{"templateName":"quickstart-page-template","cssClassNames":"page basicpage summit-page","canonicalLink":"https://www.snowflake.com/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/","robotsTags":[],"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"language":"fr","title":"Premiers pas avec le Data Engineering et le Machine Learning à l’aide de Snowpark for Python","analyticsPageType":"developer","analyticsCategory":"developers","analyticsSubCategory":"guides","excludeFromAnalytics":false,":mappedPath":"/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/",":type":"snowflake-site/components/structure/page",":items":{"root":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"markup_editor_1950346551":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-banner":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-header":"aem-GridColumn aem-GridColumn--default--12","responsivegrid":"aem-GridColumn aem-GridColumn--default--12","experiencefragment-footer":"aem-GridColumn aem-GridColumn--default--12","modal_container":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},":items":{"experiencefragment-banner":{"id":"experiencefragment-a33f760313","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/pushdown-banner/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"pushdown_banner_copy_1875701434":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-816c4f8101",":type":"snowflake-site/components/container",":items":{"pushdown_banner_copy_1875701434":{"id":"pushdown-banner-eca13b62cc","contentHeadline":"Expedition. Gratuit, virtuel, du 4 au 6 novembre.","contentDescription":"Parcours techniques pour les professionnels, résultats pour les dirigeants.","contentJustifyContent":"center","linkStyle":"text-white","linkCTA":{"id":"link-cta","heapButtonClasses":["pushdown_banner"],"showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"/en/expedition/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"S’inscrire"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy_1875701434"]},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"],"classNames":"aem-xf"},"experiencefragment-header":{"id":"experiencefragment-063f32854c","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/mega-nav-header/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-995aa714e3",":type":"snowflake-site/components/container",":items":{"markup_editor":{"id":"markup-editor-359f4d3e35","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:'Ce que Snowflake peut faire pour vous \u003E';display:block;color:var(--ui-01);margin-top:16px}.nav-item__platform-parent .snowflake-mega-nav-nav-item-description::after{content:'Voir la plateforme \u003E';display:block;color:var(--ui-01);margin-top:16px}}@media screen and (min-width:1367px){.snowflake-mega-nav-nav-item-description{font-size:13px !important;line-height:20px !important}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{font-size:17px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-title,.nav-item__platform-parent .snowflake-mega-nav-nav-item-title{font-size:24px !important;line-height:32px !important;margin-bottom:8px !important}.nav-item__platform-parent-why-sf .snowflake-mega-nav-nav-item-description,.nav-item__platform-parent .snowflake-mega-nav-nav-item-description{font-size:14px !important;line-height:20px !important}}html.wf-texta-n9-loading .display-1-v2{font-size:48px!important;line-height:50px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-4-v2{font-size:18px!important;line-height:24px!important;font-family:sans-serif!important}@media screen and (min-width:768px){html.wf-texta-n9-loading .display-2-v2{font-size:48px!important;line-height:50px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:55.5px!important;line-height:54px!important;letter-spacing:-.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .heading-5-v2,html.wf-lato-n4-loading .snowflake-card-v2-advanced-text .snowflake-text p{font-size:15.5px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:34px!important;line-height:38px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-4,html.wf-texta-n8-loading .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-regular .snowflake-button-container{font-size:13px!important;line-height:20px!important;letter-spacing:.25px!important;font-family:sans-serif!important}}@media screen and (min-width:1024px){html.wf-lato-n4-loading .snowflake-mega-nav-nav-item-description{font-size:11.