong\u003EMet office Weather Data\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe exact name is \u003Cstrong\u003EPostcode Sector Weather Forecasts\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EPress \u003Cstrong\u003ETry for Free\u003C/strong\u003E - Keep the name as it is.  You will have 14 days of free weather data to try for 14 days.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/metoffice.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will have access to the dataset for a period of \u003Cstrong\u003E14 days\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch2\u003ECreate your First map Layer\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow2.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn Snowsight, go back to the home page and select \u003Cstrong\u003EProjects\u003C/strong\u003E » \u003Cstrong\u003ENotebooks\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I006.png\" alt=\"alt text\"\u003E\nObserve the alternative actions - you can also import a notebook or create one from github repository  (if you have github connected to snowflake).\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I007.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EToday we will be creating a new notebook from scratch.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I008.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn \u003Cstrong\u003ENotebook location\u003C/strong\u003E, select BUILD_UK from the list of available databases and NOTEBOOKS from the available schemas. All data within the notebook will be held inside the chosen database and schema and will sit inside an automatically generated stage. Select the BUILD_UK_WAREHOUSE and press \u003Cstrong\u003ECreate\u003C/strong\u003E.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you wish, you can import the previously created notebook from the following location:\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/Trains%20and%20Restaurants.ipynb\"\u003Enotebook in Github\u003C/a\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EHowever, to experience the creation of the notebook yourself, carry on with the blank notebook, and copy/paste the code as we go along.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003ECreating your first Map\u003C/h4\u003E\n","\u003Cp\u003EOnce the notebook has loaded, Remove all cells in the notebook.  You should see something like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I009.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBefore we start working in our notebook, we will add an additional package which is not installed as default.  This package is available within the Anaconda Snowflake channel and is easily accessible.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdd the Pydeck package for visualising location data\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the top menu, under Packages » Anaconda Packages, import pydeck.  You need this to render maps.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I010.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we have all the packages we need.  Lets start the notebook by pressing \u003Cstrong\u003EStart\u003C/strong\u003E which is at the top right hand corner of the screen.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I009a.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will be using a variety of Snowflake functions to do some transformation tasks.  Some of the functions are built into the snowpark library (such as array_agg, parse_json), others we need to call using the \u003Cstrong\u003Ecall_function\u003C/strong\u003E module.  \u003Cstrong\u003Ecall_function\u003C/strong\u003E allows the user to leverage ANY Snowflake scalar function - you can use this for both built in as well as user defined functions.\u003C/p\u003E\n","\u003Cp\u003EAll functions are held inside the \u003Cstrong\u003Esnowflake.snowpark.functions\u003C/strong\u003E module.\u003C/p\u003E\n","\u003Cp\u003EWe will import the call_function, streamlit library, json, numpy, pandas and pydeck packages.  For pydeck, you will need to add the package as this is not installed by default.\u003C/p\u003E\n","\u003Cp\u003EClick on the + Python button to add a new Python cell.  On the top left hand corner of the screen, you can rename your cell from 'cell1' to something more meaningful.  This is useful for debugging purposes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ERename\u003C/strong\u003E cell1 to libraries by typing over the text 'cell1'.\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the following code into the newly created cell.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Import python packages\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import *\nfrom snowflake.snowpark.types import *\nimport json\nimport pandas as pd\nimport numpy as np\nimport pydeck as pdk\n\n# Write directly to the app\nst.title("UK Analytics within the North of England :train:")\nst.write(\n    """This app shows key insight of places and events that may effect Northern Trains.\n    """\n)\n\n# Get the current credentials\nsession = get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I011.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EAdd a new python cell by hovering over the bottom edge of the cell and select Python.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I012.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERename the the cell to \u003Cstrong\u003En_trains_data\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will firstly leverage the Northern Trains dataset to filter the carto overture maps places dataset.  We want to do this in order to get Points of interests that are relevant for the Northern Trains locality.  Joining by each Station Code would be resource hungry - plus we do not want to join by exact locations, only by roughly where all of the train stations are situated.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a Boundary Box to filter the data based on Northern Trains\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will be seeing where Northern Train stations are and will create a boundary box to filter all our data so it will be only within the boundaries of the train stations.  Firstly, lets find out where the stations are so we can effectively draw a boundary box around it.\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the code into the new python cell and run this notebook cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING."StationLatLong"')\n\nst.markdown('#### A dataframe which shows all the train stations')\nst.dataframe(trains_latlon)\nst.map(trains_latlon, latitude='Latitude', longitude='Longitude')\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EBelow is an example of what you should see\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I013.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe have created a dataframe which is simply selecting the shared station table.  We have also leveraged Streamlit to create a title,  leveraged st.dataframe to display the snowpark dataframe in a clear way, and finally created a simple map using the streamlit \u003Ca href=\"https://docs.streamlit.io/develop/api-reference/charts/st.map\"\u003Est.map\u003C/a\u003E function.\u003C/p\u003E\n","\u003Cp\u003Est.map is useful for quickly generating simple maps by rendering latitude and longitude as points.  Pydeck however, has more sophisticated capabilities such as rendering lines, points, polygons and H3 indexes.  We will be leveraging the Pydeck library in the next step\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECreate a Boundary for filtering purposes\u003C/h2\u003E\n","\u003Cp\u003EYou previously loaded the places dataset from Carto Overture maps.  This dataset offers a comprehensive list of places of interest across the world such as restaurants, bars and schools.  We want to filter this dataset to only list places of interest that occur within the Northern Trains locality.  Creating a Boundary box is the easiest option.\u003C/p\u003E\n","\u003Cp\u003ECopy and Paste the following into a new cell and name the new cell \u003Cstrong\u003Eboundary_box\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#create a point from the coordinates\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('"Longitude"'),col('"Latitude"')))\n\n#collect all the points into one row of data\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\n\n#### convert from geography to geometry\nenvelope = envelope.select(to_geometry('POINTS').alias('POINTS'))\n\n\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\n\n#convert back to geography\nenvelope = envelope.select(to_geography('BOUNDARY').alias('BOUNDARY'))\nenvelope.collect()[0][0]\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see this has generated 5 sets of coordinates in order to draw the boundary box.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EFACT:\u003C/strong\u003E Every \u003Cstrong\u003Evalid\u003C/strong\u003E polygon will have the same last pair of coordinates as the first pair.   Lets visualise what this looks like using the library pydeck.  Although st.map is useful for simple quick visualisation of points, pydeck has the ability to visualise lines, points and polygons in 2D and 3D.  It also has layer options for lines, points, icons and H3 indexes.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003Ehttps://deckgl.readthedocs.io/en/latest/\u003C/p\u003E\n","\u003Cp\u003EAdd a new Python cell and name it \u003Cstrong\u003Eboundary_layer\u003C/strong\u003E.  Copy and paste the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E#find the centre point so the map will render from that location\n\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\n\n#create LON and LAT variables\n\ncentrepd = centre.select('LON','LAT').to_pandas()\nLON = centrepd.LON.iloc[0]\nLAT = centrepd.LAT.iloc[0]\n\n### transform the data in pandas so the pydeck visualisation tool can view it as a polygon\n\nenvelopepd = envelope.to_pandas()\nenvelopepd["coordinates"] = envelopepd["BOUNDARY"].apply(lambda row: json.loads(row)["coordinates"][0])\n\n\n####visualise on a map\n\n#### create a layer - this layer will visualise the rectangle\n\npolygon_layer = pdk.Layer(\n            "PolygonLayer",\n            envelopepd,\n            opacity=0.3,\n            get_polygon="coordinates",\n            filled=True,\n            get_fill_color=[16, 14, 40],\n            auto_highlight=True,\n            pickable=False,\n        )\n\n \n#### render the map \n    \nst.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=400\n        ),\n    \nlayers= [polygon_layer]\n\n))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see that to render the map, we present the data in a format for pydeck to accurately read.  The final transformed dataset is a pandas dDataframe.  We specify the dataframe in a pydeck layer, then apply this layer to a streamlit pydeck chart.  If we want, we can use the same logic in order to create a streamlit app.  Snowflake Notebooks are great as you can render streamlit on the fly - without having to run the ‘app’ separately.\u003C/p\u003E\n","\u003Cp\u003EWhen you run the cell, the boundary box should look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I014.png\" alt=\"boundarybox\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EFiltering the data using the boundary box\u003C/h2\u003E\n","\u003Cp\u003ENext, lets leverage and filter the overture maps so these will only consist of data within this area.  Overture maps consist of location data across the entire globe.\u003C/p\u003E\n","\u003Cp\u003ECreate and preview the places dataframe - we are also filtering to only display places in the UK.\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the python code into a new python cell.  Name the cell \u003Cstrong\u003Eplaces_dataset\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_1 = session.table('OVERTURE_MAPS__PLACES.CARTO.PLACE')\nplaces_1 = places_1.filter(col('ADDRESSES')['list'][0]['element']['country'] =='GB')\n\nplaces_1.limit(3)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen you run the cell, you will see there is a lot of semi structured data returned.  We will use snowflake’s native \u003Cstrong\u003Esemi structured\u003C/strong\u003E querying capability to take key elements out of the data which includes information concerning the location\u003C/p\u003E\n","\u003Cp\u003EIn a new cell which you will name \u003Cstrong\u003Eplaces_refined\u003C/strong\u003E, paste the following and run the cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplaces_2 = places_1.select(col('NAMES')['primary'].astype(StringType()).alias('NAME'),\n                        col('PHONES')['list'][0]['element'].astype(StringType()).alias('PHONE'),\n                      col('CATEGORIES')['primary'].astype(StringType()).alias('CATEGORY'),\n                        col('CATEGORIES')['alternate']['list'][0]['element'].astype(StringType()).alias('ALTERNATE'),\n                    col('websites')['list'][0]['element'].astype(StringType()).alias('WEBSITE'),\n                      col('GEOMETRY'))\n                        \n\nplaces_2.limit(10)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is what you should see.  The exact dataset set may ot be the same as the screenshot but will contain the same type of data.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I015.png\" alt=\"semi_structured_filtered\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will now filter the data to only view places which are categorised as \u003Cstrong\u003Etrain_station\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the code below into a new \u003Cstrong\u003Epython\u003C/strong\u003E cell.  Name the cell \u003Cstrong\u003Eplaces_filtered_boundary\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eplaces_3 = places_2.filter(col('CATEGORY') =='train_station')\n\nplaces_3 = places_3.join(envelope,call_function('ST_WITHIN',places_3['GEOMETRY'],envelope['boundary']))\nplaces_3 = places_3.with_column('LON',call_function('ST_X',col('GEOMETRY')))\nplaces_3 = places_3.with_column('LAT',call_function('ST_Y',col('GEOMETRY')))\nst.write(places_3)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe can view the points on a map easily by using st.map(places) but as pydeck has many more options such as different mark types, tool tips and layers we will create an additional pydeck layer which adds this data to the previously created data layer.  When you hover over in the boundary box you will see a tooltip containing the  alternate category as well as the place name.\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the following into a new cell and name the cell \u003Cstrong\u003Eplaces_visualised\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nplacespd = places_3.to_pandas()\npoi_l = pdk.Layer(\n            'ScatterplotLayer',\n            data=placespd,\n            get_position='[LON, LAT]',\n            get_color='[255,255,255]',\n            get_radius=600,\n            pickable=True)\n\n#### render the map showing trainstations based on overture maps\n    \nst.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=400\n        ),\n    \nlayers= [polygon_layer, poi_l], tooltip = {'text':"Place Name: {NAME}, alternate: {ALTERNATE}"}\n\n))\n\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGo back to \u003Cstrong\u003Eplaces_filter_boundary\u003C/strong\u003E cell and modify the category in the filter from train_station to restaurant.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect \u003Cstrong\u003ERun all below\u003C/strong\u003E.  These steps will start from retreiving a fresh table from the marketplace table:\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I016.jpg\" alt=\"boundarybox\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe results should look like this.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I017.png\" alt=\"boundarybox\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow we have a map with all the restaurants within the Northern trains boundary.  Lets now compare this with another layer which shows the train stations provided by Northern Trains.  We have already loaded the train station locations into the notebook when we created the boundary box.\u003C/p\u003E\n","\u003Cp\u003ECreate a new cell and name it \u003Cstrong\u003Etrains_visualised\u003C/strong\u003E.  Copy and paste the following:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntrains_latlon_renamed = trains_latlon\n\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"CrsCode"','NAME')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Postcode"','ALTERNATE')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Latitude"','LAT')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Longitude"','LON')\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\n\nnw_trains_l = pdk.Layer(\n            'ScatterplotLayer',\n            data=trains_latlon_renamed_pd,\n            get_position='[LON, LAT]',\n            get_color='[0,187,255]',\n            get_radius=600,\n            pickable=True)\n\n#### render the map showing trainstations based on overture maps\n    \nst.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=400\n        ),\n    \nlayers= [polygon_layer, poi_l, nw_trains_l], tooltip = {'text':"Place Name: {NAME}, alternate: {ALTERNATE}"}\n\n))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe have now rendered a multi layer map which overlays restaurants and northern rail train stations.  Next, we will leverage Cortex to curate descriptive tooltips derived by station attributes.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Cortex to describe the Train Stations\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow3.png\" alt=\"alt text\"\u003E\nAt the moment we only have very basic train station information.  Lets add more info from the shared dataset:\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the following code into a new cell.  Name the cell \u003Cstrong\u003Estation_attributes\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nfurther_train_info_1 = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING."STATION ATTRIBUTES 2"')\nfurther_train_info_1.limit(4)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I018.png\" alt=\"boundarybox\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe have quite a bit of information, it would be great if we can  use Snowflake Cortex LLM to explain this data and then we could add the results to our tool tip!! Right now we only have the postcode in the tooltip.\u003C/p\u003E\n","\u003Cp\u003ELet's do that by creating a new cell.  Name the cell \u003Cstrong\u003Ecortex_tooltip\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EPaste the following into the new cell and run. This takes around 1.5 minutes to complete as it will write a summary report for all 400 train stations.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nfurther_train_info_2= further_train_info_1.with_column('OBJECT',object_construct(lit('CRS Code'),\ncol('"CRS Code"'),\nlit('Full Timetable Calls'),\ncol('"Dec21 Weekday Full Timetable Daily Calls"').astype(IntegerType()),\nlit('Emergency Timetable Calls'),\ncol('"Dec21 Weekday Emergency Timetable Daily Calls"').astype(IntegerType()),\nlit('Footfall'),\ncol( '"ORR Station Footfall 2020-21"').astype(IntegerType()),\nlit('Parking'),\ncol('"Car Parking - Free/Chargeable"'),\nlit('MP'),\ncol("MP"),\nlit("Political Party"),\ncol('"Political Party"'),\nlit('MP Email Address'),\ncol('"MP Email Address"'),                                                                             \nlit('Car Parking Spaces'),\ncol('"Car Parking Spaces"').astype(IntegerType()),\nlit('Staffed?'),\ncol('"Staffed?"'))).cache_result()\n\nprompt = 'In less than 200 words, write a summary based on the following train station details.  \nThe trainstations are based in the North of England. \nOnly include Northern train station names in the description.'\nprompt2 = 'write in the best way for it to describe a point on a map.'\n\nfurther_train_info_2 = further_train_info_2.select('"CRS Code"',\n        'MP',\n        '"Political Party"', \n        '"MP Email Address"',\n        call_function('snowflake.cortex.complete','mistral-large2',\n            concat(lit(prompt),\n            col('OBJECT').astype(StringType()),\n            lit('prompt2'))).alias('ALTERNATE'))\n\nfurther_train_info_2.write.mode('overwrite').save_as_table("DATA.TRAIN_STATION_INFORMATION")\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\nstation_info.limit(5)\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhile we wait for the train station tooltips to materialize add a \u003Cstrong\u003Emarkdown cell\u003C/strong\u003E above the cell we have just created. Markdown is useful to help tell a data story within the notebook. Hover above the newly created cell which is currently running and press  \u003Cstrong\u003EMarkdown\u003C/strong\u003E.  Name the cell \u003Cstrong\u003Ecortex_description\u003C/strong\u003E and paste the following narrative:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-markdown\"\u003E\n\nBelow we are leveraging Mistral-large2 to produce meaningful tooltips relating to over **400** train stations which are managed by **Northern Trains**\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPress the tick on the top right hand side of the cell to confirm the edit.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I026.jpg\" alt=\"train_details\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe used \u003Cstrong\u003Ecall_function\u003C/strong\u003E to call Snowflake Cortex complete which returns a response that completes an input prompt. Snowflake Cortex runs LLMs that are fully hosted and managed by Snowflake, requiring no setup. In this example, we are using  Mistral-Large2, an open enterprise-grade LLM model fully managed by Snowflake.\u003C/p\u003E\n","\u003Cp\u003EYou should get a new table that will look like this:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/IO192.jpg\" alt=\"train_details\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIMPORTANT\u003C/strong\u003E Comment out the write to table command to prevent the LLM being called every time you refresh the notebook.  We now have the data saved in a table so you do not need to run this model again.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n# further_train_info_2.write.mode('overwrite').\n# save_as_table("DATA.TRAIN_STATION_INFORMATION")\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOk, let's now revise the map.  Create a new python cell and name it \u003Cstrong\u003Evisualise_tooltip\u003C/strong\u003E Add the following code:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etrains_latlon_renamed = trains_latlon\n\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"CrsCode"','NAME')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Latitude"','LAT')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Longitude"','LON')\n\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\n\ntrains_latlon_renamed = trains_latlon_renamed.join(station_info,station_info['"CRS Code"']==trains_latlon_renamed['NAME']).drop('"CRS Code"')\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\n\nnw_trains_l = pdk.Layer(\n            'ScatterplotLayer',\n            data=trains_latlon_renamed_pd,\n            get_position='[LON, LAT]',\n            get_color='[0,187,2]',\n            get_radius=600,\n            pickable=True)\n\n#### render the map showing trainstations based on overture maps\n\ntooltip = {\n   "html": """<b>Name:</b> {NAME} <br> <b>Alternate:</b> {ALTERNATE}""",\n   "style": {\n       "width":"50%",\n        "backgroundColor": "steelblue",\n        "color": "white",\n       "text-wrap": "balance"\n   }\n}\n    \nst.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=700\n        ),\n    \nlayers= [polygon_layer, poi_l, nw_trains_l], tooltip = tooltip\n\n))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHover over the map and checked the updated tool tips.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I0202.png\" alt=\"tool_tip_image\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Cortex to list Key location events\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow4.png\" alt=\"alt text\"\u003E\nAny location may be impacted by key events.  Let's try and pinpoint out any key event happening in the north of England and how restaurants and train stations may be impacted by this.  We do not have specific event data for this, so in this case, we will leverage Snowflake Cortex to suggest events that may impact this area.\u003C/p\u003E\n","\u003Cp\u003ECreate the following in a new cell named \u003Cstrong\u003Ecortex_events\u003C/strong\u003E.  This will generate event data and saves the results in a new table.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ejson1 = '''{"DATE":"YYYY-MM-DD", "NAME":"event",DESCRIPTION:"describe what the event is" "CENTROID":{\n  "coordinates": [\n    0.000000<<<this needs to be longitude,\n    0.000000<<<<this needs to be latitude\n  ],\n  "type": "Point"\n},"COLOR":"Random bright and unique color in RGB presented in an array"}'''\n\n\nprompt = f''' Retrieve 6 events within different cities of the north of england and will happen in 2024.  do not include commentary or notes retrive this in the following json format {json1}  '''\nevents_1 = session.create_dataframe([{'prompt':prompt}])\n\nevents_1 = events_1.select(call_function('SNOWFLAKE.CORTEX.COMPLETE','mistral-large2',prompt).alias('EVENT_DATA'))\n\nevents_1 = events_1.with_column('EVENT_DATA',replace(col('EVENT_DATA'),'''```json''',''))\nevents_1 = events_1.with_column('EVENT_DATA',replace(col('EVENT_DATA'),'''```''',''))\n\nevents_1.write.mode('overwrite').save_as_table("DATA.EVENTS_IN_THE_NORTH")\nsession.table('DATA.EVENTS_IN_THE_NORTH')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAgain, we will utilise the semi-structured support in Snowflake to flatten the retrieved json to transpose a data frame in a table format\u003C/p\u003E\n","\u003Cp\u003ECopy and paste the code below into a new cell which you will rename as \u003Cstrong\u003Eevents_normalised\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nevents_2 = session.table('DATA.EVENTS_IN_THE_NORTH')\nevents_2 = events_2.join_table_function('flatten',parse_json('EVENT_DATA')).select('VALUE')\nevents_2=events_2.with_column('NAME',col('VALUE')['NAME'].astype(StringType()))\nevents_2=events_2.with_column('DESCRIPTION',col('VALUE')['DESCRIPTION'].astype(StringType()))\nevents_2=events_2.with_column('CENTROID',to_geography(col('VALUE')['CENTROID']))\nevents_2=events_2.with_column('COLOR',col('VALUE')['COLOR'])\nevents_2=events_2.with_column('DATE',col('VALUE')['DATE'].astype(DateType())).drop('VALUE')\nevents_2\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ELeveraging H3\u003C/h3\u003E\n","\u003Cp\u003EWe now have a list of events in a new table.  We would like to utilise this to understand the restaurants and train stations which may be impacted. H3 indexes are a way to bucket multiple points into a standardised grid.  H3 buckets points into hexagons.   Every hexagon at each resolution has a unique index. This index can also be used to join with other datasets which have also been indexed to the same standardised grid. We will do this later in the lab.\u003C/p\u003E\n","\u003Cp\u003ECreate a new cell which you will name \u003Cstrong\u003Eh3index\u003C/strong\u003E.  Add the H3 Index at resolution 5 to the dataframe\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nevents_3=events_2.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('CENTROID'),lit(5)))\n\nevents_3\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will see a new column called H3:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I021.png\" alt=\"events_H3\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe will now add these events onto the map as another layer.  Add a new cell and name it \u003Cstrong\u003Evisualise_h3\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nevents = events_3.with_column('R',col('COLOR')[0])\nevents = events.with_column('G',col('COLOR')[1])\nevents = events.with_column('B',col('COLOR')[2])\nevents = events.with_column_renamed('DESCRIPTION','ALTERNATE')\neventspd = events.group_by('H3','NAME','ALTERNATE','R','G','B').count().to_pandas()\n\nst.write(eventspd)\n\nh3_events = pdk.Layer(\n        "H3HexagonLayer",\n        eventspd,\n        pickable=True,\n        stroked=True,\n        filled=True,\n        extruded=False,\n        get_hexagon="H3",\n        get_fill_color=["255-R","255-G","255-B"],\n        line_width_min_pixels=2,\n        opacity=0.4)\n\n#### render the map showing trainstations based on overture maps\n\ntooltip = {\n   "html": """<b>Name:</b> {NAME} <br> <b>Alternate:</b> {ALTERNATE}""",\n   "style": {\n       "width":"50%",\n        "backgroundColor": "steelblue",\n        "color": "white",\n       "text-wrap": "balance"\n   }\n}\n\nst.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=600\n        ),\n    \nlayers= [polygon_layer, poi_l, h3_events,nw_trains_l, ], tooltip = tooltip\n\n))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I022.png\" alt=\"events_map\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESo we can see the train stations and restaurants that might be impacted by the events.  The events rendered may be different to what is shown in the screenshot.  Lets create a dataset that extracts only the impacted areas.\u003C/p\u003E\n","\u003Cp\u003EJoin the Events data frame to The Train Stations Data frame.  Then, Join the Events Data frame to the Restaurants Data frame.\u003C/p\u003E\n","\u003Cp\u003EYou may notice that there are  new  H3 columns for the restaurants and places of the same resolution as the events.  This naturally creates a key to join to.  There is also a new column which displays the distance away the restaurant is from the event.   This was created using a standard geospatial function.  Copy and paste the following into a new cell called \u003Cstrong\u003Eaffected_by_events\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ntrains_h3 = trains_latlon_renamed.with_column('H3',call_function('H3_LATLNG_TO_CELL_STRING',col('LAT'),col('LON'),lit(5)))\ntrains_h3 = trains_h3.join(events.select('H3',col('NAME').alias('EVENT_NAME'),'DATE'),'H3')\n\nst.markdown('#### Affected Train Stations')\nst.write(trains_h3.limit(1))\nplaces_h3 = places_3.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('GEOMETRY'),lit(5)))\nplaces_h3 = places_h3.join(events.select('H3','CENTROID',col('NAME').alias('EVENT_NAME'),'DATE'),'H3')\nplaces_h3 = places_h3.with_column('DISTANCE_FROM_EVENT',call_function('ST_DISTANCE',col('CENTROID'),col('GEOMETRY')))\nplaces_h3 = places_h3.filter(col('DISTANCE_FROM_EVENT')< 3000)\nplaces_h3 = places_h3.sort(col('DISTANCE_FROM_EVENT').asc())\nst.markdown('#### Affected Restaurants')                             \nst.write(places_h3.limit(10))\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I023.png\" alt=\"events_map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe now have all of this joined together - in the next step we will use an LLM to write a letter to each MP which details the concerns which may impact the events.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Cortex to write relevant correspondence\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow6.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we can see where the events impact stations and restaurants, let's  use an LLM to craft a letter to the MP to notify them of these effects.  To do this, we need to put all the information needed into objects to  easily pass them through the cortex function.\u003C/p\u003E\n","\u003Cp\u003ECreate an object which links all affected restaurants to the respective MP.  We are also including the distance from the event for each restaurant.\u003C/p\u003E\n","\u003Cp\u003ECreate a new cell celled \u003Cstrong\u003Eletter_restaurants\u003C/strong\u003E and copy and paste the following code into the cell:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nobject3 = trains_h3.select('H3','MP','"MP Email Address"').distinct()\nobject3 = places_h3.join(object3,'H3')  \nobject3 = object3.group_by('MP','"MP Email Address"').agg(array_agg(object_construct(lit('NAME'),\n                                                                col('NAME'),\n                                                                lit('DISTANCE_FROM_EVENT'),\n                                                                round('DISTANCE_FROM_EVENT',5).astype(DecimalType(20,4)),\n                                                                lit('PHONE'),\n                                                                col('PHONE'),\n                                                               lit('WEBSITE'),\n                                                               col('WEBSITE'))).within_group('MP').alias('RESTAURANTS'))\nobject3\n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou will now create another object which links all the affected trains stations to the respective MP.  Copy and paste the following code into a new python cell called \u003Cstrong\u003Eletter_trains\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nobject1 = trains_h3.group_by('MP').agg(array_agg(object_construct(lit('Train Station information'),col('ALTERNATE'))).within_group('MP').alias('TRAIN_STATIONS'))\nobject1\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate an object which links all affected events to the respective MP.  Create a new python cell called \u003Cstrong\u003Eletter_events\u003C/strong\u003E and paste the following code into it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nobject2 = trains_h3.select('MP','EVENT_NAME','DATE').distinct()\nobject2 = object2.group_by('MP').agg(array_agg(object_construct(lit('EVENT'),col('EVENT_NAME'),lit('DATE'),col('DATE'))).within_group('MP').alias('EVENTS'))\nobject2\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eyou will now join all these objects together and persist the results in a table.  Create a new cell called \u003Cstrong\u003Eletter_object\u003C/strong\u003E and copy and paste the python below into it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nall_3 = object1.join(object2,'MP')\nall_3 = all_3.join(object3,'MP')\n\nall_3.write.mode('overwrite').save_as_table("DATA.EVENTS_AND_WHAT_IS_AFFECTED")\nsession.table('DATA.EVENTS_AND_WHAT_IS_AFFECTED')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I024.png\" alt=\"events_map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe results can include a large number of restaurants by MP, so let's only  refer to the first 8 restaurants for each MP letter based on distance from the event.  The array_slice method does just that.  Create a new Python cell called \u003Cstrong\u003Efilt_restaurant_obj\u003C/strong\u003E and paste the following python content into it:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eall_4 = session.table("DATA.EVENTS_AND_WHAT_IS_AFFECTED")\nall_4 = all_4.select('MP','"MP Email Address"','TRAIN_STATIONS','EVENTS',\n                     \narray_slice(col('RESTAURANTS'),lit(0),lit(8)).alias('RESTAURANTS'))\n\n          \nall_4\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECreate a prompt for the LLM which pulls all this information together.  You may want to change who the letter is written to.  The prompt will encourage the letter to be written by Becky.\u003C/p\u003E\n","\u003Cp\u003ECopy the code below into a new python cell called \u003Cstrong\u003Eprompt_letter\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\ncol1,col2,col3, col4 = st.columns(4)\n\nwith col1:\n    name = st.text_input('Name:','''Becky O'Connor''')\nwith col2:\n    email = st.text_input('Email:','becky.oconnor@snowflake.com')\nwith col3:\n    title = st.text_input('Title:','a concerned Citizen')\nwith col4:\n    style = st.text_input('Style:','a worried resident')\n\n\nprompt = concat(lit('Write an email addressed to this MP:'),\n                lit('in the style of '),\n                lit(style),\n                col('MP'),\n               lit('about these events: '),\n               col('EVENTS').astype(StringType()),\n               lit('effecting these stations: '),\n               col('TRAIN_STATIONS').astype(StringType()),\n                lit('And these Restaurants: '),\n                col('RESTAURANTS').astype(StringType()),\n               lit(f'''The letter is written by {name} - {email} - {title}'''))\n\nst.info(f'''Letters will be generated from {name} - {email} - {title} in the style of {style}''')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOnce you run the cell, change the prompts to reflect a sender of your choice.\u003C/p\u003E\n","\u003Cp\u003ECall the LLM with the prompt by copying the code below into a new cell. You may want to be creative and change who the letter is written by, or even ask Cortex to write it in the style of someone. The LLM we are using for this is Mixtral-8x7b as its good at writing letters.  Copy and paste the following in a new python cell called \u003Cstrong\u003Ecortex_letter\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nletters = all_4.select('MP','"MP Email Address"', call_function('SNOWFLAKE.CORTEX.COMPLETE','mixtral-8x7b',prompt).alias('LETTER'))\nletters.write.mode('overwrite').save_as_table("DATA.LETTERS_TO_MP")\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe have now saved our letters.  The next task is to view the letters by copying this code into a new python cell called \u003Cstrong\u003Enew_letters\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eletters = session.table('DATA.LETTERS_TO_MP')\nletters\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe code below  allows the user to browse the letters with a slider and visualise a letter.Try it out by copying the code into a new python cell called \u003Cstrong\u003Eletter_reader\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nletterspd = letters.to_pandas()\nselected_letter = st.slider('Choose Letter:',0,letterspd.shape[0]-1,1)\nst.markdown(f''' **Email**: {letterspd['MP Email Address'].iloc[selected_letter]}''')\nst.write()\nst.write(letterspd.LETTER.iloc[selected_letter])\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I025.png\" alt=\"events_map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThat's it. If you wish, you can download the completed python code from here by exporting it as .ipynb that could be used to import it into a new notebook.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Synthetic Incidents\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow7.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis streamlit application will generate events occurring during the time of the events, which will involve the train stations and the restaurants.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st001.png\" alt=\"events_map\"\u003E\u003C/p\u003E\n","\u003Cp\u003EGo back to the home page and Navigate to Streamlit which is within the Projects Menu.\u003C/p\u003E\n","\u003Cp\u003EAdd a new Streamlit App\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st002.png\" alt=\"create_streamlit\"\u003E\u003C/p\u003E\n","\u003Cp\u003EPopulate the prompts as suggested below.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st003.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EReplace all the sample code with what is provided below:\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/sis001.py\"\u003EThe code is also found here\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n# Import python packages\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import col, call_function, lit,concat, parse_json,object_construct,replace\nfrom snowflake.snowpark.types import StringType, FloatType, ArrayType, VariantType, DateType\n\n\n\n# Write directly to the app\n\n\n# Get the current credentials\nsession = get_active_session()\n\nst.markdown(\n    """\n    <style>\n    .heading{\n        background-color: rgb(41, 181, 232);  /* light blue background */\n        color: white;  /* white text */\n        padding: 60px;  /* add padding around the content */\n    }\n    .tabheading{\n        background-color: rgb(41, 181, 232);  /* light blue background */\n        color: white;  /* white text */\n        padding: 10px;  /* add padding around the content */\n    }\n    .veh1 {\n        color: rgb(125, 68, 207);  /* purple */\n    }\n    .veh2 {\n        color: rgb(212, 91, 144);  /* pink */\n    }\n    .veh3 {\n        color: rgb(255, 159, 54);  /* orange */\n    }\n    .veh4 {\n        padding: 10px;  /* add padding around the content */\n        color: rgb(0,53,69);  /* midnight */\n    }\n    \n    body {\n        color: rgb(0,53,69);\n    }\n    \n    div[role="tablist"] > div[aria-selected="true"] {\n        background-color: rgb(41, 181, 232);\n        color: rgb(0,53,69);  /* Change the text color if needed */\n    }\n    \n    </style>\n    """,\n    unsafe_allow_html=True\n)\n\ncol1,col2 = st.columns([0.2,0.8])\n\n\nst.markdown('<h1 class="heading">EVENT SIMULATOR</h2>', unsafe_allow_html=True)\nst.write(\n    """<BR>Here are all the events and what places are potentially affected again.\n    """,unsafe_allow_html=True\n)\n\n\n####Use a higher order filter to filter each array to only show restaurants that are no more than 250m from the event and include this in the LLM\n\nevents_what_affected = session.sql('SELECT MP, TRAIN_STATIONS,EVENTS,CASE WHEN ARRAY_SIZE(RESTAURANTS) >10 THEN FILTER(RESTAURANTS,i -> i:DISTANCE_FROM_EVENT <=250) ELSE RESTAURANTS END RESTAURANTS FROM DATA.EVENTS_AND_WHAT_IS_AFFECTED')\nmps = events_what_affected.select('MP').to_pandas()\n\nwith st.expander("View Prompt Information"):\n    st.dataframe(events_what_affected,column_config={\n                                    'MP':st.column_config.ListColumn('MP',\n                                     help='The current acting MP responsible for the area',\n                                     width='medium')   \n                                        }\n              )\n\nst.markdown('<h4 class="veh1">GENERATE SYNTHETIC EVENTS</h2>', unsafe_allow_html=True)\nst.markdown(\n    """ \n    Create synthetic events based on a mixture of Activity types. \n    The options are Overcrowding, Food Poisoning, Train Incidents and Fights. <BR><BR>\n    Once complete scrolL down to indidual incidents and wait for social media data to generate\n\n    """,unsafe_allow_html=True)\n\n\n\n    \nst.image('https://cdn.prgloo.com/media/aad47116f9cc4c33b6f02e51fb8070f4.jpg?width=1200&height=400')\n\n\n\njson_template = {"DATE":"01/01/2024",\n                 "Location Details":"CRS Code of train station or Restaurant name and address",\n                 "LOCATION":{"LAT":0,"LON":0},\n                 "REPORTED_BY":"BECKY O'CONNOR","DESCRIPTION_OF_INCIDENTS":"generate unique ficticious incident details here"}\n\n#st.write(json_template)\n\nwith st.form('Generate Events'):\n    'Generate Synthetic Events based on the following:'\n    col1,col2, col3,col4 = st.columns(4)\n    with col1:\n        model = st.selectbox('Choose Model',['mistral-large2'])\n    with col2:\n        mp = st.selectbox('Choose MP: ',mps)\n    with col3:\n        activity = st.selectbox('Choose Activity: ', ['Overcrowding','Food Poisoning','Train Incident','Fight'])\n    with col4:\n        event_count = st.number_input('How many events:',1,10,5)\n    \n    submitted = st.form_submit_button('Generate Reports')\n\n\nif submitted:\n    filtered_data = events_what_affected.filter(col('MP')==lit(mp))\n\n    st.markdown('Filtered Objects')\n\n    st.dataframe(filtered_data)\n\n    filtered_data_pd = filtered_data.to_pandas()\n\n    prompt = concat(lit('create'),\n                    lit(event_count),\n                    lit('synthetic incidents using this json template'),\n                    lit(json_template).astype(StringType()), \n                    lit('involving'), \n                    lit(activity), \n                    lit('concerning one of these train stations:'), \n                    col('TRAIN_STATIONS').astype(StringType()), \n                    lit('populate the incident date as the same date as one of these events'),\n                    col('EVENTS').astype(StringType()),\n                    lit('.Each incident will have connection with the provided event, and will also involve'),\n                    lit('one of the following restaurants:'), \n                    col('RESTAURANTS').astype(StringType()),\n                    lit('.  Each Incident will be Reported By a synthetic and randomly generated full name'),\n                    lit('populate the latitude and longitude as one json element using the provided json template'),\n                   lit('Nest all the generated incidents in an single json object called incidents.  Do not include Note'), \n                    lit('RETURN ONLY THE JSON'))\n\n    mistral = call_function('snowflake.cortex.complete',(lit(model),prompt))\n\n\n    \n\n    generated = filtered_data.with_column('generated_events',mistral)\n\n    generated = generated.with_column('generated_events',replace(col('generated_events'),'''```json''',lit('')))\n    generated = generated.with_column('generated_events',replace(col('generated_events'),'''```''',''))\n    #st.write(generated)\n    generated = generated.select('MP',parse_json('GENERATED_EVENTS').alias('GENERATED_EVENTS'))\n    generated = generated.with_column('INCIDENT_TYPE',lit(activity))\n    #st.write(generated)\n\n    sql2 = '''create table if not exists DATA.INCIDENTS (MP VARCHAR(255),\n            GENERATED_EVENTS VARIANT,\n         INCIDENT_TYPE VARCHAR(255))'''\n    \n    session.sql(sql2).collect()\n    generated.write.mode('append').save_as_table("DATA.INCIDENTS")\n\n    \n\n    st.markdown('<h4 class="veh1">NEW EVENTS</h2>', unsafe_allow_html=True)\n\n    st.dataframe(generated)\n\nst.markdown('<h4 class="veh1">GENERATED EVENTS</h2>', unsafe_allow_html=True)\n\n\ntry:\n    \n    incident_table = session.table('DATA.INCIDENTS')\n    st.markdown('<h4 class="veh1">ALL GENERATED EVENTS</h2>', unsafe_allow_html=True)\n    \n    sql = 'DROP TABLE DATA.INCIDENTS'\n\n\n\n    \n    clear = st.button('clear incident table')\n    \n    if clear:\n        session.sql(sql).collect()\n    \n    #st.dataframe(incident_table)\n\n    flatten = incident_table.select('MP','INCIDENT_TYPE',parse_json('GENERATED_EVENTS').alias('JSON'))\n    #st.write(flatten)\n    flatten = flatten.join_table_function('FLATTEN',col('JSON')['incidents'])\n    flatten = flatten.select('MP','INCIDENT_TYPE','VALUE')\n    \n    flatten = flatten.with_column('DESCRIPTION_OF_INCIDENTS',\n                                  col('VALUE')['DESCRIPTION_OF_INCIDENTS'].astype(StringType()),\n                                 )\n\n    flatten = flatten.with_column('LAT',\n                                  col('VALUE')['LOCATION']['LAT'].astype(FloatType()))\n                                 \n    flatten = flatten.with_column('LON',\n                                  col('VALUE')['LOCATION']['LON'].astype(FloatType()))\n                                 \n\n    flatten = flatten.with_column('REPORTED_BY',\n                                  col('VALUE')['REPORTED_BY'].astype(StringType()),\n                                 )\n\n    flatten = flatten.with_column('DATE',\n                                  col('VALUE')['DATE'].astype(StringType()),\n                                 ).drop('VALUE')\n    \n    \n    \n    st.write(flatten)\n    \n    \n    \n\n\n    st.divider()\n\n    st.markdown('<h4 class="veh1">INDIVIDUAL INCIDENTS</h2>', unsafe_allow_html=True)\n    MP = st.selectbox('Choose MP:',flatten.select('MP').distinct())\n    flatten = flatten.filter(col('MP')==MP)\n    \n    map = flatten.select('LAT','LON')\n    st.map(map)\n    flattenpd = flatten.to_pandas()\n    count = flattenpd.shape[0]\n    record = st.slider('Choose Incident Record:',0,count-1,count-1)\n    \n    st.markdown('###### INCIDENT TYPE')\n    st.write(flattenpd.INCIDENT_TYPE.iloc[record])\n    st.markdown('###### REPORTED BY')\n    st.write(flattenpd.REPORTED_BY.iloc[record])\n    st.markdown('###### DATE OF INCIDENT')\n    st.write(flattenpd.DATE.iloc[record])\n    st.markdown('###### DESCRIPTION OF INCIDENT')\n    st.write(flattenpd.DESCRIPTION_OF_INCIDENTS.iloc[record])\n\n    st.divider()\n\n    st.markdown('<h4 class="veh1">NEWLY GENERATED SOCIAL MEDIA</h2>', unsafe_allow_html=True)\n\n    #### SOCIAL MEDIA DATA\n    \n    social_media = session.create_dataframe([0])\n    json = '''{"date","YYYY-MM-DD","post","abcdefg","sentiment_score",0.2,"username","bob"}'''\n    \n    social_media = social_media.with_column('V',call_function('SNOWFLAKE.CORTEX.COMPLETE',model, \n    concat(lit('generate 4 random synthetic social media post concerning the follwing incident:'), \n    lit(f'''{flattenpd.DESCRIPTION_OF_INCIDENTS.iloc[record]}'''), \n    lit('Add a date, username and relevant emojis to each post.\n    Include emotion.  Return the results as a json object and include sentiment scores.')\n           ,lit('use the following json template to structure the data'),lit(json))).astype(VariantType()))\n    \n\n    social_media = social_media.with_column('V',replace(col('V'),'''```json''',lit('')))\n    social_media = social_media.with_column('V',replace(col('V'),'''```''',''))\n    \n    smedia = social_media.join_table_function('flatten',parse_json('V')).select('VALUE')\n    smedia = smedia.select(object_construct(lit('INCIDENT_TYPE'),lit(flattenpd.INCIDENT_TYPE.iloc[record]),lit('MP'),lit(MP),lit('DATA'),col('VALUE')).alias('V'))\n    smedia.write.mode('append').save_as_table('DATA.SOCIAL_MEDIA')\n    smedia = smedia.with_column('"Date"',col('V')['DATA']['date'].astype(DateType()))\n    smedia = smedia.with_column('"Post"',col('V')['DATA']['post'].astype(StringType()))\n    smedia = smedia.with_column('"Sentiment"',col('V')['DATA']['sentiment_score'])\n    smedia = smedia.with_column('"Username"',col('V')['DATA']['username'].astype(StringType()))\n    smedia = smedia.with_column('"Incident Type"',col('V')['INCIDENT_TYPE'].astype(StringType()))\n    smedia = smedia.with_column('"MP"',col('V')['MP'].astype(StringType()))\n    st.dataframe(smedia)\n\n    st.divider()\nexcept:\n    st.info('No Results Found')\n    \n\n    \n\n\ntry:\n    st.markdown('<h4 class="veh1">ALL SOCIAL MEDIAL POSTINGS</h2>', unsafe_allow_html=True)\n    smediaV = session.table('DATA.SOCIAL_MEDIA')\n    smediaV = smediaV.with_column('"Date"',col('V')['DATA']['date'].astype(DateType()))\n    smediaV = smediaV.with_column('"Post"',col('V')['DATA']['post'].astype(StringType()))\n    smediaV = smediaV.with_column('"Sentiment"',col('V')['DATA']['sentiment_score'])\n    smediaV = smediaV.with_column('"Username"',col('V')['DATA']['username'].astype(StringType()))\n    smediaV = smediaV.with_column('"Incident Type"',col('V')['INCIDENT_TYPE'].astype(StringType()))\n    smediaV = smediaV.with_column('"MP"',col('V')['MP'].astype(StringType()))\n    smediaV.create_or_replace_view('DATA.V_SOCIAL_MEDIA')\n    st.write(session.table('DATA.V_SOCIAL_MEDIA'))\nexcept:\n    st.info('No Results Found')\n\nst.markdown('<h4 class="veh1">WHATS NEXT</h2>', unsafe_allow_html=True)\n\nst.markdown(\n    """\n    Go to the **ANALYSE_GEO_WITH_CORTEX** notebook to see how Cortex Search can make sense of this generated Unstructured Text\n\n    """,unsafe_allow_html=True)\n    \n         \n\n\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ERunning the App to generate data\u003C/h3\u003E\n","\u003Cp\u003EWithin the App, generate events for various mp's.   You can decide how many you would like to generate.  Once  you have selected what you would like to generate, press Generate Reports.  This will create Incidents relating to the MP.  They will also be in the context of the specified activity.\u003C/p\u003E\n","\u003Cp\u003EOnce you have generated events, select each MP under the Individual incidents section.  This will generate synthetic social media posts for the incidents.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st004.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFinally we will create a Streamlit app which adds this additional information to a map.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVisualise the data\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow9.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st005.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou will now be creating another streamlit app in order to visualise the results.\u003C/p\u003E\n","\u003Cp\u003EBelow is sample code which takes what we have learnt to create a streamlit with all the places event, location and incident data that from the shared datasets as well as synthetic data.\u003C/p\u003E\n","\u003Cp\u003EYou will need to install \u003Cstrong\u003Epydeck\u003C/strong\u003E.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGo back to the home page and add a new streamlit application using the same database, schema and warehouse as the previous app.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EGo to packages and add \u003Cstrong\u003Epydeck\u003C/strong\u003E to the streamlit app before you paste the app code.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/packages_option.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EAfter you have installed pydeck, delete the default contents of the newly created app\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EReplace all the sample code with what is provided below:\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/sis002.py\"\u003EThe code is also found here\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import max,min,avg,call_function, split,substr,hour,concat,col,sqrt,lit,array_slice,array_agg,object_construct,parse_json, to_geography, to_array,to_date,round\nfrom snowflake.snowpark.types import StringType,VariantType, DateType,FloatType, IntegerType,DecimalType\nimport json\nimport pandas as pd\nimport numpy as np\nimport pydeck as pdk\nst.set_page_config(layout="wide")\n# Write directly to the app\nst.markdown('<h1 class="heading">VISUALISE LOCATION DATA</h2>', unsafe_allow_html=True)\nst.write(\n    """<BR> This app shows key insight of places and events that may effect Northern Trains).\n    """,unsafe_allow_html=True)\n\n\n# Get the current credentials\nsession = get_active_session()\n\nst.markdown(\n    """\n    <style>\n    .heading{\n        background-color: rgb(41, 181, 232);  /* light blue background */\n        color: white;  /* white text */\n        padding: 60px;  /* add padding around the content */\n    }\n    .tabheading{\n        background-color: rgb(41, 181, 232);  /* light blue background */\n        color: white;  /* white text */\n        padding: 10px;  /* add padding around the content */\n    }\n    .veh1 {\n        color: rgb(125, 68, 207);  /* purple */\n    }\n    .veh2 {\n        color: rgb(212, 91, 144);  /* pink */\n    }\n    .veh3 {\n        color: rgb(255, 159, 54);  /* orange */\n    }\n    .veh4 {\n        padding: 10px;  /* add padding around the content */\n        color: rgb(0,53,69);  /* midnight */\n    }\n    \n    body {\n        color: rgb(0,53,69);\n    }\n    \n    div[role="tablist"] > div[aria-selected="true"] {\n        background-color: rgb(41, 181, 232);\n        color: rgb(0,53,69);  /* Change the text color if needed */\n    }\n    \n    </style>\n    """,\n    unsafe_allow_html=True\n)\n\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING."StationLatLong"')\n\n\n#create a point from the coordinates\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('"Longitude"'),col('"Latitude"')))\n\n#collect all the points into one row of data\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\n\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\n\n#find the centre point so the map will render from that location\n\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\n\n#create LON and LAT variables\n\ncentrepd = centre.select('LON','LAT').to_pandas()\nLON = centrepd.LON.iloc[0]\nLAT = centrepd.LAT.iloc[0]\n\n### transform the data in pandas so the pydeck visualisation tool can view it as a polygon\n\nenvelopepd = envelope.to_pandas()\nenvelopepd["coordinates"] = envelopepd["BOUNDARY"].apply(lambda row: json.loads(row)["coordinates"][0])\n\nplaces = session.table('OVERTURE_MAPS__PLACES.CARTO.PLACE')\nplaces = places.filter(col('ADDRESSES')['list'][0]['element']['country'] =='GB')\n\nplaces = places.select(col('NAMES')['primary'].astype(StringType()).alias('NAME'),\n                        col('PHONES')['list'][0]['element'].astype(StringType()).alias('PHONE'),\n                      col('CATEGORIES')['primary'].astype(StringType()).alias('CATEGORY'),\n                        col('CATEGORIES')['alternate']['list'][0]['element'].astype(StringType()).alias('ALTERNATE'),\n                    col('websites')['list'][0]['element'].astype(StringType()).alias('WEBSITE'),\n                      col('GEOMETRY'))\n\nplaces = places.filter(col('CATEGORY') =='restaurant')\n\n\nplaces = places.join(envelope,call_function('ST_WITHIN',places['GEOMETRY'],envelope['boundary']))\nplaces = places.with_column('LON',call_function('ST_X',col('GEOMETRY')))\nplaces = places.with_column('LAT',call_function('ST_Y',col('GEOMETRY')))\nplacespd = places.to_pandas()\n\n\ntrains_latlon_renamed = trains_latlon\n\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"CrsCode"','NAME')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Latitude"','LAT')\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('"Longitude"','LON')\n\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\n\ntrains_latlon_renamed = trains_latlon_renamed.join(station_info,station_info['"CRS Code"']==trains_latlon_renamed['NAME']).drop('"CRS Code"')\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\n\nevents = session.table('DATA.EVENTS_IN_THE_NORTH')\nevents = events.join_table_function('flatten',parse_json('EVENT_DATA')).select('VALUE')\nevents=events.with_column('NAME',col('VALUE')['NAME'].astype(StringType()))\nevents=events.with_column('DESCRIPTION',col('VALUE')['DESCRIPTION'].astype(StringType()))\nevents=events.with_column('CENTROID',to_geography(col('VALUE')['CENTROID']))\nevents=events.with_column('COLOR',col('VALUE')['COLOR'])\nevents=events.with_column('DATE',col('VALUE')['DATE'].astype(DateType())).drop('VALUE')\nevents=events.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('CENTROID'),lit(5)))\n\nevents = events.with_column('R',col('COLOR')[0])\nevents = events.with_column('G',col('COLOR')[1])\nevents = events.with_column('B',col('COLOR')[2])\nevents = events.with_column_renamed('DESCRIPTION','ALTERNATE')\neventspd = events.group_by('H3','NAME','ALTERNATE','R','G','B').count().to_pandas()\n\nincident_table = session.table('DATA.INCIDENTS')\nflatten = incident_table.select('MP','INCIDENT_TYPE',parse_json('GENERATED_EVENTS').alias('JSON'))\nflatten = flatten.join_table_function('FLATTEN',col('JSON')['incidents'])\nflatten = flatten.select('MP',col('INCIDENT_TYPE').alias('NAME'),'VALUE')\nflatten = flatten.with_column('ALTERNATE',\n                                  col('VALUE')['DESCRIPTION_OF_INCIDENTS'].astype(StringType()),\n                                 )\nflatten = flatten.with_column('LAT',\n                                  col('VALUE')['LOCATION']['LAT'].astype(FloatType()))                           \nflatten = flatten.with_column('LON',\n                                  col('VALUE')['LOCATION']['LON'].astype(FloatType()))\nflatten = flatten.with_column('REPORTED_BY',\n                                  col('VALUE')['REPORTED_BY'].astype(StringType()),\n                                 )\nflatten = flatten.with_column('DATE',\n                                  col('VALUE')['DATE'].astype(StringType()),\n                                 ).drop('VALUE')\nflattenpd = flatten.to_pandas()\n####MAP LAYERS\n\npolygon_layer = pdk.Layer(\n            "PolygonLayer",\n            envelopepd,\n            opacity=0.3,\n            get_polygon="coordinates",\n            filled=True,\n            get_fill_color=[16, 14, 40],\n            auto_highlight=True,\n            pickable=False,\n        )\n\n\npoi_l = pdk.Layer(\n            'ScatterplotLayer',\n            data=placespd,\n            opacity=0.8,\n            get_position='[LON, LAT]',\n            get_color='[255,255,255]',\n            radius_min_pixels=3,\n            radius_max_pixels=6,\n            pickable=True)\n\n\nnw_trains_l = pdk.Layer(\n            'ScatterplotLayer',\n            data=trains_latlon_renamed_pd,\n            get_position='[LON, LAT]',\n            get_color='[41,181,232]',\n            radius_min_pixels=3,\n            radius_max_pixels=20,\n            get_radius=4,\n            pickable=True)\n\nh3_events = pdk.Layer(\n        "H3HexagonLayer",\n        eventspd,\n        pickable=True,\n        stroked=True,\n        filled=True,\n        extruded=False,\n        get_hexagon="H3",\n        get_fill_color=["255-R","255-G","255-B"],\n        line_width_min_pixels=2,\n        opacity=0.3)\n\n\n\nincidents_layer = pdk.Layer(\n            'ScatterplotLayer',\n            data=flattenpd,\n            get_position='[LON, LAT]',\n            get_color='[255,165,0]',\n            get_radius=2000,\n            pickable=True)\n\n#### render the map showing trainstations based on overture maps\n\ntooltip = {\n   "html": """<b>Name:</b> {NAME} <br> <b>Alternate:</b> {ALTERNATE}""",\n   "style": {\n       "width":"50%",\n        "backgroundColor": "steelblue",\n        "color": "white",\n       "text-wrap": "balance"\n    }\n    }\n    \nletters = session.table('DATA.LETTERS_TO_MP')\nmps = letters.select('MP').distinct()\nselected_mp = st.selectbox('Choose MP:',mps)\nletterspd = letters.filter(col('MP')==lit(selected_mp)).to_pandas()\n#st.write(letterspd.PROMPT.iloc[0])\n\nst.divider()\ncol1,col2 = st.columns([0.5,0.5])\n\nwith col1:\n    st.markdown('<h4 class="veh1">MAP OF EVENTS WITH STATIONS AND RESTAURANTS</h2>', unsafe_allow_html=True)\n    st.pydeck_chart(pdk.Deck(\n    map_style=None,\n    initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=7,\n        height=750\n        ),\n    \n    layers= [polygon_layer, poi_l,nw_trains_l,h3_events,incidents_layer], tooltip = tooltip\n\n    ))\n    st.caption('Hover for more info')\nwith col2:\n    st.markdown('<h4 class="veh1">LETTERS TO MP</h2>', unsafe_allow_html=True)\n    st.write(letterspd.LETTER.iloc[0])\n    st.divider()\n\nsocial_media = session.table('DATA.V_SOCIAL_MEDIA').filter(col('MP')==selected_mp)\n\nst.markdown('<h4 class="veh1">SOCIAL MEDIA</h2>', unsafe_allow_html=True)\nst.table(social_media.drop('V'))\n\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EUse Cortex to Embed Generated Text and create a Search Service\u003C/h2\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow9.png\" alt=\"assets\"\u003E\u003C/p\u003E\n","\u003Cp\u003EDuring the lab we have produced quite a bit of unstructured data from social media posts, to incidents, through to letters.  Now lets use vector embedding functionality to make this information searchable.  This is really useful when you would like to use an LLM to answer questions but do not want to send the entire dataset as a large object - which could be quite expensive and also would take a long time to run.  For large text blocks, you may wish to 'chunk' the data first.  As the text in this scenario is relatively small - we will keep it as is.  Once you have explored the principles of how text searching works, you will create a Cortex Search Service.\u003C/p\u003E\n","\u003Cp\u003EClick here and download the notebook from GitHub\u003C/p\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/Vector_Embeddings.ipynb\"\u003E\u003Cstrong\u003EVector_Embeddings.ipynb\u003C/strong\u003E\u003C/a\u003E\u003C/p\u003E\n","\u003Cp\u003EImport as a new snowflake notebook.  Add it to the BUILD.UK.NOTEBOOKS schema and follow the instructions provided in the notebook.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EBringing in Met Office Weather\u003C/h2\u003E\n","\u003Ch3\u003EMet office Weather\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow10.png\" alt=\"alt text\"\u003E\nNow we will leverage the weather data to understand the weather forecasts for each Event.  This time we will import a notebook which has already been created.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EGo to the Home page and select Notebooks\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EDownload the following python notebook file\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/weather.ipynb\"\u003E\u003Cstrong\u003Eweather.ipynb\u003C/strong\u003E\u003C/a\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the notebbook menu selection, select Import from .ipynb file and choose the downloaded file\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W001.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPopulate the fields with the following:\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W003.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EFollow the instructions as per the imported notebook.  You will end up with a weather forecast for each of the different events.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W002.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStreamlit showing weather by Train Station\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W004.png\" alt=\"alt text\"\u003E\u003C/p\u003E\n","\u003Cp\u003Ethis is an example weather app which shows the weather forecast for each train station\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy and paste the code below into a new \u003Cstrong\u003Estreamlit app\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ENOTE: you will need to install the package \u003Cstrong\u003Epydeck\u003C/strong\u003E from the packages menu.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E\n# Import python packages\nimport streamlit as st\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.functions import any_value,max,min,avg,call_function, split,substr,hour,concat,col,sqrt,lit,array_slice,array_agg,object_construct,parse_json, to_geography, to_array,to_date\nfrom snowflake.snowpark.types import StringType,VariantType, DateType, IntegerType,DecimalType\nimport json\nimport pandas as pd\nimport numpy as np\nimport pydeck as pdk\n\nst.set_page_config(layout="wide")\n\n# Write directly to the app\n\ncol1,col2,col3 = st.columns([0.2,0.6,0.2])\nwith col1:\n    st.image('https://cdn.prgloo.com/web/NorthernRail/NorthernNewLogo.png')\n\nwith col3:\n    st.image('https://upload.wikimedia.org/wikipedia/en/thumb/f/f4/Met_Office.svg/1200px-Met_Office.svg.png')\nst.title("Northern Trains Weather Data")\n\nst.write(\n    """This app shows the weather that may affect Northern Trains).\n    """\n)\n\n# Get the current credentials\nsession = get_active_session()\n\n#### add postcode sector to trains\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING."StationLatLong"')\ntrains_latlon = trains_latlon.with_column('POSTCODE_SPLIT',split(col('"Postcode"'),lit(' ')))\ntrains_latlon = trains_latlon.with_column('Postcode_outcode',col('POSTCODE_SPLIT')[0].astype(StringType()))\ntrains_latlon = trains_latlon.with_column('Postcode_inward_code',col('POSTCODE_SPLIT')[1].astype(StringType()))\ntrains_latlon = trains_latlon.with_column('Postcode_Sector',concat('POSTCODE_OUTCODE',lit('_'),substr(col('Postcode_inward_code'),1,1)))\ntrains_latlon = trains_latlon.drop('POSTCODE_SPLIT','POSTCODE_OUTCODE','POSTCODE_INWARD_CODE')\n\n\n# Add hourly and daily weather for each train station\n\nweather_hourly = session.table('POSTCODE_SECTOR_WEATHER_FORECASTS.PCSECT_FORECAST."postcode_hourly_view"')\nweather_daily = session.table('POSTCODE_SECTOR_WEATHER_FORECASTS.PCSECT_FORECAST."postcode_daily_view"')\n\nweather_hourly_max = weather_hourly.agg(max('"Issued_at"').alias('MAX'))\nweather_hourly = weather_hourly.join(weather_hourly_max,weather_hourly_max['MAX']==weather_hourly['"Issued_at"']).drop('MAX')\n\nweather_daily_max = weather_daily.agg(max('"Issued_at"').alias('MAX'))\nweather_daily = weather_daily.join(weather_daily_max,weather_daily_max['MAX']==weather_daily['"Issued_at"']).drop('MAX')\n\nweather_hourly = weather_hourly.join(trains_latlon,trains_latlon['POSTCODE_SECTOR']==weather_hourly['PC_SECT']).drop('PC_SECT')\nweather_hourly = weather_hourly.with_column('VALIDITY_DATE',to_date('"Validity_date_and_time"'))\n\nweather_daily_t = weather_daily.join(trains_latlon,trains_latlon['POSTCODE_SECTOR']==weather_daily['PC_SECT']).drop('PC_SECT')\n\n\n\n\n\n\n\n\n#add h3 column to weather\nweather_daily_h3 = weather_daily_t.with_column('H3',call_function('H3_LATLNG_TO_CELL_STRING',col('"Latitude"'),col('"Longitude"'),lit(5)))\n\n\n#add northern trains boundary box\n#create a point from the coordinates\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('"Longitude"'),col('"Latitude"')))\n\n#collect all the points into one row of data\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\n\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\n\n# Create a centre point to position the maps\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\n\n\n\n#create LON and LAT variables\n\ncentrepd = centre.select('LON','LAT').to_pandas()\nLON = centrepd.LON.iloc[0]\nLAT = centrepd.LAT.iloc[0]\n\n# index the boundary with h3 res 5\nboundary_h3 = envelope.with_column('H3',call_function('H3_COVERAGE_STRINGS',col('BOUNDARY'),lit(5))).join_table_function('flatten',col('H3')).select(col('VALUE').astype(StringType()).alias('H3'))\n\n\n#### use the postcodes data set to add H3 to postcode sectors\npostcodes = weather_daily.group_by('PC_SECT').agg(any_value('POINT').alias('POINT'))\npostcodes = postcodes.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('"POINT"'),lit(5)))\npostcodes = postcodes.join(boundary_h3,boundary_h3['H3']==postcodes['H3'],lsuffix='L').drop('H3L')\n\n\n### join weather data to the boundary\nweather_boundary = boundary_h3.join(weather_daily_h3,weather_daily_h3['H3']==boundary_h3['H3'],lsuffix='L',type='right').drop('H3L')\nweather_boundarypd = weather_boundary.select('H3','"Max_temperature_day"','"Max_feels_like_temperature_day"').to_pandas()\n\n\nfrom snowflake.snowpark.functions import dateadd\n\n\n##join weather to all postcode sectors\n\n\nweather_daily_north = weather_daily.join(weather_daily_max,weather_daily_max['MAX']==weather_daily['"Issued_at"']).drop('MAX')\nweather_daily_north = weather_daily_north.join(postcodes,postcodes['PC_SECT']==weather_daily_north['PC_SECT'])\n\n\n\n\n\n\n\nstation_filter = trains_latlon.select('"CrsCode"')\ndate_filter = weather_hourly.agg(max('VALIDITY_DATE').alias('MAX'),\n                          min('VALIDITY_DATE').alias('MIN'))\n\ndate_filter = date_filter.with_column('MIN',dateadd('DAY',lit(1),col('MIN'))).to_pandas()\n\nwith st.form('select_data'):\n    col1,col2, = st.columns([0.3,0.7])\n    with col1:\n        selected_station = st.selectbox('Select Station:',station_filter)\n        selected_date = st.date_input('Select Date:',date_filter.MIN.iloc[0],date_filter.MIN.iloc[0],date_filter.MAX.iloc[0])\n        model = 'reka-flash','mixtral-8x7b', 'gemma-7b','llama2-70b-chat'\n        select_model = st.selectbox('Choose Model',model)\n        run = st.form_submit_button('Run Dashboard')\n    with col2:\n        st.image('https://cdn.prgloo.com/media/aad47116f9cc4c33b6f02e51fb8070f4.jpg?width=1200&height=400')\n\nif run:\n\n    #st.write(weather_daily_north).limit(10)\n    todaypd = weather_daily_t.filter((col('"Validity_date"')==selected_date)\n                                     & (col('"CrsCode"')==selected_station)).to_pandas()\n    st.write(todaypd)\n    melt = pd.melt(todaypd)\n    melt['variable'] = melt['variable'].astype("string")\n    melt['value'] = melt['value'].astype("string")\n\n    ##### create an object to feed into the LLM\n    object = session.create_dataframe(melt)\n    object = object.with_column('object',object_construct(col('"variable"'),col('"value"')))\n    \n    object = object.select(array_agg('OBJECT').alias('OBJECT'))\n\n    \n\n        ##### create an LLM prompt which includes the data object\n    prompt = concat(lit('Summarise the weather report in 500 words using paragraphs for the date specified which includes relevant emojis to summarise the weather based on the following dataset'),\n                    col('object').astype(StringType()),\n                   lit('USE APPROPIATE MARKDOWN TO ENHANCE THE PRESENTATION. NO COMMENTS'))\n    \n    # construct the cortex.complete function - this will run based on the chosen model\n    complete = call_function('snowflake.cortex.complete',lit(select_model),lit(prompt))\n    \n    object = object.select(complete)\n\n    #### print the results\n    st.write(object.collect()[0][0])\n\n    st.divider()\n    \n    # dataframes for h3 maps\n\n    weather_daily_north = weather_daily_north.select('H3',col('"Validity_date"').astype(StringType()).alias('DATE'),\n                                                     '"Max_temperature_day"',\n                                                     '"Max_feels_like_temperature_day"')\n    \n    weather_daily_north_0 = weather_daily_north.filter(col('"Validity_date"')== dateadd('day',lit(1),lit(selected_date)))\n    weather_daily_north_1 = weather_daily_north.filter(col('"Validity_date"')== dateadd('day',lit(2),lit(selected_date)))\n    weather_daily_north_2 = weather_daily_north.filter(col('"Validity_date"')== dateadd('day',lit(3),lit(selected_date)))\n\n\n    ####\n    weather_daily_northpd = weather_daily_north_0.to_pandas()\n    weather_daily_northpd1 = weather_daily_north_1.to_pandas()\n    weather_daily_northpd2 = weather_daily_north_2.to_pandas()\n\n    \n\n    #################CONSTRUCT THE MAP\n\n    ### create a layer to cover the boundary with H3 Indexes\n\n    st.markdown('#### TEMPERATURE COMPARISON OVER THE NEXT 3 DAYS')\n\n    filtered_station = trains_latlon.filter(col('"CrsCode"')==selected_station)\n    filtered_station_pd = filtered_station.to_pandas()\n\n    #st.write(filtered_station)\n\n\n\n    station = pdk.Layer(\n            'ScatterplotLayer',\n            data=filtered_station_pd,\n            get_position='[Longitude,Latitude]',\n            get_color='[0,0,0]',\n            get_radius=2000,\n            radius_scale=6,\n            radius_min_pixels=1,\n            opacity=0.6,\n            radius_max_pixels=1000,\n            filled=True,\n            line_width_min_pixels=1,\n            pickable=False)\n    \n\n    h3_coverage = pdk.Layer(\n        "H3HexagonLayer",\n        weather_daily_northpd,\n        pickable=True,\n        stroked=True,\n        filled=True,\n        extruded=False,\n        get_hexagon="H3",\n        get_fill_color=["0+Max_temperature_day*8","(255/100) * 0+Max_temperature_day*12","(255/100) * 200-Max_temperature_day*9"],\n        line_width_min_pixels=0,\n        opacity=0.9)\n\n    h3_coverage_1 = pdk.Layer(\n        "H3HexagonLayer",\n        weather_daily_northpd1,\n        pickable=True,\n        stroked=True,\n        filled=True,\n        extruded=False,\n        get_hexagon="H3",\n        get_fill_color=["0+Max_temperature_day*8","(255/100) * 0+Max_temperature_day*12","(255/100) * 200-Max_temperature_day*9"],\n        line_width_min_pixels=0,\n        opacity=0.9)\n\n    h3_coverage_2 = pdk.Layer(\n        "H3HexagonLayer",\n        weather_daily_northpd2,\n        pickable=True,\n        stroked=True,\n        filled=True,\n        extruded=False,\n        get_hexagon="H3",\n        get_fill_color=["0+Max_temperature_day*8","(255/100) * 0+Max_temperature_day*12","(255/100) * 200-Max_temperature_day*9"],\n        line_width_min_pixels=0,\n        opacity=0.9)\n\n\n\n    #### render the map \n\n\n    col1,col2,col3 = st.columns(3)\n\n    with col1:\n        st.write(weather_daily_northpd.DATE.iloc[1])\n        st.pydeck_chart(pdk.Deck(\n        map_style=None,\n        initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=400\n        ),\n    \n        layers= [h3_coverage,station],tooltip = {'text':"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}"}\n\n        ))\n\n    with col2:\n        st.write(weather_daily_northpd1.DATE.iloc[1])\n        st.pydeck_chart(pdk.Deck(\n        map_style=None,\n        initial_view_state=pdk.ViewState(\n            latitude=LAT,\n            longitude=LON,\n            zoom=5,\n            height=400\n            ),\n    \n        layers= [h3_coverage_1,station],tooltip = {'text':"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}"}\n\n        ))\n\n    with col3:\n        st.write(weather_daily_northpd2.DATE.iloc[1])\n        st.pydeck_chart(pdk.Deck(\n        map_style=None,\n        initial_view_state=pdk.ViewState(\n        latitude=LAT,\n        longitude=LON,\n        zoom=5,\n        height=400\n            ),\n    \n        layers= [h3_coverage_2,station],tooltip = {'text':"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}"}\n\n        ))\n\n\n\n    \n    weather_filter_hour = weather_hourly.filter(col('"CrsCode"') == selected_station)\n    weather_filter_day = weather_daily_t.filter(col('"CrsCode"') == selected_station)\n\n    weather_filter_hour = weather_filter_hour.filter(col('VALIDITY_DATE')==selected_date)\n    #weather_filter_day = weather_filter_day.filter(col('"Validity_date"')==selected_date)\n    weather_filter_hour = weather_filter_hour.with_column('HOUR',hour('"Validity_date_and_time"'))\n\n\n    weather_filter_hour = weather_filter_hour.group_by('HOUR').agg(avg('"Feels_like_temperature"').alias('Feels Like'),\n                                                              avg('"Screen_temperature"').alias('"Screen"'),\n                                                               avg('"Screen_Dew_Point_Temperature"').alias('"Dew Point"'),\n    avg('"UV_index"').alias(' '),                                                        avg('"Relative_humidity"').alias('"Relative"'),                                                        avg('"Wind_speed"').alias('"Speed"'),\n    avg('"Wind_gust"').alias('"Gust"'),).to_pandas()\n\n    #weather_filter_hour\n    st.markdown('#### 12 DAY DAILY FORECAST')\n\n    col1,col2 = st.columns(2)\n    with col1:\n        st.markdown('##### TEMPERATURE')\n    #weather_filter_day\n\n        st.line_chart(weather_filter_day.to_pandas(),x='Validity_date',y=['Max_temperature_day','Max_feels_like_temperature_day','Min_feels_like_temperature_night'])\n\n\n    with col2:\n        st.markdown('##### HUMIDITY')\n    #weather_filter_day\n\n        st.line_chart(weather_filter_day.to_pandas(),x='Validity_date',y=['Relative_humidity_Approx_Local_Midday','Relative_humidity_Approx_Local_Midnight'])\n\n    st.divider()\n\n    st.markdown('#### HOURLY VIEW')\n\n    col1,col2 = st.columns(2)\n\n    with col1:\n        st.markdown('##### TEMPERATURE')\n        st.line_chart(weather_filter_hour,x='HOUR',y=['Feels Like','Screen','Dew Point'])\n    with col2:\n        st.markdown('##### UV INDEX')\n        st.bar_chart(weather_filter_hour,x='HOUR',y=[' '])\n\n    with col1:\n        st.markdown('##### WIND')\n        st.line_chart(weather_filter_hour,x='HOUR',y=['Speed','Gust'])\n    with col2:\n        st.markdown('##### HUMIDITY')\n        st.bar_chart(weather_filter_hour,x='HOUR',y=['Relative'])\n\n\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EConclusion\u003C/h3\u003E\n","\u003Cp\u003ESnowflake provides powerful capabilities when you bring Cortex with Snowpark and Streamlit's visualization capabilities together.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n","\u003Cp\u003EYou will have learned the following:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to use Snowflake Cortex to produce descriptive tooltips, create letters, suggest large events as well as produce synthetic data for simulating potential incidents that may occur.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to leverage snowpark data frames to wrangle shared datasets into meaningful dataframes\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EHow to utilise Pydeck to combine local information on a multi layered map.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n","\u003Ch4\u003EDatasources used in the lab\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/providers/GZT0ZKUCHE3/CARTO?dbName=OVERTURE_MAPS__PLACES\"\u003EOverture Maps - Places\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTDZ2CEXT/northern-trains-ltd-northern-trains-station-data?search=northern\"\u003ENorthern Trains\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EMet office \u003Cstrong\u003Eto follow\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ESource code\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data\"\u003ESource Code on Github\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EFurther Related Material\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/functions-geospatial\"\u003EGeospatial Functions\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://h3geo.org/docs/\"\u003EH3 Indexing\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://streamlit.io/\"\u003EStreamlit\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://deckgl.readthedocs.io/en/latest/index.html#\"\u003EPydeck\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"/en/developers/guides/geo-for-machine-learning/\"\u003EGetting started with Geospatial AI and ML using Snowflake Cortex\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Ca href=\"https://quickstarts.snowflake.com/guide/asking_questions_to_your_own_documents_with_snowflake_cortex/index.html#0\"\u003EBuild a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex\u003C/a\u003E\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E"],":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\r\n## Overview \r\n\r\nAnalyzing Location specific data in order to make decisions often requires 'niche' techniques which are often worked on by 'geography' experts.   In snowflake, everyone can be an expert in any location in which they need to understand - whether it's points of interest, transport links or government boundaries - all is feasible using standard functionality available within Snowflake.  \r\n\r\nAlready, there are so many location specific datasets available within the **Snowflake Marketplace** which significantly reduces data ingestion and engineering time.  Getting access to these datasets are as simple as **Get Data**, where you will enjoy leveraging location specific features using either SQL Queries or Snowpark Dataframes.  And these result sets are then rendered easily using **Streamlit in Snowflake** dashboards.\r\n\r\n**Snowflake Cortex LLMs** - are used to save the analyst, engineer or even BI developer time - which can help a multitude of tasks - from improving the readability of location tooltips, to the generation or synthetic data for testing purposes.\r\n\r\n\r\n\r\nIn this quickstart, we will be leveraging the the tools within Snowflake to:\r\n\r\n  - **Visualise** the location of train stations within the north of england and understand where nearby restaurants are located\r\n\r\n  - **Discover** where the locations of Large events are and where they may impact stations and Restaurants\r\n\r\n  - **Understand** the weather conditions which might impact train stations\r\n  - **Generate** \r\n  \r\n    * A warning letter to the MP after discovering potential risk\r\n\r\n    * Synthezised events events which might happen and will impact services\r\n\r\n  - **Visualise**  the data using Streamlit\r\n\r\n\r\n### Lab Flow\r\nHere is a flow of what you will achieve if you complete all steps in the quickstart.  You will be reminded of where you are at key intervals.\r\n\r\n  ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow1.png)\r\n\r\n\r\n\r\n### Prerequisites\r\n- A new free trial of Snowflake in **a region of you choice***.\r\n\r\n \r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I001.png)\r\n\r\n**Note**:  All datasets for this dataset used are either frequently replicated or originate in AWS London.  If you choose an alternative location, you may need to wait 10 minutes for the replication process to finish.\r\n\r\n\r\n### What You’ll Learn \r\n\r\n- An understanding of Geospatial data in Snowflake\r\n- Using Cortex functions with Snowpark\r\n- Creating a location centric application using Streamlit \r\n- An insight to UK centric Datasets such as\r\n  - Places of Interest\r\n  - Weather\r\n  - Train Stations\r\n- Using Notebooks and Streamlit to make discoveries and present findings \r\n\r\n\r\n### What You’ll Build \r\n- A streamlit apps and a notebook to visualise your results\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Initial Setup\r\n\r\nOpen up a new SQL worksheet and run the following commands. To open up a new SQL worksheet, select Projects » Worksheets, then click the blue plus button and select SQL worksheet.\r\n\r\n~~~sql\r\nCREATE OR REPLACE DATABASE BUILD_UK;\r\n\r\nCREATE OR REPLACE SCHEMA STREAMLITS;\r\n\r\nCREATE OR REPLACE SCHEMA NOTEBOOKS;\r\n\r\nCREATE OR REPLACE SCHEMA DATA;\r\n\r\nCREATE OR REPLACE WAREHOUSE BUILD_UK_WAREHOUSE;\r\n\r\nGRANT USAGE, OPERATE ON WAREHOUSE BUILD_UK_WAREHOUSE TO ROLE ACCOUNTADMIN;\r\n\r\nGRANT USAGE ON DATABASE BUILD_UK TO ROLE ACCOUNTADMIN;\r\n\r\n-------- use this paramater to try the LLMs using a region which does not currently support them.\r\n\r\nALTER ACCOUNT SET CORTEX_ENABLED_CROSS_REGION = 'ANY_REGION';\r\n\r\n~~~\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Snowflake Marketplace\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow0.png)\r\nOnce logged in go to the Snowflake Marketplace - this is under Data Products \u003E Snowflake Marketplace\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I002.png)\r\n\r\nSearch for Northern Trains Station Data\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I003.png)\r\n\r\nPress **Get** to get the data from the marketplace.  Do not change the database name.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I003a.png)\r\n\r\nSearch for Overture Maps - Places\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I004.png)\r\n\r\nClick on the following dataset then press **Get** Do not change the database name.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I004a.png)\r\n\r\n\r\n\r\nSearch for the **Met office Weather Data**\r\n- The exact name is **Postcode Sector Weather Forecasts**\r\n- Press **Try for Free** - Keep the name as it is.  You will have 14 days of free weather data to try for 14 days.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/metoffice.png)\r\n\r\nYou will have access to the dataset for a period of **14 days**\r\n\r\n\r\n## Create your First map Layer\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow2.png)\r\n\r\nIn Snowsight, go back to the home page and select **Projects** » **Notebooks**.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I006.png)\r\nObserve the alternative actions - you can also import a notebook or create one from github repository  (if you have github connected to snowflake).\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I007.png)\r\n\r\nToday we will be creating a new notebook from scratch.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I008.png)\r\n\r\nIn **Notebook location**, select BUILD_UK from the list of available databases and NOTEBOOKS from the available schemas. All data within the notebook will be held inside the chosen database and schema and will sit inside an automatically generated stage. Select the BUILD_UK_WAREHOUSE and press **Create**.\r\n\r\n---\r\n**Note**\r\n\r\n\u003E If you wish, you can import the previously created notebook from the following location:\r\n\r\n[notebook in Github](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/Trains%20and%20Restaurants.ipynb)\r\n\r\n\u003E However, to experience the creation of the notebook yourself, carry on with the blank notebook, and copy/paste the code as we go along.\r\n\r\n\r\n#### Creating your first Map\r\n\r\nOnce the notebook has loaded, Remove all cells in the notebook.  You should see something like this:\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I009.png)\r\n\r\nBefore we start working in our notebook, we will add an additional package which is not installed as default.  This package is available within the Anaconda Snowflake channel and is easily accessible.\r\n\r\n**Add the Pydeck package for visualising location data**\r\n\r\nIn the top menu, under Packages » Anaconda Packages, import pydeck.  You need this to render maps. \r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I010.png)\r\n\r\nNow we have all the packages we need.  Lets start the notebook by pressing **Start** which is at the top right hand corner of the screen.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I009a.png)\r\n\r\nYou will be using a variety of Snowflake functions to do some transformation tasks.  Some of the functions are built into the snowpark library (such as array_agg, parse_json), others we need to call using the **call_function** module.  **call_function** allows the user to leverage ANY Snowflake scalar function - you can use this for both built in as well as user defined functions.\r\n\r\nAll functions are held inside the **snowflake.snowpark.functions** module.\r\n\r\nWe will import the call_function, streamlit library, json, numpy, pandas and pydeck packages.  For pydeck, you will need to add the package as this is not installed by default.\r\n\r\nClick on the + Python button to add a new Python cell.  On the top left hand corner of the screen, you can rename your cell from 'cell1' to something more meaningful.  This is useful for debugging purposes.\r\n\r\n**Rename** cell1 to libraries by typing over the text 'cell1'.\r\n\r\nCopy and paste the following code into the newly created cell.\r\n\r\n```python\r\n# Import python packages\r\nimport streamlit as st\r\nfrom snowflake.snowpark.context import get_active_session\r\nfrom snowflake.snowpark.functions import *\r\nfrom snowflake.snowpark.types import *\r\nimport json\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pydeck as pdk\r\n\r\n# Write directly to the app\r\nst.title(\"UK Analytics within the North of England :train:\")\r\nst.write(\r\n    \"\"\"This app shows key insight of places and events that may effect Northern Trains.\r\n    \"\"\"\r\n)\r\n\r\n# Get the current credentials\r\nsession = get_active_session()\r\n```\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I011.png)\r\n\r\nAdd a new python cell by hovering over the bottom edge of the cell and select Python.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I012.png)\r\n\r\nRename the the cell to **n_trains_data**\r\n\r\n\r\nWe will firstly leverage the Northern Trains dataset to filter the carto overture maps places dataset.  We want to do this in order to get Points of interests that are relevant for the Northern Trains locality.  Joining by each Station Code would be resource hungry - plus we do not want to join by exact locations, only by roughly where all of the train stations are situated.\r\n\r\n\r\n\r\n**Create a Boundary Box to filter the data based on Northern Trains**\r\n\r\n\r\n\r\nWe will be seeing where Northern Train stations are and will create a boundary box to filter all our data so it will be only within the boundaries of the train stations.  Firstly, lets find out where the stations are so we can effectively draw a boundary box around it. \r\n\r\n\r\n\r\nCopy and paste the code into the new python cell and run this notebook cell: \r\n\r\n```python\r\n\r\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING.\"StationLatLong\"')\r\n\r\nst.markdown('#### A dataframe which shows all the train stations')\r\nst.dataframe(trains_latlon)\r\nst.map(trains_latlon, latitude='Latitude', longitude='Longitude')\r\n\r\n```\r\n\r\nBelow is an example of what you should see\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I013.png)\r\n\r\nWe have created a dataframe which is simply selecting the shared station table.  We have also leveraged Streamlit to create a title,  leveraged st.dataframe to display the snowpark dataframe in a clear way, and finally created a simple map using the streamlit [st.map](https://docs.streamlit.io/develop/api-reference/charts/st.map) function.  \r\n\r\nst.map is useful for quickly generating simple maps by rendering latitude and longitude as points.  Pydeck however, has more sophisticated capabilities such as rendering lines, points, polygons and H3 indexes.  We will be leveraging the Pydeck library in the next step\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Create a Boundary for filtering purposes\r\n\r\n\r\nYou previously loaded the places dataset from Carto Overture maps.  This dataset offers a comprehensive list of places of interest across the world such as restaurants, bars and schools.  We want to filter this dataset to only list places of interest that occur within the Northern Trains locality.  Creating a Boundary box is the easiest option.\r\n\r\nCopy and Paste the following into a new cell and name the new cell **boundary_box**\r\n\r\n```python\r\n#create a point from the coordinates\r\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('\"Longitude\"'),col('\"Latitude\"')))\r\n\r\n#collect all the points into one row of data\r\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\r\n\r\n#### convert from geography to geometry\r\nenvelope = envelope.select(to_geometry('POINTS').alias('POINTS'))\r\n\r\n\r\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\r\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\r\n\r\n#convert back to geography\r\nenvelope = envelope.select(to_geography('BOUNDARY').alias('BOUNDARY'))\r\nenvelope.collect()[0][0]\r\n\r\n```\r\nYou will see this has generated 5 sets of coordinates in order to draw the boundary box.  \r\n\r\n\u003E**FACT:** Every **valid** polygon will have the same last pair of coordinates as the first pair.   Lets visualise what this looks like using the library pydeck.  Although st.map is useful for simple quick visualisation of points, pydeck has the ability to visualise lines, points and polygons in 2D and 3D.  It also has layer options for lines, points, icons and H3 indexes.\r\n\r\nhttps://deckgl.readthedocs.io/en/latest/\r\n\r\nAdd a new Python cell and name it **boundary_layer**.  Copy and paste the following:\r\n\r\n```python\r\n#find the centre point so the map will render from that location\r\n\r\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\r\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\r\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\r\n\r\n#create LON and LAT variables\r\n\r\ncentrepd = centre.select('LON','LAT').to_pandas()\r\nLON = centrepd.LON.iloc[0]\r\nLAT = centrepd.LAT.iloc[0]\r\n\r\n### transform the data in pandas so the pydeck visualisation tool can view it as a polygon\r\n\r\nenvelopepd = envelope.to_pandas()\r\nenvelopepd[\"coordinates\"] = envelopepd[\"BOUNDARY\"].apply(lambda row: json.loads(row)[\"coordinates\"][0])\r\n\r\n\r\n####visualise on a map\r\n\r\n#### create a layer - this layer will visualise the rectangle\r\n\r\npolygon_layer = pdk.Layer(\r\n            \"PolygonLayer\",\r\n            envelopepd,\r\n            opacity=0.3,\r\n            get_polygon=\"coordinates\",\r\n            filled=True,\r\n            get_fill_color=[16, 14, 40],\r\n            auto_highlight=True,\r\n            pickable=False,\r\n        )\r\n\r\n \r\n#### render the map \r\n    \r\nst.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=400\r\n        ),\r\n    \r\nlayers= [polygon_layer]\r\n\r\n))\r\n```\r\n\r\nYou will see that to render the map, we present the data in a format for pydeck to accurately read.  The final transformed dataset is a pandas dDataframe.  We specify the dataframe in a pydeck layer, then apply this layer to a streamlit pydeck chart.  If we want, we can use the same logic in order to create a streamlit app.  Snowflake Notebooks are great as you can render streamlit on the fly - without having to run the ‘app’ separately.\r\n\r\n\r\n\r\nWhen you run the cell, the boundary box should look like this:\r\n\r\n![boundarybox](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I014.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Filtering the data using the boundary box\r\n\r\nNext, lets leverage and filter the overture maps so these will only consist of data within this area.  Overture maps consist of location data across the entire globe.\r\n\r\nCreate and preview the places dataframe - we are also filtering to only display places in the UK.\r\n\r\nCopy and paste the python code into a new python cell.  Name the cell **places_dataset**\r\n\r\n```python\r\n\r\nplaces_1 = session.table('OVERTURE_MAPS__PLACES.CARTO.PLACE')\r\nplaces_1 = places_1.filter(col('ADDRESSES')['list'][0]['element']['country'] =='GB')\r\n\r\nplaces_1.limit(3)\r\n```\r\nWhen you run the cell, you will see there is a lot of semi structured data returned.  We will use snowflake’s native **semi structured** querying capability to take key elements out of the data which includes information concerning the location\r\n\r\nIn a new cell which you will name **places_refined**, paste the following and run the cell:\r\n\r\n```python\r\n\r\nplaces_2 = places_1.select(col('NAMES')['primary'].astype(StringType()).alias('NAME'),\r\n                        col('PHONES')['list'][0]['element'].astype(StringType()).alias('PHONE'),\r\n                      col('CATEGORIES')['primary'].astype(StringType()).alias('CATEGORY'),\r\n                        col('CATEGORIES')['alternate']['list'][0]['element'].astype(StringType()).alias('ALTERNATE'),\r\n                    col('websites')['list'][0]['element'].astype(StringType()).alias('WEBSITE'),\r\n                      col('GEOMETRY'))\r\n                        \r\n\r\nplaces_2.limit(10)\r\n```\r\nThis is what you should see.  The exact dataset set may ot be the same as the screenshot but will contain the same type of data.\r\n![semi_structured_filtered](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I015.png)\r\n\r\nYou will now filter the data to only view places which are categorised as **train_station**.\r\n\r\nCopy and paste the code below into a new **python** cell.  Name the cell **places_filtered_boundary**\r\n\r\n```python\r\nplaces_3 = places_2.filter(col('CATEGORY') =='train_station')\r\n\r\nplaces_3 = places_3.join(envelope,call_function('ST_WITHIN',places_3['GEOMETRY'],envelope['boundary']))\r\nplaces_3 = places_3.with_column('LON',call_function('ST_X',col('GEOMETRY')))\r\nplaces_3 = places_3.with_column('LAT',call_function('ST_Y',col('GEOMETRY')))\r\nst.write(places_3)\r\n\r\n```\r\nWe can view the points on a map easily by using st.map(places) but as pydeck has many more options such as different mark types, tool tips and layers we will create an additional pydeck layer which adds this data to the previously created data layer.  When you hover over in the boundary box you will see a tooltip containing the  alternate category as well as the place name.\r\n\r\nCopy and paste the following into a new cell and name the cell **places_visualised**:\r\n\r\n```python\r\n\r\nplacespd = places_3.to_pandas()\r\npoi_l = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=placespd,\r\n            get_position='[LON, LAT]',\r\n            get_color='[255,255,255]',\r\n            get_radius=600,\r\n            pickable=True)\r\n\r\n#### render the map showing trainstations based on overture maps\r\n    \r\nst.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=400\r\n        ),\r\n    \r\nlayers= [polygon_layer, poi_l], tooltip = {'text':\"Place Name: {NAME}, alternate: {ALTERNATE}\"}\r\n\r\n))\r\n\r\n```\r\n- Go back to **places_filter_boundary** cell and modify the category in the filter from train_station to restaurant. \r\n\r\n\r\n- Select **Run all below**.  These steps will start from retreiving a fresh table from the marketplace table:\r\n\r\n![boundarybox](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I016.jpg)\r\n\r\nThe results should look like this.\r\n\r\n![boundarybox](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I017.png)\r\n\r\nNow we have a map with all the restaurants within the Northern trains boundary.  Lets now compare this with another layer which shows the train stations provided by Northern Trains.  We have already loaded the train station locations into the notebook when we created the boundary box.  \r\n\r\nCreate a new cell and name it **trains_visualised**.  Copy and paste the following:\r\n\r\n```python\r\n\r\ntrains_latlon_renamed = trains_latlon\r\n\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"CrsCode\"','NAME')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Postcode\"','ALTERNATE')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Latitude\"','LAT')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Longitude\"','LON')\r\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\r\n\r\nnw_trains_l = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=trains_latlon_renamed_pd,\r\n            get_position='[LON, LAT]',\r\n            get_color='[0,187,255]',\r\n            get_radius=600,\r\n            pickable=True)\r\n\r\n#### render the map showing trainstations based on overture maps\r\n    \r\nst.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=400\r\n        ),\r\n    \r\nlayers= [polygon_layer, poi_l, nw_trains_l], tooltip = {'text':\"Place Name: {NAME}, alternate: {ALTERNATE}\"}\r\n\r\n))\r\n\r\n```\r\nWe have now rendered a multi layer map which overlays restaurants and northern rail train stations.  Next, we will leverage Cortex to curate descriptive tooltips derived by station attributes.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Use Cortex to describe the Train Stations\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow3.png)\r\nAt the moment we only have very basic train station information.  Lets add more info from the shared dataset:\r\n\r\nCopy and paste the following code into a new cell.  Name the cell **station_attributes**  \r\n\r\n```python\r\n\r\nfurther_train_info_1 = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING.\"STATION ATTRIBUTES 2\"')\r\nfurther_train_info_1.limit(4)\r\n\r\n```\r\n\r\n![boundarybox](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I018.png)\r\n\r\nWe have quite a bit of information, it would be great if we can  use Snowflake Cortex LLM to explain this data and then we could add the results to our tool tip!! Right now we only have the postcode in the tooltip.\r\n\r\nLet's do that by creating a new cell.  Name the cell **cortex_tooltip**.  \r\n\r\nPaste the following into the new cell and run. This takes around 1.5 minutes to complete as it will write a summary report for all 400 train stations.\r\n\r\n```python\r\n\r\nfurther_train_info_2= further_train_info_1.with_column('OBJECT',object_construct(lit('CRS Code'),\r\ncol('\"CRS Code\"'),\r\nlit('Full Timetable Calls'),\r\ncol('\"Dec21 Weekday Full Timetable Daily Calls\"').astype(IntegerType()),\r\nlit('Emergency Timetable Calls'),\r\ncol('\"Dec21 Weekday Emergency Timetable Daily Calls\"').astype(IntegerType()),\r\nlit('Footfall'),\r\ncol( '\"ORR Station Footfall 2020-21\"').astype(IntegerType()),\r\nlit('Parking'),\r\ncol('\"Car Parking - Free/Chargeable\"'),\r\nlit('MP'),\r\ncol(\"MP\"),\r\nlit(\"Political Party\"),\r\ncol('\"Political Party\"'),\r\nlit('MP Email Address'),\r\ncol('\"MP Email Address\"'),                                                                             \r\nlit('Car Parking Spaces'),\r\ncol('\"Car Parking Spaces\"').astype(IntegerType()),\r\nlit('Staffed?'),\r\ncol('\"Staffed?\"'))).cache_result()\r\n\r\nprompt = 'In less than 200 words, write a summary based on the following train station details.  \r\nThe trainstations are based in the North of England. \r\nOnly include Northern train station names in the description.'\r\nprompt2 = 'write in the best way for it to describe a point on a map.'\r\n\r\nfurther_train_info_2 = further_train_info_2.select('\"CRS Code\"',\r\n        'MP',\r\n        '\"Political Party\"', \r\n        '\"MP Email Address\"',\r\n        call_function('snowflake.cortex.complete','mistral-large2',\r\n            concat(lit(prompt),\r\n            col('OBJECT').astype(StringType()),\r\n            lit('prompt2'))).alias('ALTERNATE'))\r\n\r\nfurther_train_info_2.write.mode('overwrite').save_as_table(\"DATA.TRAIN_STATION_INFORMATION\")\r\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\r\nstation_info.limit(5)\r\n\r\n```\r\n\r\nWhile we wait for the train station tooltips to materialize add a **markdown cell** above the cell we have just created. Markdown is useful to help tell a data story within the notebook. Hover above the newly created cell which is currently running and press  **Markdown**.  Name the cell **cortex_description** and paste the following narrative:\r\n\r\n```markdown\r\n\r\n\r\nBelow we are leveraging Mistral-large2 to produce meaningful tooltips relating to over **400** train stations which are managed by **Northern Trains**\r\n\r\n```\r\nPress the tick on the top right hand side of the cell to confirm the edit.\r\n\r\n![train_details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I026.jpg)\r\n\r\nWe used **call_function** to call Snowflake Cortex complete which returns a response that completes an input prompt. Snowflake Cortex runs LLMs that are fully hosted and managed by Snowflake, requiring no setup. In this example, we are using  Mistral-Large2, an open enterprise-grade LLM model fully managed by Snowflake.\r\n\r\nYou should get a new table that will look like this:\r\n\r\n![train_details](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/IO192.jpg)\r\n\r\n\u003E **IMPORTANT** Comment out the write to table command to prevent the LLM being called every time you refresh the notebook.  We now have the data saved in a table so you do not need to run this model again.\r\n\r\n```python\r\n\r\n# further_train_info_2.write.mode('overwrite').\r\n# save_as_table(\"DATA.TRAIN_STATION_INFORMATION\")\r\n\r\n```\r\n\r\nOk, let's now revise the map.  Create a new python cell and name it **visualise_tooltip** Add the following code:\r\n\r\n```python\r\ntrains_latlon_renamed = trains_latlon\r\n\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"CrsCode\"','NAME')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Latitude\"','LAT')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Longitude\"','LON')\r\n\r\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\r\n\r\ntrains_latlon_renamed = trains_latlon_renamed.join(station_info,station_info['\"CRS Code\"']==trains_latlon_renamed['NAME']).drop('\"CRS Code\"')\r\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\r\n\r\nnw_trains_l = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=trains_latlon_renamed_pd,\r\n            get_position='[LON, LAT]',\r\n            get_color='[0,187,2]',\r\n            get_radius=600,\r\n            pickable=True)\r\n\r\n#### render the map showing trainstations based on overture maps\r\n\r\ntooltip = {\r\n   \"html\": \"\"\"\u003Cb\u003EName:\u003C/b\u003E {NAME} \u003Cbr\u003E \u003Cb\u003EAlternate:\u003C/b\u003E {ALTERNATE}\"\"\",\r\n   \"style\": {\r\n       \"width\":\"50%\",\r\n        \"backgroundColor\": \"steelblue\",\r\n        \"color\": \"white\",\r\n       \"text-wrap\": \"balance\"\r\n   }\r\n}\r\n    \r\nst.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=700\r\n        ),\r\n    \r\nlayers= [polygon_layer, poi_l, nw_trains_l], tooltip = tooltip\r\n\r\n))\r\n```\r\n\r\nHover over the map and checked the updated tool tips.\r\n\r\n![tool_tip_image](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I0202.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Use Cortex to list Key location events\r\n\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow4.png)\r\nAny location may be impacted by key events.  Let's try and pinpoint out any key event happening in the north of England and how restaurants and train stations may be impacted by this.  We do not have specific event data for this, so in this case, we will leverage Snowflake Cortex to suggest events that may impact this area.  \r\n\r\n\r\n\r\nCreate the following in a new cell named **cortex_events**.  This will generate event data and saves the results in a new table.  \r\n\r\n```python\r\njson1 = '''{\"DATE\":\"YYYY-MM-DD\", \"NAME\":\"event\",DESCRIPTION:\"describe what the event is\" \"CENTROID\":{\r\n  \"coordinates\": [\r\n    0.000000\u003C\u003C\u003Cthis needs to be longitude,\r\n    0.000000\u003C\u003C\u003C\u003Cthis needs to be latitude\r\n  ],\r\n  \"type\": \"Point\"\r\n},\"COLOR\":\"Random bright and unique color in RGB presented in an array\"}'''\r\n\r\n\r\nprompt = f''' Retrieve 6 events within different cities of the north of england and will happen in 2024.  do not include commentary or notes retrive this in the following json format {json1}  '''\r\nevents_1 = session.create_dataframe([{'prompt':prompt}])\r\n\r\nevents_1 = events_1.select(call_function('SNOWFLAKE.CORTEX.COMPLETE','mistral-large2',prompt).alias('EVENT_DATA'))\r\n\r\nevents_1 = events_1.with_column('EVENT_DATA',replace(col('EVENT_DATA'),'''```json''',''))\r\nevents_1 = events_1.with_column('EVENT_DATA',replace(col('EVENT_DATA'),'''```''',''))\r\n\r\nevents_1.write.mode('overwrite').save_as_table(\"DATA.EVENTS_IN_THE_NORTH\")\r\nsession.table('DATA.EVENTS_IN_THE_NORTH')\r\n```\r\n\r\nAgain, we will utilise the semi-structured support in Snowflake to flatten the retrieved json to transpose a data frame in a table format\r\n\r\nCopy and paste the code below into a new cell which you will rename as **events_normalised**:\r\n\r\n```python\r\n\r\nevents_2 = session.table('DATA.EVENTS_IN_THE_NORTH')\r\nevents_2 = events_2.join_table_function('flatten',parse_json('EVENT_DATA')).select('VALUE')\r\nevents_2=events_2.with_column('NAME',col('VALUE')['NAME'].astype(StringType()))\r\nevents_2=events_2.with_column('DESCRIPTION',col('VALUE')['DESCRIPTION'].astype(StringType()))\r\nevents_2=events_2.with_column('CENTROID',to_geography(col('VALUE')['CENTROID']))\r\nevents_2=events_2.with_column('COLOR',col('VALUE')['COLOR'])\r\nevents_2=events_2.with_column('DATE',col('VALUE')['DATE'].astype(DateType())).drop('VALUE')\r\nevents_2\r\n\r\n```\r\n\r\n### Leveraging H3\r\n\r\nWe now have a list of events in a new table.  We would like to utilise this to understand the restaurants and train stations which may be impacted. H3 indexes are a way to bucket multiple points into a standardised grid.  H3 buckets points into hexagons.   Every hexagon at each resolution has a unique index. This index can also be used to join with other datasets which have also been indexed to the same standardised grid. We will do this later in the lab.\r\n\r\n\r\n\r\nCreate a new cell which you will name **h3index**.  Add the H3 Index at resolution 5 to the dataframe\r\n\r\n```python\r\n\r\nevents_3=events_2.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('CENTROID'),lit(5)))\r\n\r\nevents_3\r\n\r\n```\r\nYou will see a new column called H3:\r\n\r\n![events_H3](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I021.png)\r\n\r\nWe will now add these events onto the map as another layer.  Add a new cell and name it **visualise_h3**.\r\n\r\n```python\r\n\r\nevents = events_3.with_column('R',col('COLOR')[0])\r\nevents = events.with_column('G',col('COLOR')[1])\r\nevents = events.with_column('B',col('COLOR')[2])\r\nevents = events.with_column_renamed('DESCRIPTION','ALTERNATE')\r\neventspd = events.group_by('H3','NAME','ALTERNATE','R','G','B').count().to_pandas()\r\n\r\nst.write(eventspd)\r\n\r\nh3_events = pdk.Layer(\r\n        \"H3HexagonLayer\",\r\n        eventspd,\r\n        pickable=True,\r\n        stroked=True,\r\n        filled=True,\r\n        extruded=False,\r\n        get_hexagon=\"H3\",\r\n        get_fill_color=[\"255-R\",\"255-G\",\"255-B\"],\r\n        line_width_min_pixels=2,\r\n        opacity=0.4)\r\n\r\n#### render the map showing trainstations based on overture maps\r\n\r\ntooltip = {\r\n   \"html\": \"\"\"\u003Cb\u003EName:\u003C/b\u003E {NAME} \u003Cbr\u003E \u003Cb\u003EAlternate:\u003C/b\u003E {ALTERNATE}\"\"\",\r\n   \"style\": {\r\n       \"width\":\"50%\",\r\n        \"backgroundColor\": \"steelblue\",\r\n        \"color\": \"white\",\r\n       \"text-wrap\": \"balance\"\r\n   }\r\n}\r\n\r\nst.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=600\r\n        ),\r\n    \r\nlayers= [polygon_layer, poi_l, h3_events,nw_trains_l, ], tooltip = tooltip\r\n\r\n))\r\n\r\n```\r\n![events_map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I022.png)\r\n\r\n\r\nSo we can see the train stations and restaurants that might be impacted by the events.  The events rendered may be different to what is shown in the screenshot.  Lets create a dataset that extracts only the impacted areas.\r\n\r\nJoin the Events data frame to The Train Stations Data frame.  Then, Join the Events Data frame to the Restaurants Data frame.\r\n\r\n\r\nYou may notice that there are  new  H3 columns for the restaurants and places of the same resolution as the events.  This naturally creates a key to join to.  There is also a new column which displays the distance away the restaurant is from the event.   This was created using a standard geospatial function.  Copy and paste the following into a new cell called **affected_by_events**\r\n\r\n```python\r\n\r\ntrains_h3 = trains_latlon_renamed.with_column('H3',call_function('H3_LATLNG_TO_CELL_STRING',col('LAT'),col('LON'),lit(5)))\r\ntrains_h3 = trains_h3.join(events.select('H3',col('NAME').alias('EVENT_NAME'),'DATE'),'H3')\r\n\r\nst.markdown('#### Affected Train Stations')\r\nst.write(trains_h3.limit(1))\r\nplaces_h3 = places_3.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('GEOMETRY'),lit(5)))\r\nplaces_h3 = places_h3.join(events.select('H3','CENTROID',col('NAME').alias('EVENT_NAME'),'DATE'),'H3')\r\nplaces_h3 = places_h3.with_column('DISTANCE_FROM_EVENT',call_function('ST_DISTANCE',col('CENTROID'),col('GEOMETRY')))\r\nplaces_h3 = places_h3.filter(col('DISTANCE_FROM_EVENT')\u003C 3000)\r\nplaces_h3 = places_h3.sort(col('DISTANCE_FROM_EVENT').asc())\r\nst.markdown('#### Affected Restaurants')                             \r\nst.write(places_h3.limit(10))\r\n\r\n```\r\n\r\n![events_map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I023.png)\r\n\r\nWe now have all of this joined together - in the next step we will use an LLM to write a letter to each MP which details the concerns which may impact the events.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Use Cortex to write relevant correspondence\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow6.png)\r\n\r\nNow that we can see where the events impact stations and restaurants, let's  use an LLM to craft a letter to the MP to notify them of these effects.  To do this, we need to put all the information needed into objects to  easily pass them through the cortex function.\r\n\r\nCreate an object which links all affected restaurants to the respective MP.  We are also including the distance from the event for each restaurant.  \r\n\r\nCreate a new cell celled **letter_restaurants** and copy and paste the following code into the cell:\r\n\r\n```python\r\n\r\nobject3 = trains_h3.select('H3','MP','\"MP Email Address\"').distinct()\r\nobject3 = places_h3.join(object3,'H3')  \r\nobject3 = object3.group_by('MP','\"MP Email Address\"').agg(array_agg(object_construct(lit('NAME'),\r\n                                                                col('NAME'),\r\n                                                                lit('DISTANCE_FROM_EVENT'),\r\n                                                                round('DISTANCE_FROM_EVENT',5).astype(DecimalType(20,4)),\r\n                                                                lit('PHONE'),\r\n                                                                col('PHONE'),\r\n                                                               lit('WEBSITE'),\r\n                                                               col('WEBSITE'))).within_group('MP').alias('RESTAURANTS'))\r\nobject3\r\n\r\n\r\n```\r\n\r\nYou will now create another object which links all the affected trains stations to the respective MP.  Copy and paste the following code into a new python cell called **letter_trains**\r\n\r\n```python\r\n\r\nobject1 = trains_h3.group_by('MP').agg(array_agg(object_construct(lit('Train Station information'),col('ALTERNATE'))).within_group('MP').alias('TRAIN_STATIONS'))\r\nobject1\r\n\r\n```\r\nCreate an object which links all affected events to the respective MP.  Create a new python cell called **letter_events** and paste the following code into it:\r\n\r\n```python\r\n\r\nobject2 = trains_h3.select('MP','EVENT_NAME','DATE').distinct()\r\nobject2 = object2.group_by('MP').agg(array_agg(object_construct(lit('EVENT'),col('EVENT_NAME'),lit('DATE'),col('DATE'))).within_group('MP').alias('EVENTS'))\r\nobject2\r\n\r\n```\r\n\r\nyou will now join all these objects together and persist the results in a table.  Create a new cell called **letter_object** and copy and paste the python below into it: \r\n\r\n```python\r\n\r\nall_3 = object1.join(object2,'MP')\r\nall_3 = all_3.join(object3,'MP')\r\n\r\nall_3.write.mode('overwrite').save_as_table(\"DATA.EVENTS_AND_WHAT_IS_AFFECTED\")\r\nsession.table('DATA.EVENTS_AND_WHAT_IS_AFFECTED')\r\n```\r\n\r\n![events_map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I024.png)\r\n\r\nThe results can include a large number of restaurants by MP, so let's only  refer to the first 8 restaurants for each MP letter based on distance from the event.  The array_slice method does just that.  Create a new Python cell called **filt_restaurant_obj** and paste the following python content into it:\r\n\r\n```python\r\nall_4 = session.table(\"DATA.EVENTS_AND_WHAT_IS_AFFECTED\")\r\nall_4 = all_4.select('MP','\"MP Email Address\"','TRAIN_STATIONS','EVENTS',\r\n                     \r\narray_slice(col('RESTAURANTS'),lit(0),lit(8)).alias('RESTAURANTS'))\r\n\r\n          \r\nall_4\r\n\r\n```\r\n\r\nCreate a prompt for the LLM which pulls all this information together.  You may want to change who the letter is written to.  The prompt will encourage the letter to be written by Becky.\r\n\r\n\r\n\r\nCopy the code below into a new python cell called **prompt_letter**\r\n\r\n```python\r\n\r\ncol1,col2,col3, col4 = st.columns(4)\r\n\r\nwith col1:\r\n    name = st.text_input('Name:','''Becky O'Connor''')\r\nwith col2:\r\n    email = st.text_input('Email:','becky.oconnor@snowflake.com')\r\nwith col3:\r\n    title = st.text_input('Title:','a concerned Citizen')\r\nwith col4:\r\n    style = st.text_input('Style:','a worried resident')\r\n\r\n\r\nprompt = concat(lit('Write an email addressed to this MP:'),\r\n                lit('in the style of '),\r\n                lit(style),\r\n                col('MP'),\r\n               lit('about these events: '),\r\n               col('EVENTS').astype(StringType()),\r\n               lit('effecting these stations: '),\r\n               col('TRAIN_STATIONS').astype(StringType()),\r\n                lit('And these Restaurants: '),\r\n                col('RESTAURANTS').astype(StringType()),\r\n               lit(f'''The letter is written by {name} - {email} - {title}'''))\r\n\r\nst.info(f'''Letters will be generated from {name} - {email} - {title} in the style of {style}''')\r\n```\r\n\r\nOnce you run the cell, change the prompts to reflect a sender of your choice.\r\n\r\nCall the LLM with the prompt by copying the code below into a new cell. You may want to be creative and change who the letter is written by, or even ask Cortex to write it in the style of someone. The LLM we are using for this is Mixtral-8x7b as its good at writing letters.  Copy and paste the following in a new python cell called **cortex_letter**\r\n\r\n```python\r\n\r\nletters = all_4.select('MP','\"MP Email Address\"', call_function('SNOWFLAKE.CORTEX.COMPLETE','mixtral-8x7b',prompt).alias('LETTER'))\r\nletters.write.mode('overwrite').save_as_table(\"DATA.LETTERS_TO_MP\")\r\n\r\n```\r\n\r\nWe have now saved our letters.  The next task is to view the letters by copying this code into a new python cell called **new_letters**:\r\n\r\n```python\r\nletters = session.table('DATA.LETTERS_TO_MP')\r\nletters\r\n```\r\nThe code below  allows the user to browse the letters with a slider and visualise a letter.Try it out by copying the code into a new python cell called **letter_reader**.\r\n\r\n```python\r\n\r\nletterspd = letters.to_pandas()\r\nselected_letter = st.slider('Choose Letter:',0,letterspd.shape[0]-1,1)\r\nst.markdown(f''' **Email**: {letterspd['MP Email Address'].iloc[selected_letter]}''')\r\nst.write()\r\nst.write(letterspd.LETTER.iloc[selected_letter])\r\n\r\n```\r\n\r\n![events_map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/I025.png)\r\n\r\nThat's it. If you wish, you can download the completed python code from here by exporting it as .ipynb that could be used to import it into a new notebook. \r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Generate Synthetic Incidents\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow7.png)\r\n\r\nThis streamlit application will generate events occurring during the time of the events, which will involve the train stations and the restaurants.  \r\n\r\n![events_map](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st001.png)\r\n\r\nGo back to the home page and Navigate to Streamlit which is within the Projects Menu.\r\n\r\nAdd a new Streamlit App\r\n\r\n![create_streamlit](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st002.png)\r\n\r\nPopulate the prompts as suggested below.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st003.png)\r\n\r\n\r\n\r\n\r\n- Replace all the sample code with what is provided below:\r\n- [The code is also found here](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/sis001.py)\r\n\r\n```python\r\n\r\n# Import python packages\r\nimport streamlit as st\r\nfrom snowflake.snowpark.context import get_active_session\r\nfrom snowflake.snowpark.functions import col, call_function, lit,concat, parse_json,object_construct,replace\r\nfrom snowflake.snowpark.types import StringType, FloatType, ArrayType, VariantType, DateType\r\n\r\n\r\n\r\n# Write directly to the app\r\n\r\n\r\n# Get the current credentials\r\nsession = get_active_session()\r\n\r\nst.markdown(\r\n    \"\"\"\r\n    \u003Cstyle\u003E\r\n    .heading{\r\n        background-color: rgb(41, 181, 232);  /* light blue background */\r\n        color: white;  /* white text */\r\n        padding: 60px;  /* add padding around the content */\r\n    }\r\n    .tabheading{\r\n        background-color: rgb(41, 181, 232);  /* light blue background */\r\n        color: white;  /* white text */\r\n        padding: 10px;  /* add padding around the content */\r\n    }\r\n    .veh1 {\r\n        color: rgb(125, 68, 207);  /* purple */\r\n    }\r\n    .veh2 {\r\n        color: rgb(212, 91, 144);  /* pink */\r\n    }\r\n    .veh3 {\r\n        color: rgb(255, 159, 54);  /* orange */\r\n    }\r\n    .veh4 {\r\n        padding: 10px;  /* add padding around the content */\r\n        color: rgb(0,53,69);  /* midnight */\r\n    }\r\n    \r\n    body {\r\n        color: rgb(0,53,69);\r\n    }\r\n    \r\n    div[role=\"tablist\"] \u003E div[aria-selected=\"true\"] {\r\n        background-color: rgb(41, 181, 232);\r\n        color: rgb(0,53,69);  /* Change the text color if needed */\r\n    }\r\n    \r\n    \u003C/style\u003E\r\n    \"\"\",\r\n    unsafe_allow_html=True\r\n)\r\n\r\ncol1,col2 = st.columns([0.2,0.8])\r\n\r\n\r\nst.markdown('\u003Ch1 class=\"heading\"\u003EEVENT SIMULATOR\u003C/h2\u003E', unsafe_allow_html=True)\r\nst.write(\r\n    \"\"\"\u003CBR\u003EHere are all the events and what places are potentially affected again.\r\n    \"\"\",unsafe_allow_html=True\r\n)\r\n\r\n\r\n####Use a higher order filter to filter each array to only show restaurants that are no more than 250m from the event and include this in the LLM\r\n\r\nevents_what_affected = session.sql('SELECT MP, TRAIN_STATIONS,EVENTS,CASE WHEN ARRAY_SIZE(RESTAURANTS) \u003E10 THEN FILTER(RESTAURANTS,i -\u003E i:DISTANCE_FROM_EVENT \u003C=250) ELSE RESTAURANTS END RESTAURANTS FROM DATA.EVENTS_AND_WHAT_IS_AFFECTED')\r\nmps = events_what_affected.select('MP').to_pandas()\r\n\r\nwith st.expander(\"View Prompt Information\"):\r\n    st.dataframe(events_what_affected,column_config={\r\n                                    'MP':st.column_config.ListColumn('MP',\r\n                                     help='The current acting MP responsible for the area',\r\n                                     width='medium')   \r\n                                        }\r\n              )\r\n\r\nst.markdown('\u003Ch4 class=\"veh1\"\u003EGENERATE SYNTHETIC EVENTS\u003C/h2\u003E', unsafe_allow_html=True)\r\nst.markdown(\r\n    \"\"\" \r\n    Create synthetic events based on a mixture of Activity types. \r\n    The options are Overcrowding, Food Poisoning, Train Incidents and Fights. \u003CBR\u003E\u003CBR\u003E\r\n    Once complete scrolL down to indidual incidents and wait for social media data to generate\r\n\r\n    \"\"\",unsafe_allow_html=True)\r\n\r\n\r\n\r\n    \r\nst.image('https://cdn.prgloo.com/media/aad47116f9cc4c33b6f02e51fb8070f4.jpg?width=1200&height=400')\r\n\r\n\r\n\r\njson_template = {\"DATE\":\"01/01/2024\",\r\n                 \"Location Details\":\"CRS Code of train station or Restaurant name and address\",\r\n                 \"LOCATION\":{\"LAT\":0,\"LON\":0},\r\n                 \"REPORTED_BY\":\"BECKY O'CONNOR\",\"DESCRIPTION_OF_INCIDENTS\":\"generate unique ficticious incident details here\"}\r\n\r\n#st.write(json_template)\r\n\r\nwith st.form('Generate Events'):\r\n    'Generate Synthetic Events based on the following:'\r\n    col1,col2, col3,col4 = st.columns(4)\r\n    with col1:\r\n        model = st.selectbox('Choose Model',['mistral-large2'])\r\n    with col2:\r\n        mp = st.selectbox('Choose MP: ',mps)\r\n    with col3:\r\n        activity = st.selectbox('Choose Activity: ', ['Overcrowding','Food Poisoning','Train Incident','Fight'])\r\n    with col4:\r\n        event_count = st.number_input('How many events:',1,10,5)\r\n    \r\n    submitted = st.form_submit_button('Generate Reports')\r\n\r\n\r\nif submitted:\r\n    filtered_data = events_what_affected.filter(col('MP')==lit(mp))\r\n\r\n    st.markdown('Filtered Objects')\r\n\r\n    st.dataframe(filtered_data)\r\n\r\n    filtered_data_pd = filtered_data.to_pandas()\r\n\r\n    prompt = concat(lit('create'),\r\n                    lit(event_count),\r\n                    lit('synthetic incidents using this json template'),\r\n                    lit(json_template).astype(StringType()), \r\n                    lit('involving'), \r\n                    lit(activity), \r\n                    lit('concerning one of these train stations:'), \r\n                    col('TRAIN_STATIONS').astype(StringType()), \r\n                    lit('populate the incident date as the same date as one of these events'),\r\n                    col('EVENTS').astype(StringType()),\r\n                    lit('.Each incident will have connection with the provided event, and will also involve'),\r\n                    lit('one of the following restaurants:'), \r\n                    col('RESTAURANTS').astype(StringType()),\r\n                    lit('.  Each Incident will be Reported By a synthetic and randomly generated full name'),\r\n                    lit('populate the latitude and longitude as one json element using the provided json template'),\r\n                   lit('Nest all the generated incidents in an single json object called incidents.  Do not include Note'), \r\n                    lit('RETURN ONLY THE JSON'))\r\n\r\n    mistral = call_function('snowflake.cortex.complete',(lit(model),prompt))\r\n\r\n\r\n    \r\n\r\n    generated = filtered_data.with_column('generated_events',mistral)\r\n\r\n    generated = generated.with_column('generated_events',replace(col('generated_events'),'''```json''',lit('')))\r\n    generated = generated.with_column('generated_events',replace(col('generated_events'),'''```''',''))\r\n    #st.write(generated)\r\n    generated = generated.select('MP',parse_json('GENERATED_EVENTS').alias('GENERATED_EVENTS'))\r\n    generated = generated.with_column('INCIDENT_TYPE',lit(activity))\r\n    #st.write(generated)\r\n\r\n    sql2 = '''create table if not exists DATA.INCIDENTS (MP VARCHAR(255),\r\n            GENERATED_EVENTS VARIANT,\r\n         INCIDENT_TYPE VARCHAR(255))'''\r\n    \r\n    session.sql(sql2).collect()\r\n    generated.write.mode('append').save_as_table(\"DATA.INCIDENTS\")\r\n\r\n    \r\n\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003ENEW EVENTS\u003C/h2\u003E', unsafe_allow_html=True)\r\n\r\n    st.dataframe(generated)\r\n\r\nst.markdown('\u003Ch4 class=\"veh1\"\u003EGENERATED EVENTS\u003C/h2\u003E', unsafe_allow_html=True)\r\n\r\n\r\ntry:\r\n    \r\n    incident_table = session.table('DATA.INCIDENTS')\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003EALL GENERATED EVENTS\u003C/h2\u003E', unsafe_allow_html=True)\r\n    \r\n    sql = 'DROP TABLE DATA.INCIDENTS'\r\n\r\n\r\n\r\n    \r\n    clear = st.button('clear incident table')\r\n    \r\n    if clear:\r\n        session.sql(sql).collect()\r\n    \r\n    #st.dataframe(incident_table)\r\n\r\n    flatten = incident_table.select('MP','INCIDENT_TYPE',parse_json('GENERATED_EVENTS').alias('JSON'))\r\n    #st.write(flatten)\r\n    flatten = flatten.join_table_function('FLATTEN',col('JSON')['incidents'])\r\n    flatten = flatten.select('MP','INCIDENT_TYPE','VALUE')\r\n    \r\n    flatten = flatten.with_column('DESCRIPTION_OF_INCIDENTS',\r\n                                  col('VALUE')['DESCRIPTION_OF_INCIDENTS'].astype(StringType()),\r\n                                 )\r\n\r\n    flatten = flatten.with_column('LAT',\r\n                                  col('VALUE')['LOCATION']['LAT'].astype(FloatType()))\r\n                                 \r\n    flatten = flatten.with_column('LON',\r\n                                  col('VALUE')['LOCATION']['LON'].astype(FloatType()))\r\n                                 \r\n\r\n    flatten = flatten.with_column('REPORTED_BY',\r\n                                  col('VALUE')['REPORTED_BY'].astype(StringType()),\r\n                                 )\r\n\r\n    flatten = flatten.with_column('DATE',\r\n                                  col('VALUE')['DATE'].astype(StringType()),\r\n                                 ).drop('VALUE')\r\n    \r\n    \r\n    \r\n    st.write(flatten)\r\n    \r\n    \r\n    \r\n\r\n\r\n    st.divider()\r\n\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003EINDIVIDUAL INCIDENTS\u003C/h2\u003E', unsafe_allow_html=True)\r\n    MP = st.selectbox('Choose MP:',flatten.select('MP').distinct())\r\n    flatten = flatten.filter(col('MP')==MP)\r\n    \r\n    map = flatten.select('LAT','LON')\r\n    st.map(map)\r\n    flattenpd = flatten.to_pandas()\r\n    count = flattenpd.shape[0]\r\n    record = st.slider('Choose Incident Record:',0,count-1,count-1)\r\n    \r\n    st.markdown('###### INCIDENT TYPE')\r\n    st.write(flattenpd.INCIDENT_TYPE.iloc[record])\r\n    st.markdown('###### REPORTED BY')\r\n    st.write(flattenpd.REPORTED_BY.iloc[record])\r\n    st.markdown('###### DATE OF INCIDENT')\r\n    st.write(flattenpd.DATE.iloc[record])\r\n    st.markdown('###### DESCRIPTION OF INCIDENT')\r\n    st.write(flattenpd.DESCRIPTION_OF_INCIDENTS.iloc[record])\r\n\r\n    st.divider()\r\n\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003ENEWLY GENERATED SOCIAL MEDIA\u003C/h2\u003E', unsafe_allow_html=True)\r\n\r\n    #### SOCIAL MEDIA DATA\r\n    \r\n    social_media = session.create_dataframe([0])\r\n    json = '''{\"date\",\"YYYY-MM-DD\",\"post\",\"abcdefg\",\"sentiment_score\",0.2,\"username\",\"bob\"}'''\r\n    \r\n    social_media = social_media.with_column('V',call_function('SNOWFLAKE.CORTEX.COMPLETE',model, \r\n    concat(lit('generate 4 random synthetic social media post concerning the follwing incident:'), \r\n    lit(f'''{flattenpd.DESCRIPTION_OF_INCIDENTS.iloc[record]}'''), \r\n    lit('Add a date, username and relevant emojis to each post.\r\n    Include emotion.  Return the results as a json object and include sentiment scores.')\r\n           ,lit('use the following json template to structure the data'),lit(json))).astype(VariantType()))\r\n    \r\n\r\n    social_media = social_media.with_column('V',replace(col('V'),'''```json''',lit('')))\r\n    social_media = social_media.with_column('V',replace(col('V'),'''```''',''))\r\n    \r\n    smedia = social_media.join_table_function('flatten',parse_json('V')).select('VALUE')\r\n    smedia = smedia.select(object_construct(lit('INCIDENT_TYPE'),lit(flattenpd.INCIDENT_TYPE.iloc[record]),lit('MP'),lit(MP),lit('DATA'),col('VALUE')).alias('V'))\r\n    smedia.write.mode('append').save_as_table('DATA.SOCIAL_MEDIA')\r\n    smedia = smedia.with_column('\"Date\"',col('V')['DATA']['date'].astype(DateType()))\r\n    smedia = smedia.with_column('\"Post\"',col('V')['DATA']['post'].astype(StringType()))\r\n    smedia = smedia.with_column('\"Sentiment\"',col('V')['DATA']['sentiment_score'])\r\n    smedia = smedia.with_column('\"Username\"',col('V')['DATA']['username'].astype(StringType()))\r\n    smedia = smedia.with_column('\"Incident Type\"',col('V')['INCIDENT_TYPE'].astype(StringType()))\r\n    smedia = smedia.with_column('\"MP\"',col('V')['MP'].astype(StringType()))\r\n    st.dataframe(smedia)\r\n\r\n    st.divider()\r\nexcept:\r\n    st.info('No Results Found')\r\n    \r\n\r\n    \r\n\r\n\r\ntry:\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003EALL SOCIAL MEDIAL POSTINGS\u003C/h2\u003E', unsafe_allow_html=True)\r\n    smediaV = session.table('DATA.SOCIAL_MEDIA')\r\n    smediaV = smediaV.with_column('\"Date\"',col('V')['DATA']['date'].astype(DateType()))\r\n    smediaV = smediaV.with_column('\"Post\"',col('V')['DATA']['post'].astype(StringType()))\r\n    smediaV = smediaV.with_column('\"Sentiment\"',col('V')['DATA']['sentiment_score'])\r\n    smediaV = smediaV.with_column('\"Username\"',col('V')['DATA']['username'].astype(StringType()))\r\n    smediaV = smediaV.with_column('\"Incident Type\"',col('V')['INCIDENT_TYPE'].astype(StringType()))\r\n    smediaV = smediaV.with_column('\"MP\"',col('V')['MP'].astype(StringType()))\r\n    smediaV.create_or_replace_view('DATA.V_SOCIAL_MEDIA')\r\n    st.write(session.table('DATA.V_SOCIAL_MEDIA'))\r\nexcept:\r\n    st.info('No Results Found')\r\n\r\nst.markdown('\u003Ch4 class=\"veh1\"\u003EWHATS NEXT\u003C/h2\u003E', unsafe_allow_html=True)\r\n\r\nst.markdown(\r\n    \"\"\"\r\n    Go to the **ANALYSE_GEO_WITH_CORTEX** notebook to see how Cortex Search can make sense of this generated Unstructured Text\r\n\r\n    \"\"\",unsafe_allow_html=True)\r\n    \r\n         \r\n\r\n\r\n```\r\n### Running the App to generate data\r\n\r\nWithin the App, generate events for various mp's.   You can decide how many you would like to generate.  Once  you have selected what you would like to generate, press Generate Reports.  This will create Incidents relating to the MP.  They will also be in the context of the specified activity.\r\n\r\nOnce you have generated events, select each MP under the Individual incidents section.  This will generate synthetic social media posts for the incidents.\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st004.png)\r\n\r\nFinally we will create a Streamlit app which adds this additional information to a map.\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Visualise the data \r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow9.png)\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/streamlit1/st005.png)\r\n\r\nYou will now be creating another streamlit app in order to visualise the results.\r\n\r\nBelow is sample code which takes what we have learnt to create a streamlit with all the places event, location and incident data that from the shared datasets as well as synthetic data.\r\n\r\nYou will need to install **pydeck**.  \r\n\r\n- Go back to the home page and add a new streamlit application using the same database, schema and warehouse as the previous app.\r\n\r\n\r\n- Go to packages and add **pydeck** to the streamlit app before you paste the app code.\r\n\r\n    ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/packages_option.png)\r\n\r\n- After you have installed pydeck, delete the default contents of the newly created app\r\n- Replace all the sample code with what is provided below:\r\n- [The code is also found here](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/sis002.py)\r\n\r\n```python\r\n\r\nimport streamlit as st\r\nfrom snowflake.snowpark.context import get_active_session\r\nfrom snowflake.snowpark.functions import max,min,avg,call_function, split,substr,hour,concat,col,sqrt,lit,array_slice,array_agg,object_construct,parse_json, to_geography, to_array,to_date,round\r\nfrom snowflake.snowpark.types import StringType,VariantType, DateType,FloatType, IntegerType,DecimalType\r\nimport json\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pydeck as pdk\r\nst.set_page_config(layout=\"wide\")\r\n# Write directly to the app\r\nst.markdown('\u003Ch1 class=\"heading\"\u003EVISUALISE LOCATION DATA\u003C/h2\u003E', unsafe_allow_html=True)\r\nst.write(\r\n    \"\"\"\u003CBR\u003E This app shows key insight of places and events that may effect Northern Trains).\r\n    \"\"\",unsafe_allow_html=True)\r\n\r\n\r\n# Get the current credentials\r\nsession = get_active_session()\r\n\r\nst.markdown(\r\n    \"\"\"\r\n    \u003Cstyle\u003E\r\n    .heading{\r\n        background-color: rgb(41, 181, 232);  /* light blue background */\r\n        color: white;  /* white text */\r\n        padding: 60px;  /* add padding around the content */\r\n    }\r\n    .tabheading{\r\n        background-color: rgb(41, 181, 232);  /* light blue background */\r\n        color: white;  /* white text */\r\n        padding: 10px;  /* add padding around the content */\r\n    }\r\n    .veh1 {\r\n        color: rgb(125, 68, 207);  /* purple */\r\n    }\r\n    .veh2 {\r\n        color: rgb(212, 91, 144);  /* pink */\r\n    }\r\n    .veh3 {\r\n        color: rgb(255, 159, 54);  /* orange */\r\n    }\r\n    .veh4 {\r\n        padding: 10px;  /* add padding around the content */\r\n        color: rgb(0,53,69);  /* midnight */\r\n    }\r\n    \r\n    body {\r\n        color: rgb(0,53,69);\r\n    }\r\n    \r\n    div[role=\"tablist\"] \u003E div[aria-selected=\"true\"] {\r\n        background-color: rgb(41, 181, 232);\r\n        color: rgb(0,53,69);  /* Change the text color if needed */\r\n    }\r\n    \r\n    \u003C/style\u003E\r\n    \"\"\",\r\n    unsafe_allow_html=True\r\n)\r\n\r\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING.\"StationLatLong\"')\r\n\r\n\r\n#create a point from the coordinates\r\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('\"Longitude\"'),col('\"Latitude\"')))\r\n\r\n#collect all the points into one row of data\r\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\r\n\r\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\r\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\r\n\r\n#find the centre point so the map will render from that location\r\n\r\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\r\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\r\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\r\n\r\n#create LON and LAT variables\r\n\r\ncentrepd = centre.select('LON','LAT').to_pandas()\r\nLON = centrepd.LON.iloc[0]\r\nLAT = centrepd.LAT.iloc[0]\r\n\r\n### transform the data in pandas so the pydeck visualisation tool can view it as a polygon\r\n\r\nenvelopepd = envelope.to_pandas()\r\nenvelopepd[\"coordinates\"] = envelopepd[\"BOUNDARY\"].apply(lambda row: json.loads(row)[\"coordinates\"][0])\r\n\r\nplaces = session.table('OVERTURE_MAPS__PLACES.CARTO.PLACE')\r\nplaces = places.filter(col('ADDRESSES')['list'][0]['element']['country'] =='GB')\r\n\r\nplaces = places.select(col('NAMES')['primary'].astype(StringType()).alias('NAME'),\r\n                        col('PHONES')['list'][0]['element'].astype(StringType()).alias('PHONE'),\r\n                      col('CATEGORIES')['primary'].astype(StringType()).alias('CATEGORY'),\r\n                        col('CATEGORIES')['alternate']['list'][0]['element'].astype(StringType()).alias('ALTERNATE'),\r\n                    col('websites')['list'][0]['element'].astype(StringType()).alias('WEBSITE'),\r\n                      col('GEOMETRY'))\r\n\r\nplaces = places.filter(col('CATEGORY') =='restaurant')\r\n\r\n\r\nplaces = places.join(envelope,call_function('ST_WITHIN',places['GEOMETRY'],envelope['boundary']))\r\nplaces = places.with_column('LON',call_function('ST_X',col('GEOMETRY')))\r\nplaces = places.with_column('LAT',call_function('ST_Y',col('GEOMETRY')))\r\nplacespd = places.to_pandas()\r\n\r\n\r\ntrains_latlon_renamed = trains_latlon\r\n\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"CrsCode\"','NAME')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Latitude\"','LAT')\r\ntrains_latlon_renamed = trains_latlon_renamed.with_column_renamed('\"Longitude\"','LON')\r\n\r\nstation_info = session.table('DATA.TRAIN_STATION_INFORMATION')\r\n\r\ntrains_latlon_renamed = trains_latlon_renamed.join(station_info,station_info['\"CRS Code\"']==trains_latlon_renamed['NAME']).drop('\"CRS Code\"')\r\ntrains_latlon_renamed_pd = trains_latlon_renamed.to_pandas()\r\n\r\nevents = session.table('DATA.EVENTS_IN_THE_NORTH')\r\nevents = events.join_table_function('flatten',parse_json('EVENT_DATA')).select('VALUE')\r\nevents=events.with_column('NAME',col('VALUE')['NAME'].astype(StringType()))\r\nevents=events.with_column('DESCRIPTION',col('VALUE')['DESCRIPTION'].astype(StringType()))\r\nevents=events.with_column('CENTROID',to_geography(col('VALUE')['CENTROID']))\r\nevents=events.with_column('COLOR',col('VALUE')['COLOR'])\r\nevents=events.with_column('DATE',col('VALUE')['DATE'].astype(DateType())).drop('VALUE')\r\nevents=events.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('CENTROID'),lit(5)))\r\n\r\nevents = events.with_column('R',col('COLOR')[0])\r\nevents = events.with_column('G',col('COLOR')[1])\r\nevents = events.with_column('B',col('COLOR')[2])\r\nevents = events.with_column_renamed('DESCRIPTION','ALTERNATE')\r\neventspd = events.group_by('H3','NAME','ALTERNATE','R','G','B').count().to_pandas()\r\n\r\nincident_table = session.table('DATA.INCIDENTS')\r\nflatten = incident_table.select('MP','INCIDENT_TYPE',parse_json('GENERATED_EVENTS').alias('JSON'))\r\nflatten = flatten.join_table_function('FLATTEN',col('JSON')['incidents'])\r\nflatten = flatten.select('MP',col('INCIDENT_TYPE').alias('NAME'),'VALUE')\r\nflatten = flatten.with_column('ALTERNATE',\r\n                                  col('VALUE')['DESCRIPTION_OF_INCIDENTS'].astype(StringType()),\r\n                                 )\r\nflatten = flatten.with_column('LAT',\r\n                                  col('VALUE')['LOCATION']['LAT'].astype(FloatType()))                           \r\nflatten = flatten.with_column('LON',\r\n                                  col('VALUE')['LOCATION']['LON'].astype(FloatType()))\r\nflatten = flatten.with_column('REPORTED_BY',\r\n                                  col('VALUE')['REPORTED_BY'].astype(StringType()),\r\n                                 )\r\nflatten = flatten.with_column('DATE',\r\n                                  col('VALUE')['DATE'].astype(StringType()),\r\n                                 ).drop('VALUE')\r\nflattenpd = flatten.to_pandas()\r\n####MAP LAYERS\r\n\r\npolygon_layer = pdk.Layer(\r\n            \"PolygonLayer\",\r\n            envelopepd,\r\n            opacity=0.3,\r\n            get_polygon=\"coordinates\",\r\n            filled=True,\r\n            get_fill_color=[16, 14, 40],\r\n            auto_highlight=True,\r\n            pickable=False,\r\n        )\r\n\r\n\r\npoi_l = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=placespd,\r\n            opacity=0.8,\r\n            get_position='[LON, LAT]',\r\n            get_color='[255,255,255]',\r\n            radius_min_pixels=3,\r\n            radius_max_pixels=6,\r\n            pickable=True)\r\n\r\n\r\nnw_trains_l = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=trains_latlon_renamed_pd,\r\n            get_position='[LON, LAT]',\r\n            get_color='[41,181,232]',\r\n            radius_min_pixels=3,\r\n            radius_max_pixels=20,\r\n            get_radius=4,\r\n            pickable=True)\r\n\r\nh3_events = pdk.Layer(\r\n        \"H3HexagonLayer\",\r\n        eventspd,\r\n        pickable=True,\r\n        stroked=True,\r\n        filled=True,\r\n        extruded=False,\r\n        get_hexagon=\"H3\",\r\n        get_fill_color=[\"255-R\",\"255-G\",\"255-B\"],\r\n        line_width_min_pixels=2,\r\n        opacity=0.3)\r\n\r\n\r\n\r\nincidents_layer = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=flattenpd,\r\n            get_position='[LON, LAT]',\r\n            get_color='[255,165,0]',\r\n            get_radius=2000,\r\n            pickable=True)\r\n\r\n#### render the map showing trainstations based on overture maps\r\n\r\ntooltip = {\r\n   \"html\": \"\"\"\u003Cb\u003EName:\u003C/b\u003E {NAME} \u003Cbr\u003E \u003Cb\u003EAlternate:\u003C/b\u003E {ALTERNATE}\"\"\",\r\n   \"style\": {\r\n       \"width\":\"50%\",\r\n        \"backgroundColor\": \"steelblue\",\r\n        \"color\": \"white\",\r\n       \"text-wrap\": \"balance\"\r\n    }\r\n    }\r\n    \r\nletters = session.table('DATA.LETTERS_TO_MP')\r\nmps = letters.select('MP').distinct()\r\nselected_mp = st.selectbox('Choose MP:',mps)\r\nletterspd = letters.filter(col('MP')==lit(selected_mp)).to_pandas()\r\n#st.write(letterspd.PROMPT.iloc[0])\r\n\r\nst.divider()\r\ncol1,col2 = st.columns([0.5,0.5])\r\n\r\nwith col1:\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003EMAP OF EVENTS WITH STATIONS AND RESTAURANTS\u003C/h2\u003E', unsafe_allow_html=True)\r\n    st.pydeck_chart(pdk.Deck(\r\n    map_style=None,\r\n    initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=7,\r\n        height=750\r\n        ),\r\n    \r\n    layers= [polygon_layer, poi_l,nw_trains_l,h3_events,incidents_layer], tooltip = tooltip\r\n\r\n    ))\r\n    st.caption('Hover for more info')\r\nwith col2:\r\n    st.markdown('\u003Ch4 class=\"veh1\"\u003ELETTERS TO MP\u003C/h2\u003E', unsafe_allow_html=True)\r\n    st.write(letterspd.LETTER.iloc[0])\r\n    st.divider()\r\n\r\nsocial_media = session.table('DATA.V_SOCIAL_MEDIA').filter(col('MP')==selected_mp)\r\n\r\nst.markdown('\u003Ch4 class=\"veh1\"\u003ESOCIAL MEDIA\u003C/h2\u003E', unsafe_allow_html=True)\r\nst.table(social_media.drop('V'))\r\n\r\n```\r\n\u003C!-- ------------------------ --\u003E\r\n## Use Cortex to Embed Generated Text and create a Search Service\r\n\r\n![assets](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow9.png)\r\n\r\nDuring the lab we have produced quite a bit of unstructured data from social media posts, to incidents, through to letters.  Now lets use vector embedding functionality to make this information searchable.  This is really useful when you would like to use an LLM to answer questions but do not want to send the entire dataset as a large object - which could be quite expensive and also would take a long time to run.  For large text blocks, you may wish to 'chunk' the data first.  As the text in this scenario is relatively small - we will keep it as is.  Once you have explored the principles of how text searching works, you will create a Cortex Search Service.\r\n\r\nClick here and download the notebook from GitHub\r\n\r\n[**Vector_Embeddings.ipynb**](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/Vector_Embeddings.ipynb)\r\n\r\nImport as a new snowflake notebook.  Add it to the BUILD.UK.NOTEBOOKS schema and follow the instructions provided in the notebook.\r\n\r\n\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Bringing in Met Office Weather\r\n### Met office Weather\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/flow10.png)\r\nNow we will leverage the weather data to understand the weather forecasts for each Event.  This time we will import a notebook which has already been created.\r\n\r\n- Go to the Home page and select Notebooks\r\n\r\n- Download the following python notebook file\r\n\r\n[**weather.ipynb**](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data/blob/main/weather.ipynb)\r\n\r\n- In the notebbook menu selection, select Import from .ipynb file and choose the downloaded file\r\n\r\n    ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W001.png)\r\n\r\n\r\n\r\n- Populate the fields with the following:\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W003.png)\r\n\r\n- Follow the instructions as per the imported notebook.  You will end up with a weather forecast for each of the different events.\r\n\r\n    ![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W002.png)\r\n\r\n\r\n### Streamlit showing weather by Train Station\r\n\r\n![alt text](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/weather/W004.png)\r\n\r\nthis is an example weather app which shows the weather forecast for each train station\r\n\r\n- Copy and paste the code below into a new **streamlit app**\r\n\r\n- NOTE: you will need to install the package **pydeck** from the packages menu.\r\n\r\n\r\n```python\r\n\r\n# Import python packages\r\nimport streamlit as st\r\nfrom snowflake.snowpark.context import get_active_session\r\nfrom snowflake.snowpark.functions import any_value,max,min,avg,call_function, split,substr,hour,concat,col,sqrt,lit,array_slice,array_agg,object_construct,parse_json, to_geography, to_array,to_date\r\nfrom snowflake.snowpark.types import StringType,VariantType, DateType, IntegerType,DecimalType\r\nimport json\r\nimport pandas as pd\r\nimport numpy as np\r\nimport pydeck as pdk\r\n\r\nst.set_page_config(layout=\"wide\")\r\n\r\n# Write directly to the app\r\n\r\ncol1,col2,col3 = st.columns([0.2,0.6,0.2])\r\nwith col1:\r\n    st.image('https://cdn.prgloo.com/web/NorthernRail/NorthernNewLogo.png')\r\n\r\nwith col3:\r\n    st.image('https://upload.wikimedia.org/wikipedia/en/thumb/f/f4/Met_Office.svg/1200px-Met_Office.svg.png')\r\nst.title(\"Northern Trains Weather Data\")\r\n\r\nst.write(\r\n    \"\"\"This app shows the weather that may affect Northern Trains).\r\n    \"\"\"\r\n)\r\n\r\n# Get the current credentials\r\nsession = get_active_session()\r\n\r\n#### add postcode sector to trains\r\ntrains_latlon = session.table('NORTHERN_TRAINS_STATION_DATA.TESTING.\"StationLatLong\"')\r\ntrains_latlon = trains_latlon.with_column('POSTCODE_SPLIT',split(col('\"Postcode\"'),lit(' ')))\r\ntrains_latlon = trains_latlon.with_column('Postcode_outcode',col('POSTCODE_SPLIT')[0].astype(StringType()))\r\ntrains_latlon = trains_latlon.with_column('Postcode_inward_code',col('POSTCODE_SPLIT')[1].astype(StringType()))\r\ntrains_latlon = trains_latlon.with_column('Postcode_Sector',concat('POSTCODE_OUTCODE',lit('_'),substr(col('Postcode_inward_code'),1,1)))\r\ntrains_latlon = trains_latlon.drop('POSTCODE_SPLIT','POSTCODE_OUTCODE','POSTCODE_INWARD_CODE')\r\n\r\n\r\n# Add hourly and daily weather for each train station\r\n\r\nweather_hourly = session.table('POSTCODE_SECTOR_WEATHER_FORECASTS.PCSECT_FORECAST.\"postcode_hourly_view\"')\r\nweather_daily = session.table('POSTCODE_SECTOR_WEATHER_FORECASTS.PCSECT_FORECAST.\"postcode_daily_view\"')\r\n\r\nweather_hourly_max = weather_hourly.agg(max('\"Issued_at\"').alias('MAX'))\r\nweather_hourly = weather_hourly.join(weather_hourly_max,weather_hourly_max['MAX']==weather_hourly['\"Issued_at\"']).drop('MAX')\r\n\r\nweather_daily_max = weather_daily.agg(max('\"Issued_at\"').alias('MAX'))\r\nweather_daily = weather_daily.join(weather_daily_max,weather_daily_max['MAX']==weather_daily['\"Issued_at\"']).drop('MAX')\r\n\r\nweather_hourly = weather_hourly.join(trains_latlon,trains_latlon['POSTCODE_SECTOR']==weather_hourly['PC_SECT']).drop('PC_SECT')\r\nweather_hourly = weather_hourly.with_column('VALIDITY_DATE',to_date('\"Validity_date_and_time\"'))\r\n\r\nweather_daily_t = weather_daily.join(trains_latlon,trains_latlon['POSTCODE_SECTOR']==weather_daily['PC_SECT']).drop('PC_SECT')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#add h3 column to weather\r\nweather_daily_h3 = weather_daily_t.with_column('H3',call_function('H3_LATLNG_TO_CELL_STRING',col('\"Latitude\"'),col('\"Longitude\"'),lit(5)))\r\n\r\n\r\n#add northern trains boundary box\r\n#create a point from the coordinates\r\nenvelope = trains_latlon.with_column('POINT',call_function('ST_MAKEPOINT',col('\"Longitude\"'),col('\"Latitude\"')))\r\n\r\n#collect all the points into one row of data\r\nenvelope = envelope.select(call_function('ST_COLLECT',col('POINT')).alias('POINTS'))\r\n\r\n#create a rectangular shape which boarders the minimum possible size which covers all of the points\r\nenvelope = envelope.select(call_function('ST_ENVELOPE',col('POINTS')).alias('BOUNDARY'))\r\n\r\n# Create a centre point to position the maps\r\ncentre = envelope.with_column('CENTROID',call_function('ST_CENTROID',col('BOUNDARY')))\r\ncentre = centre.with_column('LON',call_function('ST_X',col('CENTROID')))\r\ncentre = centre.with_column('LAT',call_function('ST_Y',col('CENTROID')))\r\n\r\n\r\n\r\n#create LON and LAT variables\r\n\r\ncentrepd = centre.select('LON','LAT').to_pandas()\r\nLON = centrepd.LON.iloc[0]\r\nLAT = centrepd.LAT.iloc[0]\r\n\r\n# index the boundary with h3 res 5\r\nboundary_h3 = envelope.with_column('H3',call_function('H3_COVERAGE_STRINGS',col('BOUNDARY'),lit(5))).join_table_function('flatten',col('H3')).select(col('VALUE').astype(StringType()).alias('H3'))\r\n\r\n\r\n#### use the postcodes data set to add H3 to postcode sectors\r\npostcodes = weather_daily.group_by('PC_SECT').agg(any_value('POINT').alias('POINT'))\r\npostcodes = postcodes.with_column('H3',call_function('H3_POINT_TO_CELL_STRING',col('\"POINT\"'),lit(5)))\r\npostcodes = postcodes.join(boundary_h3,boundary_h3['H3']==postcodes['H3'],lsuffix='L').drop('H3L')\r\n\r\n\r\n### join weather data to the boundary\r\nweather_boundary = boundary_h3.join(weather_daily_h3,weather_daily_h3['H3']==boundary_h3['H3'],lsuffix='L',type='right').drop('H3L')\r\nweather_boundarypd = weather_boundary.select('H3','\"Max_temperature_day\"','\"Max_feels_like_temperature_day\"').to_pandas()\r\n\r\n\r\nfrom snowflake.snowpark.functions import dateadd\r\n\r\n\r\n##join weather to all postcode sectors\r\n\r\n\r\nweather_daily_north = weather_daily.join(weather_daily_max,weather_daily_max['MAX']==weather_daily['\"Issued_at\"']).drop('MAX')\r\nweather_daily_north = weather_daily_north.join(postcodes,postcodes['PC_SECT']==weather_daily_north['PC_SECT'])\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nstation_filter = trains_latlon.select('\"CrsCode\"')\r\ndate_filter = weather_hourly.agg(max('VALIDITY_DATE').alias('MAX'),\r\n                          min('VALIDITY_DATE').alias('MIN'))\r\n\r\ndate_filter = date_filter.with_column('MIN',dateadd('DAY',lit(1),col('MIN'))).to_pandas()\r\n\r\nwith st.form('select_data'):\r\n    col1,col2, = st.columns([0.3,0.7])\r\n    with col1:\r\n        selected_station = st.selectbox('Select Station:',station_filter)\r\n        selected_date = st.date_input('Select Date:',date_filter.MIN.iloc[0],date_filter.MIN.iloc[0],date_filter.MAX.iloc[0])\r\n        model = 'reka-flash','mixtral-8x7b', 'gemma-7b','llama2-70b-chat'\r\n        select_model = st.selectbox('Choose Model',model)\r\n        run = st.form_submit_button('Run Dashboard')\r\n    with col2:\r\n        st.image('https://cdn.prgloo.com/media/aad47116f9cc4c33b6f02e51fb8070f4.jpg?width=1200&height=400')\r\n\r\nif run:\r\n\r\n    #st.write(weather_daily_north).limit(10)\r\n    todaypd = weather_daily_t.filter((col('\"Validity_date\"')==selected_date)\r\n                                     & (col('\"CrsCode\"')==selected_station)).to_pandas()\r\n    st.write(todaypd)\r\n    melt = pd.melt(todaypd)\r\n    melt['variable'] = melt['variable'].astype(\"string\")\r\n    melt['value'] = melt['value'].astype(\"string\")\r\n\r\n    ##### create an object to feed into the LLM\r\n    object = session.create_dataframe(melt)\r\n    object = object.with_column('object',object_construct(col('\"variable\"'),col('\"value\"')))\r\n    \r\n    object = object.select(array_agg('OBJECT').alias('OBJECT'))\r\n\r\n    \r\n\r\n        ##### create an LLM prompt which includes the data object\r\n    prompt = concat(lit('Summarise the weather report in 500 words using paragraphs for the date specified which includes relevant emojis to summarise the weather based on the following dataset'),\r\n                    col('object').astype(StringType()),\r\n                   lit('USE APPROPIATE MARKDOWN TO ENHANCE THE PRESENTATION. NO COMMENTS'))\r\n    \r\n    # construct the cortex.complete function - this will run based on the chosen model\r\n    complete = call_function('snowflake.cortex.complete',lit(select_model),lit(prompt))\r\n    \r\n    object = object.select(complete)\r\n\r\n    #### print the results\r\n    st.write(object.collect()[0][0])\r\n\r\n    st.divider()\r\n    \r\n    # dataframes for h3 maps\r\n\r\n    weather_daily_north = weather_daily_north.select('H3',col('\"Validity_date\"').astype(StringType()).alias('DATE'),\r\n                                                     '\"Max_temperature_day\"',\r\n                                                     '\"Max_feels_like_temperature_day\"')\r\n    \r\n    weather_daily_north_0 = weather_daily_north.filter(col('\"Validity_date\"')== dateadd('day',lit(1),lit(selected_date)))\r\n    weather_daily_north_1 = weather_daily_north.filter(col('\"Validity_date\"')== dateadd('day',lit(2),lit(selected_date)))\r\n    weather_daily_north_2 = weather_daily_north.filter(col('\"Validity_date\"')== dateadd('day',lit(3),lit(selected_date)))\r\n\r\n\r\n    ####\r\n    weather_daily_northpd = weather_daily_north_0.to_pandas()\r\n    weather_daily_northpd1 = weather_daily_north_1.to_pandas()\r\n    weather_daily_northpd2 = weather_daily_north_2.to_pandas()\r\n\r\n    \r\n\r\n    #################CONSTRUCT THE MAP\r\n\r\n    ### create a layer to cover the boundary with H3 Indexes\r\n\r\n    st.markdown('#### TEMPERATURE COMPARISON OVER THE NEXT 3 DAYS')\r\n\r\n    filtered_station = trains_latlon.filter(col('\"CrsCode\"')==selected_station)\r\n    filtered_station_pd = filtered_station.to_pandas()\r\n\r\n    #st.write(filtered_station)\r\n\r\n\r\n\r\n    station = pdk.Layer(\r\n            'ScatterplotLayer',\r\n            data=filtered_station_pd,\r\n            get_position='[Longitude,Latitude]',\r\n            get_color='[0,0,0]',\r\n            get_radius=2000,\r\n            radius_scale=6,\r\n            radius_min_pixels=1,\r\n            opacity=0.6,\r\n            radius_max_pixels=1000,\r\n            filled=True,\r\n            line_width_min_pixels=1,\r\n            pickable=False)\r\n    \r\n\r\n    h3_coverage = pdk.Layer(\r\n        \"H3HexagonLayer\",\r\n        weather_daily_northpd,\r\n        pickable=True,\r\n        stroked=True,\r\n        filled=True,\r\n        extruded=False,\r\n        get_hexagon=\"H3\",\r\n        get_fill_color=[\"0+Max_temperature_day*8\",\"(255/100) * 0+Max_temperature_day*12\",\"(255/100) * 200-Max_temperature_day*9\"],\r\n        line_width_min_pixels=0,\r\n        opacity=0.9)\r\n\r\n    h3_coverage_1 = pdk.Layer(\r\n        \"H3HexagonLayer\",\r\n        weather_daily_northpd1,\r\n        pickable=True,\r\n        stroked=True,\r\n        filled=True,\r\n        extruded=False,\r\n        get_hexagon=\"H3\",\r\n        get_fill_color=[\"0+Max_temperature_day*8\",\"(255/100) * 0+Max_temperature_day*12\",\"(255/100) * 200-Max_temperature_day*9\"],\r\n        line_width_min_pixels=0,\r\n        opacity=0.9)\r\n\r\n    h3_coverage_2 = pdk.Layer(\r\n        \"H3HexagonLayer\",\r\n        weather_daily_northpd2,\r\n        pickable=True,\r\n        stroked=True,\r\n        filled=True,\r\n        extruded=False,\r\n        get_hexagon=\"H3\",\r\n        get_fill_color=[\"0+Max_temperature_day*8\",\"(255/100) * 0+Max_temperature_day*12\",\"(255/100) * 200-Max_temperature_day*9\"],\r\n        line_width_min_pixels=0,\r\n        opacity=0.9)\r\n\r\n\r\n\r\n    #### render the map \r\n\r\n\r\n    col1,col2,col3 = st.columns(3)\r\n\r\n    with col1:\r\n        st.write(weather_daily_northpd.DATE.iloc[1])\r\n        st.pydeck_chart(pdk.Deck(\r\n        map_style=None,\r\n        initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=400\r\n        ),\r\n    \r\n        layers= [h3_coverage,station],tooltip = {'text':\"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}\"}\r\n\r\n        ))\r\n\r\n    with col2:\r\n        st.write(weather_daily_northpd1.DATE.iloc[1])\r\n        st.pydeck_chart(pdk.Deck(\r\n        map_style=None,\r\n        initial_view_state=pdk.ViewState(\r\n            latitude=LAT,\r\n            longitude=LON,\r\n            zoom=5,\r\n            height=400\r\n            ),\r\n    \r\n        layers= [h3_coverage_1,station],tooltip = {'text':\"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}\"}\r\n\r\n        ))\r\n\r\n    with col3:\r\n        st.write(weather_daily_northpd2.DATE.iloc[1])\r\n        st.pydeck_chart(pdk.Deck(\r\n        map_style=None,\r\n        initial_view_state=pdk.ViewState(\r\n        latitude=LAT,\r\n        longitude=LON,\r\n        zoom=5,\r\n        height=400\r\n            ),\r\n    \r\n        layers= [h3_coverage_2,station],tooltip = {'text':\"Temperature: {Max_temperature_day}, Feels like: {Max_feels_like_temperature_day}\"}\r\n\r\n        ))\r\n\r\n\r\n\r\n    \r\n    weather_filter_hour = weather_hourly.filter(col('\"CrsCode\"') == selected_station)\r\n    weather_filter_day = weather_daily_t.filter(col('\"CrsCode\"') == selected_station)\r\n\r\n    weather_filter_hour = weather_filter_hour.filter(col('VALIDITY_DATE')==selected_date)\r\n    #weather_filter_day = weather_filter_day.filter(col('\"Validity_date\"')==selected_date)\r\n    weather_filter_hour = weather_filter_hour.with_column('HOUR',hour('\"Validity_date_and_time\"'))\r\n\r\n\r\n    weather_filter_hour = weather_filter_hour.group_by('HOUR').agg(avg('\"Feels_like_temperature\"').alias('Feels Like'),\r\n                                                              avg('\"Screen_temperature\"').alias('\"Screen\"'),\r\n                                                               avg('\"Screen_Dew_Point_Temperature\"').alias('\"Dew Point\"'),\r\n    avg('\"UV_index\"').alias(' '),                                                        avg('\"Relative_humidity\"').alias('\"Relative\"'),                                                        avg('\"Wind_speed\"').alias('\"Speed\"'),\r\n    avg('\"Wind_gust\"').alias('\"Gust\"'),).to_pandas()\r\n\r\n    #weather_filter_hour\r\n    st.markdown('#### 12 DAY DAILY FORECAST')\r\n\r\n    col1,col2 = st.columns(2)\r\n    with col1:\r\n        st.markdown('##### TEMPERATURE')\r\n    #weather_filter_day\r\n\r\n        st.line_chart(weather_filter_day.to_pandas(),x='Validity_date',y=['Max_temperature_day','Max_feels_like_temperature_day','Min_feels_like_temperature_night'])\r\n\r\n\r\n    with col2:\r\n        st.markdown('##### HUMIDITY')\r\n    #weather_filter_day\r\n\r\n        st.line_chart(weather_filter_day.to_pandas(),x='Validity_date',y=['Relative_humidity_Approx_Local_Midday','Relative_humidity_Approx_Local_Midnight'])\r\n\r\n    st.divider()\r\n\r\n    st.markdown('#### HOURLY VIEW')\r\n\r\n    col1,col2 = st.columns(2)\r\n\r\n    with col1:\r\n        st.markdown('##### TEMPERATURE')\r\n        st.line_chart(weather_filter_hour,x='HOUR',y=['Feels Like','Screen','Dew Point'])\r\n    with col2:\r\n        st.markdown('##### UV INDEX')\r\n        st.bar_chart(weather_filter_hour,x='HOUR',y=[' '])\r\n\r\n    with col1:\r\n        st.markdown('##### WIND')\r\n        st.line_chart(weather_filter_hour,x='HOUR',y=['Speed','Gust'])\r\n    with col2:\r\n        st.markdown('##### HUMIDITY')\r\n        st.bar_chart(weather_filter_hour,x='HOUR',y=['Relative'])\r\n\r\n\r\n```\r\n\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusion and Resources\r\n### Conclusion\r\n\r\nSnowflake provides powerful capabilities when you bring Cortex with Snowpark and Streamlit's visualization capabilities together.\r\n\r\n### What You Learned\r\n\r\nYou will have learned the following:\r\n - How to use Snowflake Cortex to produce descriptive tooltips, create letters, suggest large events as well as produce synthetic data for simulating potential incidents that may occur.\r\n\r\n- How to leverage snowpark data frames to wrangle shared datasets into meaningful dataframes\r\n\r\n- How to utilise Pydeck to combine local information on a multi layered map.\r\n\r\n\r\n### Related Resources\r\n\r\n#### Datasources used in the lab\r\n\r\n- [Overture Maps - Places](https://app.snowflake.com/marketplace/providers/GZT0ZKUCHE3/CARTO?dbName=OVERTURE_MAPS__PLACES)\r\n\r\n- [Northern Trains](https://app.snowflake.com/marketplace/listing/GZTDZ2CEXT/northern-trains-ltd-northern-trains-station-data?search=northern)\r\n\r\n- Met office **to follow**\r\n\r\n#### Source code\r\n\r\n- [Source Code on Github](https://github.com/Snowflake-Labs/sfguide-using-snowflake-cortex-and-streamlit-with-geospatial-data)\r\n\r\n#### Further Related Material\r\n\r\n- [Geospatial Functions](https://docs.snowflake.com/en/sql-reference/functions-geospatial)\r\n\r\n- [H3 Indexing](https://h3geo.org/docs/)\r\n\r\n- [Streamlit](https://streamlit.io/)\r\n\r\n- [Pydeck](https://deckgl.readthedocs.io/en/latest/index.html#)\r\n\r\n- [Getting started with Geospatial AI and ML using Snowflake Cortex](/en/developers/guides/geo-for-machine-learning/)\r\n\r\n- [Build a Retrieval Augmented Generation (RAG) based LLM assistant using Streamlit and Snowflake Cortex](https://quickstarts.snowflake.com/guide/asking_questions_to_your_own_documents_with_snowflake_cortex/index.html#0)","title":"Quickstart Article Body",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-146007851a","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":{"id":"container-e93fca11f5","layout":"SIMPLE",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-99f673057e","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-04-17",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-54e2600ef0","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"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-efc1a27de5","layout":"SIMPLE",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"id":"container-0640106903","layout":"SIMPLE",":items":{"quickstart_table_of_":{"id":"container-2487ccd7fe","layout":"SIMPLE","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-3b405522f7",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/using-snowflake-cortex-and-streamlit-with-geospatial-data","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003EInitial Setup\u003C/h2\u003E","\u003Ch2\u003ESnowflake Marketplace\u003C/h2\u003E","\u003Ch2\u003ECreate your First map Layer\u003C/h2\u003E","\u003Ch2\u003ECreate a Boundary for filtering purposes\u003C/h2\u003E","\u003Ch2\u003EFiltering the data using the boundary box\u003C/h2\u003E","\u003Ch2\u003EUse Cortex to describe the Train Stations\u003C/h2\u003E","\u003Ch2\u003EUse Cortex to list Key location events\u003C/h2\u003E","\u003Ch2\u003EUse Cortex to write relevant correspondence\u003C/h2\u003E","\u003Ch2\u003EGenerate Synthetic Incidents\u003C/h2\u003E","\u003Ch2\u003EVisualise the data\u003C/h2\u003E","\u003Ch2\u003EUse Cortex to Embed Generated Text and create a Search Service\u003C/h2\u003E","\u003Ch2\u003EBringing in Met Office Weather\u003C/h2\u003E","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-23cc483dba",":type":"snowflake-site/components/quickstart/quickstart-button","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/using-snowflake-cortex-and-streamlit-with-geospatial-data","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"],":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container"}},":itemsOrder":["quickstart_table_of_"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false},"markup_editor":{"id":"markup-editor-a9b4a6b6cc","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:1fr 0}.qs-disclaimer-text p \u003E span{font-size:15px !important}@media (min-width:768px){#quickstart-template-main-flexible-container{padding:24px 32px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:48px}}@media (max-width:767px){#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:0}}@media (min-width:1024px){#quickstart-template-main-flexible-container{padding:0 92px 48px 92px}#quickstart-template-main-flexible-container \u003E .snowflake-flexible-column-container-items{gap:117px}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["quickstart_hero","flexible_column_cont","markup_editor"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"id":"container-6027b4c94e","layout":"SIMPLE",":items":{},":itemsOrder":[],":type":"snowflake-site/components/modal/modal-container"},"experiencefragment-footer":{"id":"experiencefragment-ab75812731","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/jcr:content","configured":true,":items":{"root":{"additionalClasses":"sf-footer","id":"container-cf7d4aa2b4","layout":"SIMPLE",":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","id":"container-4cdfbb412f","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-7fbbac3473","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":{"id":"container-a004487454","layout":"SIMPLE",":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","id":"container-f5ed8ce258","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":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","id":"container-de4ced9519","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":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","id":"container-0bc820bafa","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-1df346ebf0","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-v2-7971a90d3b","marketoForm":{"edit":false,"formId":"45871","successUrl":null,"script":null,"values":null,"hidden":null},"munchkinId":"252-RFO-227","serverInstance":"252-RFO-227.mktoweb.com","marketoConfigured":true,"formConfigured":true,":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"],":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/container"},"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","id":"container-db73448c66","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":items":{"text":{"id":"text-b619aa807d","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EProduct\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/platform/\"\u003EPlatform\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/product/snowflake-cowork/\"\u003ESnowflake CoWork\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/data-engineering/\"\u003EData Engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/ai/\"\u003EAI\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-17518c562c","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatus\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text","text_copy"],":type":"snowflake-site/components/container"},"container_copy_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-305faa0a7e","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-53b392cdac","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003E\u003Ca href=\"/en/solutions/industries/\"\u003EIndustries\u003C/a\u003E\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/advertising-media-entertainment/\"\u003EAdvertising, Media & Entertainment\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/financial-services/\"\u003EFinancial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/healthcare-and-life-sciences/\"\u003EHealthcare & Life Sciences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/manufacturing/\"\u003EManufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/public-sector/\"\u003EPublic Sector\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/retail-consumer-goods/\"\u003ERetail & Consumer Goods\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/solutions/industries/telecom/\"\u003ETelecom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/solutions/industries/technology/\"\u003ETechnology\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],":type":"snowflake-site/components/container"},"container_copy":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-52ad4f6e6c","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-6e470c6bf5","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ECompany\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/about-snowflake/\"\u003EAbout Snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/leadership-and-board/\"\u003ELeadership & Board\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://careers.snowflake.com/us/en\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECareers\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://investors.snowflake.com/overview/default.aspx\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EInvestor Relations\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://trust.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ETrust Center\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/brand-guidelines/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EBrand Guidelines\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/contact/\"\u003EContact\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/news/\"\u003ENewsroom\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/esg/\"\u003EEnvironmental, Social & Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/snowflake-ventures/\"\u003ESnowflake Ventures\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/end-data-disparity/\"\u003EEnd Data Disparity\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/summit/\"\u003ESnowflake Summit 26\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],":type":"snowflake-site/components/container"},"container_copy_copy_":{"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-eacf0c0748","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"text":{"id":"text-d6f394cd2f","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ELearn\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/en/resources/\"\u003EResource Library\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/webinars/demo/\"\u003ELive Demos\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/fundamentals/\"\u003EFundamentals\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/training/\"\u003ETraining\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/certifications/\"\u003ECertifications\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://learn.snowflake.com/en/\"\u003ESnowflake University\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/developers/guides\"\u003EDeveloper Guides\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/data-governance/\"\u003EData Governance\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_1622723482","container","container_copy_copy","container_copy","container_copy_copy_"],":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["flexible_column_cont"],":type":"snowflake-site/components/container"},"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","id":"container-d0f3bbaa24","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":items":{"container_112062425":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-147527fe7d","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-b7d764ba50","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":{"id":"container-232add837a","layout":"SIMPLE",":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","id":"container-0191d2e5de","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":items":{"container":{"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-9fb4e4edfc","layout":"RESPONSIVE_GRID","columnCount":12,"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"image":{"id":"image-3f0a6d1417","additionalClasses":"sf-footer__logo","lazyEnabled":true,"alt":"Snowflake logo","imageLink":{"valid":true,"url":"/en/"},"src":"https://www.snowflake.com/content/experience-fragments/snowflake-site/language-masters/en/site/footer/master/_jcr_content/root/container_573483281_/container_112062425/flexible_column_cont/flexible_column_content_container_1/container/container/image.coreimg.svg/1747882370694/nav-icon-snowflake-bug.svg","height":"64","width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"],":type":"snowflake-site/components/container"},"text_copy_copy_16360":{"id":"text-7ed1df3d7c","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-0829f64816","title":" ","htmlContent":"\u003Cdiv class=\"sf-footer__social\"\u003E\r\n\u003Cdiv data-testid=\"snowflake-footer-twitter\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://x.com/Snowflake\" data-testid=\"button-external\" aria-label=\"X (Twitter)\" role=\"button\" class=\"snowflake-button-container\" title=\"X (Twitter)\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"none\" viewBox=\"0 0 59 53\" class=\"button-icon\"\u003E\u003Cpath fill=\"currentColor\" d=\"M46.614 0h9.044L35.8 22.49 59 53H40.795L26.54 34.46 10.223 53H1.18l21.036-24.055L0 0h18.657l12.878 16.937zM43.45 47.72h5.013L16.023 5.085h-5.387z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-linkedin\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.linkedin.com/company/3653845\" data-testid=\"button-external\" aria-label=\"LinkedIn\" role=\"button\" class=\"snowflake-button-container\" title=\"LinkedIn\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M22.223 0H1.772C.792 0 0 .773 0 1.73v20.536C0 23.222.792 24 1.772 24h20.451c.98 0 1.777-.778 1.777-1.73V1.73C24 .773 23.203 0 22.223 0ZM7.12 20.452H3.558V8.995H7.12v11.457ZM5.34 7.434a2.064 2.064 0 1 1 0-4.125 2.063 2.063 0 0 1 0 4.125Zm15.112 13.018h-3.558v-5.57c0-1.326-.024-3.037-1.852-3.037-1.851 0-2.133 1.449-2.133 2.944v5.663H9.356V8.995h3.413v1.566h.047c.473-.9 1.636-1.852 3.365-1.852 3.605 0 4.27 2.372 4.27 5.457v6.286Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-facebook\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.facebook.com/snowflakedb/\" data-testid=\"button-external\" aria-label=\"Facebook\" role=\"button\" class=\"snowflake-button-container\" title=\"Facebook\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M24 12c0-6.627-5.373-12-12-12S0 5.373 0 12c0 5.99 4.388 10.954 10.125 11.854V15.47H7.078V12h3.047V9.356c0-3.007 1.792-4.668 4.533-4.668 1.312 0 2.686.234 2.686.234v2.953H15.83c-1.491 0-1.956.925-1.956 1.875V12h3.328l-.532 3.469h-2.796v8.385C19.612 22.954 24 17.99 24 12Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\u003Cdiv data-testid=\"snowflake-footer-youtube\" class=\"snowflake-button-icon snowflake-button-white snowflake-footer-social-item\"\u003E\u003Cdiv class=\"snowflake-button-icon \"\u003E\u003Ca href=\"https://www.youtube.com/user/snowflakecomputing\" data-testid=\"button-external\" aria-label=\"YouTube\" role=\"button\" class=\"snowflake-button-container\" title=\"YouTube\" tabindex=\"0\" target=\"_blank\" rel=\"noreferrer\"\u003E\u003Cdiv data-testid=\"button-icon-wrapper\"\u003E\u003Csvg xmlns=\"http://www.w3.org/2000/svg\" fill=\"currentColor\" viewBox=\"0 0 24 24\" class=\"button-icon\"\u003E\u003Cpath d=\"M23.76 7.2s-.233-1.655-.955-2.381c-.914-.956-1.936-.961-2.405-1.017-3.356-.244-8.395-.244-8.395-.244h-.01s-5.039 0-8.395.244c-.469.056-1.49.06-2.405 1.017C.473 5.545.244 7.2.244 7.2S0 9.145 0 11.086v1.819c0 1.94.24 3.886.24 3.886s.233 1.654.95 2.38c.915.957 2.115.924 2.65 1.027 1.92.183 8.16.24 8.16.24s5.044-.01 8.4-.249c.469-.056 1.49-.06 2.405-1.017.722-.727.956-2.381.956-2.381S24 14.85 24 12.905v-1.819c0-1.94-.24-3.886-.24-3.886ZM9.52 15.113V8.367l6.483 3.385-6.483 3.36Z\"\u003E\u003C/path\u003E\u003C/svg\u003E\u003C/div\u003E\u003C/a\u003E\u003Cdiv\u003E\u003C/div\u003E\u003C/div\u003E\u003C/div\u003E\r\n\u003C/div\u003E",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container","text_copy_copy_16360","markup_editor"],":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["flexible_column_cont"],":type":"snowflake-site/components/container"}},":itemsOrder":["container_112062425"],":type":"snowflake-site/components/container"},"markup_editor_copy":{"id":"markup-editor-61cac3e701","title":"New css","cssContent":".snowflake-image-container img{background-color:transparent}div.snowflake-person-chip-avatar{width:80px !important}#snowflake-blog-template-main-container .aem-GridColumn:has(.vertical-video){background-color:#000;border-radius:16px;overflow:hidden}#snowflake-blog-template-main-container .vertical-video{max-width:240px;margin-left:auto;margin-right:auto}@media screen and (min-width:1367px){.dynamic .heading-1-v2 .snowflake-title-v2-line{font-size:72px !important;line-height:60px !important}}.snowflake-flexible-column-container-items-alignment-match-height .download-card,.snowflake-flexible-column-container-items-alignment-match-height .download-card\u003E.container{height:100%}.download-card div.code-toolbar\u003E.toolbar .copy-to-clipboard-button{background-color:white;border:1px solid #a9e1f6;margin-right:4px;top:6px;border-radius:16px;height:26px;width:40px}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:before{content:'';background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23249EDC;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23249EDC;'%3E%3C/path%3E%3C/svg%3E\");background-size:auto 65%;background-position:center;background-repeat:no-repeat;top:0;left:0;width:100%;height:100%}.download-card .snowflake-code-snippet\u003Ediv.code-toolbar\u003E.toolbar\u003E.toolbar-item\u003Ebutton:hover:before{background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='16' height='16' viewBox='0 0 24 24' fill='none' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Crect x='9' y='9' width='13' height='13' rx='2' ry='2' style='stroke:%23fff;'%3E%3C/rect%3E%3Cpath d='M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1' style='stroke:%23fff;'%3E%3C/path%3E%3C/svg%3E\")}.download-card\u003Ediv{background-color:#fff;border:1px solid #ccc;border-radius:8px;padding:24px}.download-chip__headline{border-bottom:1px solid #ccc;padding-bottom:16px;margin-bottom:16px}.download-chip{padding:8px 12px !important;border-radius:4px;transition:300ms ease background-color}.download-chip .black-blue-text-color .snowflake-title-v2-line{color:#000 !important;padding-right:24px;font-family:'Lato',sans-serif;font-size:14px !important;font-weight:500 !important}.download-chip .black-blue-text-color .snowflake-title-v2-line:not(:first-child){opacity:.6;font-style:italic !important}.download-chip .snowflake-content-chip-button{display:none}.download-chip.is-external-link{background-size:16px 16px;background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='15' viewBox='0 0 15 15' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1.06055 13.0607L11.8605 2.26067M13.0605 10.6607V1.06067H3.46055' stroke='%23249EDC' stroke-width='2.12132' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-chip{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_883_7979)'%3E%3Cpath d='M3.375 16.875H14.625' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M9 1.125V11.25' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M4.5 7.875L9 12.375L13.5 7.875' stroke='%23249EDC' stroke-width='1.40625' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/g%3E%3Cdefs%3E%3CclipPath id='clip0_883_7979'%3E%3Crect width='18' height='18' fill='white'/%3E%3C/clipPath%3E%3C/defs%3E%3C/svg%3E%0A\");background-size:24px auto;background-repeat:no-repeat;background-position:calc(100% - 12px) center}.download-chip__headline{display:flex;gap:16px;flex-direction:row !important;flex-wrap:nowrap}.download-chip__headline::before{content:'';display:inline-block;width:24px;height:24px;background-position:center;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='21' viewBox='0 0 21 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 9.89999C8.13657 9.89999 8.74702 9.64713 9.19711 9.19704C9.64719 8.74696 9.90005 8.13651 9.90005 7.49999V2.69999C9.90005 2.06347 9.64719 1.45302 9.19711 1.00293C8.74702 .552844 8.13657 .299988 7.50005 .299988H2.70005C2.06353 .299988 1.45308 .552844 1.00299 1.00293C.552905 1.45302 .300049 2.06347 .300049 2.69999V7.49999C.300049 8.13651 .552905 8.74696 1.00299 9.19704C1.45308 9.64713 2.06353 9.89999 2.70005 9.89999H7.50005ZM7.50005 7.49999H2.70005V2.69999H7.50005V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.50005 20.3C8.13657 20.3 8.74702 20.0472 9.19711 19.5971C9.64719 19.147 9.90005 18.5365 9.90005 17.9V13.1C9.90005 12.4635 9.64719 11.853 9.19711 11.403C8.74702 10.9529 8.13657 10.7 7.50005 10.7H2.70005C2.06353 10.7 1.45308 10.9529 1.00299 11.403C.552905 11.853 .300049 12.4635 .300049 13.1V17.9C.300049 18.5365 .552905 19.147 1.00299 19.5971C1.45308 20.0472 2.06353 20.3 2.70005 20.3H7.50005ZM7.50005 17.9H2.70005V13.1H7.50005V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 9.89999C18.5366 9.89999 19.147 9.64713 19.5971 9.19704C20.0472 8.74696 20.3001 8.13651 20.3001 7.49999V2.69999C20.3001 2.06347 20.0472 1.45302 19.5971 1.00293C19.147 .552844 18.5366 .299988 17.9001 .299988H13.1001C12.4636 .299988 11.8531 .552844 11.403 1.00293C10.9529 1.45302 10.7001 2.06347 10.7001 2.69999V7.49999C10.7001 8.13651 10.9529 8.74696 11.403 9.19704C11.8531 9.64713 12.4636 9.89999 13.1001 9.89999H17.9001ZM17.9001 7.49999H13.1001V2.69999H17.9001V7.49999Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.9001 20.3C18.5366 20.3 19.147 20.0472 19.5971 19.5971C20.0472 19.147 20.3001 18.5365 20.3001 17.9V13.1C20.3001 12.4635 20.0472 11.853 19.5971 11.403C19.147 10.9529 18.5366 10.7 17.9001 10.7H13.1001C12.4636 10.7 11.8531 10.9529 11.403 11.403C10.9529 11.853 10.7001 12.4635 10.7001 13.1V17.9C10.7001 18.5365 10.9529 19.147 11.403 19.5971C11.8531 20.0472 12.4636 20.3 13.1001 20.3H17.9001ZM17.9001 17.9H13.1001V13.1H17.9001V17.9Z' fill='%23249EDC' stroke='white' stroke-width='.6'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat}.download-chip__headline.is-cli::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M4 17L10 11L4 5' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3Cpath d='M12 19H20' stroke='%23000' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\")}.download-card pre[class*=language-]{padding:8px 12px;background-color:var(--ui-background-05);overflow:hidden}.download-chip__headline.is-windows,.download-chip__headline.is-mac{gap:12px}.download-chip__headline.is-windows::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='4875' height='4875' viewBox='0 0 4875 4875' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg clip-path='url(%23clip0_122_201)'%3E%3Cpath d='M0 0H2311V2310H0V0ZM2564 0H4875V2310H2564V0ZM0 2564H2311V4875H0V2564ZM2564 2564H4875V4875H2564' fill='%23000'/%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-mac::before{width:16px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg version='1.1' id='Layer_1' xmlns:x='ns_extend;' xmlns:i='ns_ai;' xmlns:graph='ns_graphs;' xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink' x='0' y='0' viewBox='0 0 41.5 51' style='enable-background:new 0 0 41.5 51;' xml:space='preserve'%3E%3Cmetadata%3E%3Csfw xmlns='ns_sfw;'%3E%3Cslices%3E%3C/slices%3E%3CsliceSourceBounds bottomLeftOrigin='true' height='51' width='41.5' x='166.1' y='-208.1'%3E%3C/sliceSourceBounds%3E%3C/sfw%3E%3C/metadata%3E%3Cg%3E%3Cpath d='M40.2,17.4c-3.4,2.1-5.5,5.7-5.5,9.7c0,4.5,2.7,8.6,6.8,10.3c-.8,2.6-2,5-3.5,7.2c-2.2,3.1-4.5,6.3-7.9,6.3s-4.4-2-8.4-2 c-3.9,0-5.3,2.1-8.5,2.1s-5.4-2.9-7.9-6.5C2,39.5,.1,33.7,0,27.6c0-9.9,6.4-15.2,12.8-15.2c3.4,0,6.2,2.2,8.3,2.2 c2,0,5.2-2.3,9-2.3C34.1,12.2,37.9,14.1,40.2,17.4z M28.3,8.1C30,6.1,30.9,3.6,31,1c0-.3,0-.7-.1-1c-2.9,.3-5.6,1.7-7.5,3.9 c-1.7,1.9-2.7,4.3-2.8,6.9c0,.3,0,.6,.1,.9c.2,0,.5,.1,.7,.1C24.1,11.6,26.6,10.2,28.3,8.1z'%3E%3C/path%3E%3C/g%3E%3C/svg%3E\")}.download-chip__headline.is-desktop::before{background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cg opacity='.8'%3E%3Cpath d='M1.5 21H22.5V18H1.5V21Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M19.5 15C20.2956 15 21.0587 14.6839 21.6213 14.1213C22.1839 13.5587 22.5 12.7956 22.5 12V6C22.5 5.20435 22.1839 4.44129 21.6213 3.87868C21.0587 3.31607 20.2956 3 19.5 3H4.5C3.70435 3 2.94129 3.31607 2.37868 3.87868C1.81607 4.44129 1.5 5.20435 1.5 6V12C1.5 12.7956 1.81607 13.5587 2.37868 14.1213C2.94129 14.6839 3.70435 15 4.5 15H19.5ZM19.5 12H4.5V6H19.5V12Z' fill='%23000' stroke='white' stroke-width='.75'/%3E%3C/g%3E%3C/svg%3E%0A\")}.download-card .snowflake-code-snippet,.download-card .snowflake-code-snippet code,.download-card .snowflake-code-snippet pre{font-size:14px;color:#000;text-shadow:none !important}.download-chip:hover{background-color:var(--ui-background-05) !important;transition:300ms ease background-color}body:has(.snowflake-skip-to-content[style]) #subNav,.pushdown-banner-dismissed #subNav{top:var(--scroll-padding-top) !important;transition:300ms ease top}body:has(.snowflake-skip-to-content[style*=\"58\"]) #subNav{top:34px !important}body:has(.snowflake-skip-to-content[style*=\"82\"]) #subNav{top:58px !important}body:has(.snowflake-skip-to-content[style*=\"130\"]) #subNav{top:106px !important}body:has(.snowflake-skip-to-content[style*=\"138\"]) #subNav{top:114px !important}body:has(.snowflake-skip-to-content[style*=\"146\"]) #subNav{top:122px !important}.is-hidden .snowflake-person-chip-avatar{display:none}.is-small .snowflake-person-chip-avatar{width:56px;height:56px}.ai-summary ul{margin:16px 0 0 0 !important;padding:0 !important;list-style-type:none}.ai-summary li{margin:0;padding:0 0 0 32px;position:relative}.ai-summary li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.ai-summary li:not(:last-child){margin-bottom:1rem}.snowflake-content-chip-image__image{aspect-ratio:5 / 3 !important}.content-chip-new .snowflake-content-chip-image__image{height:100% !important;aspect-ratio:unset !important}.snapshot-card .snowflake-text p:not(:first-child){margin-top:var(--spacing-01)}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p:has(b){font-family:'Texta',sans-serif;margin-top:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2) p b{font-weight:700 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){border-bottom:1px solid #ccc;padding-bottom:24px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) p:first-child:has(b){font-family:'Texta',sans-serif;font-size:20px !important;margin-bottom:1rem !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li{display:inline-block}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) li a{display:inline-block;text-decoration:none;padding:4px 16px !important;border:1px solid #ccc;border-radius:24px;color:#666 !important}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(3) ul{list-style-type:none;display:flex;padding:0 !important;margin:0 !important;gap:12px}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container img{width:90%;max-width:240px;margin:0 auto}.snapshot-card\u003E.container\u003E.cmp-container\u003E.aem-container{padding:40px;max-width:450px;margin:0 0 0 auto;background-color:#fff;box-shadow:0 2px 6px 0 rgba(152,162,179,.25),0 10px 20px 0 rgba(152,162,179,.10);border-radius:8px;border-top:4px solid var(--ui-01)}.ai-summary{background-color:#f3fbfe;border-left:2px solid var(--ui-01);padding:40px}.ai-summary\u003Espan p:last-child:has(i){color:#666;font-size:14px !important}.ai-summary\u003Espan p:last-child:has(i) a{color:#666 !important;text-decoration:underline !important}.ai-summary\u003Espan p:last-child:has(i) a:hover{color:var(--ui-01) !Important}.ai-summary\u003Espan p:first-child:has(b)::after{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='24' viewBox='0 0 24 24' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M9.3158 3.15226C8.6475 6.2258 6.22698 8.64545 3.15232 9.31587C2.94923 9.36072 2.94923 9.63928 3.15232 9.68413C6.22698 10.3522 8.6475 12.7742 9.3158 15.8477C9.36067 16.0508 9.63933 16.0508 9.6842 15.8477C10.3525 12.7742 12.773 10.3545 15.8477 9.68413C16.0508 9.63928 16.0508 9.36072 15.8477 9.31587C12.773 8.64781 10.3525 6.2258 9.6842 3.15226C9.63933 2.94925 9.36067 2.94925 9.3158 3.15226Z' fill='%23249EDC'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M17.3725 11.5461C16.9098 13.6739 15.2341 15.3491 13.1054 15.8132C12.9649 15.8443 12.9649 16.0371 13.1054 16.0681C15.2341 16.5307 16.9098 18.2074 17.3725 20.3353C17.4035 20.4758 17.5965 20.4758 17.6275 20.3353C18.0902 18.2074 19.7659 16.5323 21.8946 16.0681C22.0352 16.0371 22.0352 15.8443 21.8946 15.8132C19.7659 15.3507 18.0902 13.6739 17.6275 11.5461C17.5965 11.4055 17.4035 11.4055 17.3725 11.5461Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-repeat:no-repeat;background-size:contain;background-position:center;vertical-align:middle;margin-left:8px}.ai-summary\u003Espan p:first-child:has(b){color:var(--ui-01) !important;text-transform:uppercase}.border-top{border-top:1px solid rgba(0,0,0,.2)}.border-top\u003Espan{display:block;padding-top:32px}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9 !important}.content-chip-new .snowflake-content-chip-image__image{border-radius:0;object-fit:cover;height:100%}.sf-footer #ot-sdk-btn.ot-sdk-show-settings,.sf-footer #ot-sdk-btn.optanon-show-settings{color:rgba(255,255,255,.7) !important;text-underline-offset:4px;border-top:none;border-left:none;border-right:none;border-bottom:1px dotted transparent;background-color:transparent !important;background-image:none !important;transition:300ms ease text-decoration-color;padding:0 !important;font-size:12px;font-family:'Lato',sans-serif}.sf-footer #ot-sdk-btn.ot-sdk-show-settings:hover,.sf-footer #ot-sdk-btn.optanon-show-settings:hover{color:rgba(255,255,255,1) !important;border-bottom:1px dotted var(--ui-01);transition:300ms ease text-decoration-color}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{flex-shrink:0}.sf-footer__disclaimers{background-color:#042130}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p a{color:inherit;text-decoration:none !important}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p sup{margin-right:2px}.sf-footer__disclaimers .snowflake-simple-stat-disclaimer p{text-indent:-5px;padding-left:5px}.sf-footer__disclaimers-inner{border-top:1px solid rgba(255,255,255,.25);padding:40px 0}.sf-footer__disclaimers .snowflake-simple-stat{align-items:flex-start;text-align:left;color:rgba(255,255,255,.7);margin-bottom:10px}.sf-footer__social{display:flex;justify-content:center;gap:12px}.sf-footer .snowflake-footer-social-item{margin:0 !important}.sf-footer .snowflake-footer-social-item a{line-height:0;background-color:rgba(3,24,35,.8);display:inline-block;width:48px !important;height:48px;border-radius:8px;display:inline-flex;justify-content:center;align-items:center;transition:300ms ease background-color}.sf-footer .snowflake-footer-social-item a:hover{background-color:var(--ui-01) !important;transition:300ms ease background-color}.sf-footer__bottom{padding-bottom:40px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoError .mktoErrorMsg{max-width:100%;color:#fff}.sf-footer .mktoForm .mktoError .mktoErrorMsg .mktoErrorDetail{display:inline-block}.sf-footer .mktoFormRow:has(.mktoHtmlText:empty){display:none}.sf-footer .mktoFormRow .mktoHtmlText span{color:#fff !important}.sf-footer{background-color:#042130}.sf-footer .optanon-toggle-display:hover{text-decoration-color:var(--ui-01) !important;cursor:pointer !important;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:var(--ui-01);color:#fff !important;transition:300ms ease text-decoration-color;text-decoration:underline;opacity:1}.sf-footer__logo{width:40px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{row-gap:32px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;align-items:center;text-align:center;row-gap:16px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:nth-child(2){text-align:center;flex-grow:1}.sf-footer__legal-links li button,.sf-footer__legal-links li a,.sf-footer__legal-links li{margin:0;color:rgba(255,255,255,.7) !important;font-weight:500}.sf-footer__legal-links li a:hover{color:rgba(255,255,255,1) !important}.sf-footer div.sf-footer__copyright p,.sf-footer div.sf-footer__legal-links li,.sf-footer div.sf-footer__legal-links a,.sf-footer div.sf-footer__legal-links p{font-size:12px !important}.sf-footer__legal-links ul{list-style-type:none;margin:0;padding:0;display:flex;gap:20px;row-gap:4px;justify-content:center;flex-wrap:wrap;text-align:center}.sf-footer__legal-links li:last-child{width:100%}.sf-footer .mktoFormRow:has(.mktoPlaceholder),.sf-footer .mktoFormRow:has(input[type=\"hidden\"]){display:none !important}.sf-footer .mktoFormCol{margin-bottom:0 !important}.sf-footer label[for=\"adhoc1\"]{width:auto !important;flex-grow:1;margin-left:16px}.sf-footer .mktoFieldWrap:has(label[for=\"adhoc1\"]){display:flex;flex-direction:row-reverse;margin-top:22px}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoCheckboxList input[type=checkbox]{background-color:transparent !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-color:transparent !important;color:#fff !important;height:auto !important;border:1px solid rgba(255,255,255,.4) !important;border-radius:4px !important;padding:12px 18px !important}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoEmailField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTelField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoTextField:focus,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select:focus{border-color:var(--ui-01) !important}.sf-footer .mktoForm *{padding:0 !important}.sf-footer .mktoForm,.sf-footer .snowflake-marketo-form-container{padding:0 !important;background:transparent;margin-bottom:0;box-shadow:none}.sf-footer .mktoHtmlText.mktoHasWidth{width:100% !important;margin:24px 0}.sf-footer .mktoFormRow{flex-direction:column}.sf-footer .mktoForm .mktoButtonWrap{margin:0 !important}.sf-footer select{background-image:url(\"data:image/svg+xml,%3Csvg width='14' height='8' viewBox='0 0 14 8' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M.981445 1.43496L6.90897 7.32496L12.9314 1.33496' stroke='white' stroke-width='1.33333' stroke-miterlimit='10' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\") !important}.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative{justify-content:flex-start}.sf-footer *::placeholder{color:#fff !important;opacity:.8}.sf-footer .mktoForm .mktoButtonWrap.mktoSimple .mktoButton{background-color:var(--ui-01) !important;color:#fff !important;width:100% !important;padding:12px 16px !important;border:1px solid var(--ui-01) !important;background-image:none !important;border-radius:48px;text-transform:uppercase;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:16px !important;line-height:1.2}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoHtmlText\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap .mktoLabel\u003Espan,.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap label.mktoLabel{color:#fff !important}.sf-footer__newsletter-title p:not(:first-child){margin-top:8px !important}.sf-footer__newsletter-title p b{font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:22px !important;line-height:1.2}.sf-footer__newsletter-title p:last-child{font-size:14px !important;opacity:.8}.sf-footer__link-group li a[target=\"_blank\"]::after{content:'';display:inline-block;width:10px;height:10px;margin-left:5px;background-image:url(\"data:image/svg+xml,%3Csvg width='11' height='11' viewBox='0 0 11 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.72222 1.22222C6.38471 1.22222 6.11111 .948616 6.11111 .611111C6.11111 .273607 6.38471 0 6.72222 0H10.3889C10.551 0 10.7064 .0643867 10.821 .178988C10.9356 .293596 11 .449032 11 .611111V4.27778C11 4.61529 10.7264 4.88889 10.3889 4.88889C10.0514 4.88889 9.77778 4.61529 9.77778 4.27778V2.08647L4.09879 7.76545C3.86013 8.00409 3.4732 8.00409 3.23454 7.76545C2.99589 7.52681 2.99589 7.13986 3.23454 6.90122L8.91355 1.22222H6.72222ZM0 2.44444C0 1.76943 .547207 1.22222 1.22222 1.22222H4.27778C4.61529 1.22222 4.88889 1.49583 4.88889 1.83333C4.88889 2.17084 4.61529 2.44444 4.27778 2.44444H1.22222V9.77778H8.55556V6.72222C8.55556 6.38471 8.82915 6.11111 9.16667 6.11111C9.50418 6.11111 9.77778 6.38471 9.77778 6.72222V9.77778C9.77778 10.4528 9.23059 11 8.55556 11H1.22222C.547207 11 0 10.4528 0 9.77778V2.44444Z' fill='white'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-position:center}.sf-footer__link-group ul,.sf-footer__link-group li{margin:0;padding:0;list-style-type:none}.sf-footer__link-group ul{margin-top:20px !important}.sf-footer__link-group li{margin-top:15px}.sf-footer div.sf-footer__link-group\u003Espan\u003Ep\u003Ea,.sf-footer div.sf-footer__link-group\u003Espan\u003Ep{color:var(--ui-01) !important;font-weight:800 !important;font-family:'Texta',sans-serif !important;font-size:20px !important;line-height:1.2}.sf-footer__link-group li a{opacity:.9;color:#fff !important;font-weight:500 !important;font-size:15px !important;line-height:1.3}.sf-footer__link-group li a:hover{opacity:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::before,.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container::after{display:none}.sf-footer__column{flex-grow:1}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child){width:50%}@media (min-width:800px){.sf-footer__legal-links ul{justify-content:flex-start;text-align:left}.sf-footer__social{justify-content:flex-end}.sf-footer__legal-links ul{padding-left:24px}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container{text-align:right;flex-wrap:nowrap}.sf-footer__legal-links.align-left ul{justify-content:flex-start}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:space-between;flex-direction:row}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto !important;max-width:200px}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;order:2;width:100% !important;max-width:none}.sf-footer__legal-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto}}@media screen and (min-width:1380px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container{flex-wrap:nowrap}.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{padding-right:48px;max-width:380px;background-color:rgba(3,24,35,.4);padding:32px;margin-left:48px;border-radius:16px}.sf-footer__link-group li,.sf-footer__link-group li a{font-size:14px !important;line-height:1.3}}@media screen and (max-width:991px){.sf-footer-grid__inner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{order:2;margin-top:24px !important}}@media screen and (max-width:420px){.is-reduced-mobile .heading-1-v2,.is-reduced-mobile .heading-1-v2-sm{font-size:32px;line-height:28px}}.quote-content-chip{background-color:var(--ui-background-05);padding:24px;border-radius:12px;position:relative}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan{color:rgba(0,0,0,.8) !important;font-size:15px !important;line-height:1.5 !important;font-family:'Lato',sans-serif;font-weight:400 !important}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:not(:first-child){max-width:calc(100% - 200px)}.quote-content-chip .black-blue-text-color .snowflake-title-v2-line\u003Espan:nth-child(2){font-family:'Texta',sans-serif;color:#000 !important;font-size:20px !important;font-weight:800 !important;margin-top:24px}.quote-content-chip .snowflake-content-chip-image{width:140px !important}@media screen and (min-width:992px){.quote-content-chip .snowflake-content-chip-image{position:absolute !important;bottom:24px;right:16px}}@media screen and (max-width:991px){.quote-content-chip .snowflake-content-chip-image{margin-bottom:40px}.quote-content-chip{flex-direction:column}}#spa-root{background-color:#fff}.lowercase .snowflake-title-v2-line{text-transform:none !important}.centered .snowflake-logo-content-container-inner{justify-content:center}div.snowflake-linklist-dropdown-menu{max-height:380px}.first-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:first-child{color:var(--ui-01) !important}.is-front{position:relative;z-index:2}.use-case-body .snowflake-text h1,.use-case-body .snowflake-text h2,.use-case-body .snowflake-text h3,.use-case-body .snowflake-text h4,.use-case-body .snowflake-text h5,.use-case-body .snowflake-text h6{font-family:'Texta',sans-serif;color:#000;margin:.25rem 0 0 0}.pc-hero .button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:flex-start}.sf-footer .mktoFormRow .mktoHtmlText span{font-family:'Lato',sans-serif !important}.snowflake-button-primary.snowflake-button-blue .snowflake-button-container{justify-content:center}.related-chip-25{background-color:#fff;border:1px solid rgba(204,204,204,.5);border-radius:8px;padding:20px;position:relative}.related-chip-25:hover{box-shadow:rgba(152,162,179,.1) 0 10px 20px 0}.related-chip-25:hover::after{right:24px;transition:300ms ease right}.related-chip-25::after{content:'';display:block;transition:300ms ease right;background-image:url(\"data:image/svg+xml,%3Csvg width='8' height='14' viewBox='0 0 8 14' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M7.66699 7C7.66699 6.6571 7.53559 6.32825 7.30169 6.08578L2.34446 .947072C1.84529 .429617 1.0164 .429617 .517219 .947072C.0427878 1.43887 .042788 2.21798 .517219 2.70978L4.65591 7L.51722 11.2902C.0427889 11.782 .0427887 12.5611 .51722 13.0529C1.0164 13.5704 1.84529 13.5704 2.34447 13.0529L7.30169 7.91421C7.53559 7.67175 7.66699 7.34289 7.66699 7Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\");width:8px;height:14px;display:block;position:absolute;right:30px;top:50%;transform:translateY(-50%);background-size:contain;background-position:center;background-repeat:no-repeat}.related-chip-25 .heading-5-v2{font-size:22px;line-height:1.1}.related-chip-25 .snowflake-content-chip-image{width:48px;flex-shrink:0}.related-chip-25 .snowflake-content-chip-image__image{aspect-ratio:1;height:auto;object-fit:contain}.related-chip-25 .snowflake-content-chip-button{display:none}.related-chip-25 .snowflake-content-chip-content-without-tag{flex-grow:1;padding-right:24px}.case-study-25.small-logo .snowflake-case-study-card-logo img{width:60px !important}.swiper-slide .case-study-25{width:95%;margin-left:auto;margin-right:auto}.case-study-25 .snowflake-case-study-card-logo img{width:140px !important;height:auto !important;transform:none !important;margin:24px 0 8px 0}.case-study-25 .snowflake-case-study-card-image__image{object-position:left center}.case-study-25 .snowflake-case-study-card-information-container{padding-right:24px}.case-study-25 ul{list-style-type:none;padding:0;margin:8px 0 0 0}.case-study-25 li{font-size:15px !important;line-height:1.3 !important;display:flex;flex-direction:column;border-left:4px solid var(--ui-01);padding-left:24px;margin-top:24px;color:#535862;gap:4px}.case-study-25 li b{display:block;font-family:'Texta',sans-serif;font-weight:900 !important;font-size:48px !important;line-height:.9 !important;color:var(--ui-01)}.case-study-25 .snowflake-case-study-card-description p{color:#535862}.case-study-25 .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){color:#000;font-family:Texta;font-size:30px !important;line-height:1 !important;font-style:normal;font-weight:700;text-indent:-8px}.case-study-25.is-story .snowflake-case-study-card-description p:nth-child(2):not(:has(a)){text-indent:0}.case-study-25 .snowflake-case-study-card-key-card{background-color:transparent}.case-study-25 .snowflake-case-study-card-button{display:none}.case-study-25{border-radius:24px;overflow:hidden}@media screen and (min-width:1024px){.case-study-25 .snowflake-case-study-card-left-container{position:static;width:60%;min-height:0}.case-study-25 .snowflake-case-study-card-right-container::after{content:'';display:block;width:60%;max-width:340px;padding-bottom:50%;background-image:url(\"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='none' viewBox='0 0 22 16' class='snowflake-pushdown-banner-placeholder-arrow'%3E%3Cpath fill='%2329B5E8' fill-rule='evenodd' d='M17.865 8.756c.088-.274.124-.555.118-.834a2.551 2.551 0 0 0-1.3-2.142L7.887.76C6.645.055 5.063.475 4.35 1.7a2.535 2.535 0 0 0 .947 3.494l4.916 2.809-4.916 2.801a2.543 2.543 0 0 0-.947 3.502c.713 1.222 2.295 1.64 3.537.934l8.796-5.024a2.541 2.541 0 0 0 1.182-1.46Z' clip-rule='evenodd'%3E%3C/path%3E%3C/svg%3E\");background-size:contain;background-repeat:no-repeat;position:absolute;top:-10%;left:-20%}.case-study-25 .snowflake-case-study-card-right-container{max-width:none;width:40%;position:absolute;top:-5%;right:-5%;z-index:0;height:110%}}@media screen and (min-width:768px){.case-study-25 li{max-width:50%}.case-study-25 ul{display:flex;gap:48px}}.snowflake-text.section-eyebrow p{margin-left:auto;margin-right:auto;margin-bottom:16px !important}.snowflake-text.section-eyebrow p,.snowflake-text.eyebrow-text p{text-transform:uppercase;font-family:'Texta',sans-serif !important;font-weight:800 !important;letter-spacing:.025em;margin-bottom:12px;line-height:1.1 !important}.snowflake-title-v2.dynamic .heading-2-v2 span.snowflake-title-v2-line{font-size:clamp(2.5rem,4.5vw,4rem) !important;line-height:.82 !important}.checklist ul{padding:0;margin:0}.checklist ul li{list-style-type:none;padding-left:32px;position:relative}.checklist ul li:not(:last-child){margin-bottom:1em}.checklist ul li::before{content:'';display:inline-block;width:20px;height:20px;background-image:url(\"data:image/svg+xml,%3Csvg width='24' height='25' viewBox='0 0 24 25' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Crect y='.985352' width='24' height='24' rx='12' fill='%23D4F0FA'/%3E%3Cpath d='M7.28613 13.2967L10.7147 16.7253L17.5718 9.86816' stroke='%2329B5E8' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;position:absolute;top:3px;left:0}.last-line-blue .snowflake-typographyv2 .snowflake-title-v2-line:last-child{color:var(--ui-01)}.snowflake-text p sup{line-height:0}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px;line-height:1;text-transform:none;font-weight:700}.snowflake-title-v2.lowercase .heading-2-v2{font-size:32px;line-height:1;text-transform:none;font-weight:700}.content-chip-new{border:1px solid rgba(204,204,204,.5);border-radius:16px;overflow:hidden}.content-chip-new .snowflake-image-container{border-radius:0;display:none}.content-chip-new .snowflake-content-chip-image{margin-right:0;max-width:180px;flex-shrink:0}.content-chip-new .snowflake-content-chip-content{padding:24px}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:first-child{font-size:24px;line-height:1.1}.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){font-family:'Lato',sans-serif;font-size:17px;color:#535862 !important;font-weight:500;line-height:1.45;margin-top:8px;display:none}div.snowflake-text a{font-weight:normal;color:var(--ui-01);text-decoration:underline;text-underline-offset:4px;text-decoration-style:dotted !important;text-decoration-color:transparent;transition:300ms ease text-decoration-color}div.snowflake-text a:hover{text-decoration-color:var(--ui-01);transition:300ms ease text-decoration-color}.footer-nav__link-group .snowflake-button-container,.subnav__item--button,.snowflake-card-v2-advanced-button .snowflake-button-container{justify-content:flex-start}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center}.button-container\u003E.container\u003E.cmp-container\u003E.aem-container .snowflake-button-primary+.snowflake-button-link{margin-left:12px !important}.snowflake-button-regular.snowflake-button-link .snowflake-button-container{font-size:18px !important;text-align:left;justify-content:flex-start;line-height:1.4 !important}body .snowflake-card-v2-advanced{border:1px solid rgba(204,204,204,.5);border-radius:var(--spacing-02);transition:300ms ease all}body .snowflake-card-v2-advanced:hover{transform:translateY(-10px);box-shadow:rgba(152,162,179,.1) 0 10px 20px 0;transition:300ms ease all}body .snowflake-card-v2-advanced-inner{border-bottom:none}body .snowflake-card-v2-advanced-image{line-height:0}body .snowflake-card-v2-advanced-image__image{aspect-ratio:16 / 9}body .snowflake-card-v2-advanced-content{position:relative}body .snowflake-card-v2-advanced-content::after{content:'';display:block;position:absolute;bottom:0;left:0;transition:300ms ease all;width:20%;height:4px;background-color:var(--ui-01);opacity:0}body .snowflake-card-v2-advanced:hover .snowflake-card-v2-advanced-content::after{width:100%;opacity:1;transition:300ms ease all}body .snowflake-card-v2-advanced .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transition:300ms ease transform}body .snowflake-card-v2-advanced:hover .snowflake-button-link.snowflake-button-blue .snowflake-button-container\u003E.link-icon{transform:translateX(4px);transition:300ms ease transform}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-wrap:wrap;gap:24px}.six-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.three-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.four-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container,.five-columns.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:100%;margin:0 !important}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px);margin:0 !important}@media screen and (min-width:768px){.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(50% - 12px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}}@media screen and (min-width:1024px){.snowflake-title-v2.lowercase .heading-3-v2{font-size:34px}.snowflake-title-v2.lowercase.larger .heading-2-v2{font-size:44px;line-height:.95}.three-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.333% - 16px)}.four-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(25% - 18px)}.five-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(20% - 19.2px)}.six-columns\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(16.6666% - 20px)}.snowflake-title-v2.lowercase .heading-3-v2{font-size:28px !important}}@media screen and (min-width:1200px){.snowflake-title-v2.lowercase .heading-2-v2{font-size:40px}.content-chip-new .snowflake-content-chip-content{padding:32px}.content-chip-new .snowflake-image-container,.content-chip-new .black-blue-text-color .snowflake-title-v2-line:not(:first-child){display:block}}.promo-banner-25{border-radius:16px;overflow:hidden}.promo-banner-25 .snowflake-premium-content-banner-image-container{position:relative;max-width:380px}.promo-banner-25 .snowflake-text{color:#535862}.promo-banner-25 .snowflake-premium-content-banner-image__image{transform:translateY(8px);transition:300ms ease transform;border-radius:0;width:85%;margin:0 auto;display:block;position:relative;z-index:1}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover .snowflake-premium-content-banner-image__image{transform:translateY(0);transition:300ms ease transform}.promo-banner-25 .snowflake-premium-content-banner-image__inner{height:auto;padding-top:24px}.promo-banner-25 .snowflake-premium-content-banner-image__link{position:relative;z-index:1;height:auto}.promo-banner-25 .snowflake-premium-content-banner-image__link::after{content:'';display:block;position:absolute;clip-path:polygon(0 0,66% 0,100% 100%,0 100%);bottom:0;left:0;width:100%;height:100%;background:var(--ui-01);transition:300ms ease width}.promo-banner-25 .snowflake-premium-content-banner-image__link:hover::after{width:110%;transition:300ms ease width}.sf-footer .snowflake-marketo-form .mktoFormRow .mktoFieldWrap select{background-position:95% 50%}.sf-footer__disclaimers .text-size-small .snowflake-text p{color:#fff !important;font-size:10px !important;opacity:.8}@media screen and (min-width:768px){.sf-footer__disclaimers .text-size-small .snowflake-text p{font-size:12px !important}}@media screen and (max-width:1023px){.mobile-top-padding{padding-top:64px}}@media (max-width:799px){.sf-footer .snowflake-marketo-form .mktoButtonWrap.mktoNative .mktoButton{width:100% !important}.sf-footer__logo{text-align:center;display:block;margin:0 auto}}.customer-card .snowflake-card-v2-advanced-image{aspect-ratio:4.35 / 1}.customer-card .snowflake-card-v2-advanced-image__image{width:100%;height:100%;padding-left:8px;object-fit:contain;object-position:left center;margin:0 !important;aspect-ratio:initial}.customer-card .snowflake-card-v2-advanced-image__inner{height:110px}.customer-card .snowflake-card-v2-advanced-tag-indicator{display:none}.pc-hero .snowflake-container-arrow-small-gray-image{top:-34% !important;width:18% !important}.pc-hero .snowflake-container-arrow-small-gray-image path{fill:var(--ui-01);opacity:1}@media screen and (max-width:767px){.mobile-padding-top{padding-top:64px}.hide-mobile{display:none !important}.pc-hero{padding-top:52px}.pc-hero .snowflake-text p,.pc-hero .left-alignment .snowflake-title-v2-line,.pc-hero h1 span{text-align:center !important}}div.snowflake-pushdown-banner-button{margin-top:0}.button-group.align-center\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:center !important}.text-center .snowflake-breadcrumb-swiper .swiper-wrapper{justify-content:center}div.snowflake-breadcrumb a.snowflake-breadcrumb-item,.snowflake-breadcrumb div.snowflake-breadcrumb-item{text-transform:none;font-weight:500}.snowflake-breadcrumb svg{display:none !important}.snowflake-breadcrumb a:has(svg)::after{content:'/';margin:0 12px;color:#666}.hide-filters .snowflake-filterable-and-searchable-grid-top-part{display:none !important}.page-section{padding-left:24px;padding-right:24px}@media screen and (min-width:768px){.page-section{padding-left:48px;padding-right:48px}}.download-card pre[class*=language-]{overflow-x:scroll !important}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container_copy","container_573483281_","markup_editor_copy"],":type":"snowflake-site/components/container"}},":itemsOrder":["root"],"classNames":"aem-xf",":type":"snowflake-site/components/experiencefragment"},"markup_editor":{"id":"markup-editor-8f30465336","title":"Quickstarts Overrides","cssContent":".snowflake-markdown blockquote{padding:24px 32px;background:#f6f9fa;border:1px solid #29b5e8;border-radius:16px}.snowflake-markdown .snowflake-image-container img{width:auto !important;max-width:100%}.snowflake-markdown .snowflake-text ol{padding-left:20px !important}.snowflake-markdown .snowflake-text li{margin:0 0 12px 0 !important}.snowflake-markdown h3.snowflake-markdown-h3{font-size:20px !important;font-family:Texta,sans-serif !important}@media (min-width:768px){.snowflake-markdown h3.snowflake-markdown-h3{font-size:28px !important}}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","markup_editor_1950346551","responsivegrid","modal_container","experiencefragment-footer","markup_editor"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],":hierarchyType":"page",":path":"/content/snowflake-site/global/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data",":type":"snowflake-site/components/structure/page","analyticsDebugMode":false,"analyticsContentTags":["snowflake-site:taxonomy/product/analytics","snowflake-site:taxonomy/snowflake-feature/unstructured-data-analysis","snowflake-site:taxonomy/solution-center/certification/quickstart"],"analyticsEnabled":true,"coveoConfig":{"pipeline":"snowflake.com","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","organizationId":"snowflakecomputingproduction8neljofn","searchHub":"snowflake.com"},"isPasswordProtected":false,"locale":"en",":mappedPath":"/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data/","analyticsData":{"excludeFromAnalytics":false,"subCategory":"","pageType":"quickstart-page-template","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/content/snowflake-site/global/en/developers/guides/using-snowflake-cortex-and-streamlit-with-geospatial-data","language":"en","category":"general","pageName":"Using Snowflake Cortex And Streamlit With Geospatial Data","contentTags":["snowflake-site:taxonomy/product/analytics","snowflake-site:taxonomy/snowflake-feature/unstructured-data-analysis","snowflake-site:taxonomy/solution-center/certification/quickstart"]}}