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .snowflake-button-compact .snowflake-button-container{font-size:12px!important;letter-spacing:0!important;line-height:18px!important}}@media screen and (min-width:1367px){html.wf-lato-n4-loading .hp-hero__eyebrow a\u003Eb:first-child{font-size:11px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .hp-hero__eyebrow a{font-size:13px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-2-v2{font-size:61px!important;line-height:60px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .display-1-v2{font-size:74.5px!important;line-height:74px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-2,html.wf-texta-n9-loading .heading-2-v2{font-size:41px!important;letter-spacing:-.75px!important;font-family:sans-serif!important}html.wf-texta-n9-loading .heading-3-v2{font-family:sans-serif!important;letter-spacing:-.75px!important;font-size:33.75px!important}html.wf-texta-n9-loading .heading-4-v2{font-size:19.5px!important;line-height:26px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2{font-size:12px!important;font-family:sans-serif!important}html.wf-texta-n8-loading .heading-6-v2.snowflake-mega-nav-navigation-title{font-size:14px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-1,html.wf-lato-n4-loading .cq-Editable-dom[data-cq-data-path*=text] ol\u003Eli,html.wf-lato-n4-loading .snowflake-text li,html.wf-lato-n4-loading .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text li,html.wf-lato-n4-loading .text-size-large .snowflake-text p,html.wf-lato-n4-loading .text-size-large .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-large.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-large.cq-Editable-dom span[data-testid=text-content],html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Ep,html.wf-lato-n4-loading.cq-Editable-dom[data-cq-data-path*=text]\u003Eul\u003Eli{font-size:17.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-2,html.wf-lato-n4-loading .text-size-regular .snowflake-text li,html.wf-lato-n4-loading .text-size-regular .snowflake-text p,html.wf-lato-n4-loading .text-size-regular .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-regular.cq-Editable-dom span[data-testid=text-content],html.wf-texta-n8-loading .snowflake-button-link .snowflake-button-container,html.wf-texta-n8-loading .snowflake-button-link-back .snowflake-button-container{font-size:15.5px!important;font-family:sans-serif!important}html.wf-lato-n4-loading .body-3,html.wf-lato-n4-loading .text-size-small .snowflake-text li,html.wf-lato-n4-loading .text-size-small .snowflake-text p,html.wf-lato-n4-loading .text-size-small .snowflake-text span[data-testid=text-content],html.wf-lato-n4-loading .text-size-small.cq-Editable-dom li,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom p,html.wf-lato-n4-loading .text-size-small.cq-Editable-dom span[data-testid=text-content]{font-size:13.5px!important;font-family:sans-serif!important}}#industryPlatformSection,.sc-hero{background-position:top left;background-size:20% auto}.bwalignc,.bwalignr{list-style-position:inside}.snowflake-text p sup{font-size:10px}#industryPlatformSection .industry-platform__row .snowflake-flexible-column-container-items,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container,.snowflake-hero-system-content-container{gap:16px}.agenda-item p,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv,.partner-details p{margin:0!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::after,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container::before,.hide-logo .snowflake-case-study-card-logo,.partner-page__powered-by-logo,.sc-hero div.code-toolbar\u003E.toolbar,.snowflake-card-v2-advanced.no-link .snowflake-card-v2-advanced-button,.snowflake-partner-hero-card-badge-container{display:none!important}.section--card-mobile-carousel .snowflake-flexible-column-container-items-with-carousel{max-width:100%!important}@media screen and (min-width:768px){.button-group-pair .snowflake-button-container.inline-button--desktop,.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;display:inline-block!important}.button-group-pair\u003E.container\u003E.cmp-container\u003E.aem-container{align-items:center;justify-content:flex-start!important}.button-group-pair.center\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center!important}.section--card-mobile-carousel{margin-left:var(--tablet-portrait-margin,48px)!important;margin-right:var(--tablet-portrait-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-portrait-margin) * 2)!important}}@media screen and (min-width:1024px){.section--card-mobile-carousel{margin-left:var(--tablet-horizontal-margin,48px)!important;margin-right:var(--tablet-horizontal-margin,48px);width:calc(100% - 96px)!important;width:calc(100% - var(--tablet-horizontal-margin) * 2)!important}.snowflake-mega-nav-header-mobile-icon{display:none!important}}@media screen and (min-width:1367px){.section--card-mobile-carousel{margin-left:var(--desktop-margin,6.5%)!important;margin-right:var(--desktop-margin,6.5%);width:87%!important;width:calc(100% - var(--desktop-margin) * 2)!important}.logo-container{min-width:143px}.sc-hero__headline .heading-1-v2{font-size:60px}.snowflake-mega-nav-navigation-title{font-size:17px}.snowflake-mega-nav-dropdown-footer-wrapper .snowflake-title-v2 .snowflake-title-v2-line:first-child{font-size:16px!important;line-height:24px!important}}.hero--home{overflow:hidden;background-color:var(--ui-01);z-index:2}.hp-hero__subheadline{width:90%}.hero--home .snowflake-button-container{transition:.3s}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-secondary a:hover,.hero--home .snowflake-button-white a:hover{transition:.3s;background-color:var(--ui-02)!important;color:var(--ui-05)!important}.hero--home .snowflake-button-secondary a:hover{border-color:var(--ui-05)!important}.hero--home .snowflake-button-primary a:hover,.hero--home .snowflake-button-white a:hover{border-color:var(--ui-02)!important}.bwalignc,.hp-hero__eyebrow{text-align:center}.hp-hero__eyebrow a{display:inline-flex;flex-direction:column;justify-content:center;cursor:pointer;padding:8px;border-radius:var(--spacing-01);gap:8px;align-items:center;background-color:#45aee3;color:var(--ui-03);font-family:Texta,sans-serif;font-weight:800;font-size:16px;line-height:22px;transition:background-color .3s}.hp-hero__eyebrow a:hover{background-color:#7fc6ea;text-decoration:none;transition:background-color .3s}.hp-hero__eyebrow a\u003Eb:first-child{text-transform:uppercase;white-space:nowrap;display:inline-block;background-color:var(--ui-02);color:var(--ui-05);font-size:12px!important;line-height:16px!important;font-family:Lato,sans-serif;font-weight:500!important;padding:3px 6px;border-radius:2px;letter-spacing:1px}@media screen and (min-width:767px){.hp-hero__eyebrow{text-align:left}.hp-hero__eyebrow a{flex-direction:row;text-align:left}}.hero--home__inner .offset-video,.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{max-height:200px;overflow:hidden}.hero--home__inner .offset-video .wistia-responsive-padding{padding-top:100%}.hero--home__inner .snowflake-experience-fragment,.offset-video__bg-image{position:absolute!important;top:0;left:0;width:100%}.offset-video__bg-image{z-index:-1}@media screen and (min-width:768px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{position:absolute!important;max-height:none;top:0;left:0;width:250%;padding-bottom:250%;transform:translate(0,-50%);height:0}.workloads_7.unistore{max-width:317px}}.promo-banner--homepage{z-index:2}.homepage-banner-offset-container::after{content:\"\";display:block;position:absolute;bottom:0;z-index:1;left:0;width:100%;height:80%;background:#fff}.section--quicklinks .snowflake-button-full-width a{padding-left:24px!important;padding-right:24px!important;transition:box-shadow .25s cubic-bezier(.4,0,.2,1);text-align:left;display:flex;justify-content:center;align-items:center}.section--quicklinks .snowflake-button-full-width a:hover{box-shadow:0 16px 16px 0 rgb(0 0 0 / .16);transition:box-shadow .25s cubic-bezier(.4,0,.2,1)}.section--quicklinks .snowflake-button-container:focus-visible a::before,.section--quicklinks .snowflake-button-full-width a::before{content:\"\";width:23px;height:23px;flex-shrink:0;margin-right:12px;display:inline-block;background-size:cover;background-repeat:no-repeat;background-position:center}#industryPartnerSlider .snowflake-navigation-icon.swiper-button-disabled,#partnerResources .section--resource-hub a svg,.button-tabs span.snowflake-tabs-navigation-item:after,.customer-card--hide-cta .snowflake-case-study-card-button,.dot-tabs span.snowflake-tabs-navigation-item::after,.partner-sidebar__mobile-expand,html:not(.aem-AuthorLayer-initial):not(.aem-AuthorLayer-Edit) .tab-content:not(.is-active){display:none}.section--quicklinks .snowflake-button-full-width a.pricing::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/decorative-icons/pricing-icon.svg)}.section--quicklinks .snowflake-button-full-width a.snowflake_on_snowflake::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon_snowflake-bug.svg)}.section--quicklinks .snowflake-button-full-width a.virtual_hands_on_labs::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__training.svg)}.section--quicklinks .snowflake-button-full-width a.weekly_demo::before{background-image:url(https://www.snowflake.com/content/dam/snowflake-site/general/icons/navigation/nav-icon__webinars.svg)}@media screen and (min-width:1024px){.hero--home__inner .snowflake-experience-fragment,.offset-video,.offset-video__bg-image{left:-50%}.section--quicklinks .snowflake-flexible-column-container-items{gap:24px}.snowflake-quote-item-inner{padding:32px 24px 24px!important}}#communitiesOuter_overflowBottomGray::after{max-height:100px}#caseStudyOuter_overflowBottomMidBlue::after{max-height:180px}#caseStudyInner .snowflake-case-study-card .snowflake-wistia-video{border-radius:0!important}#caseStudyInner .snowflake-case-study-card{box-shadow:none!important;border-radius:0}#caseStudyInner{max-width:1200px;margin:0 auto;box-shadow:rgb(152 162 179 / .1) 0 10px 20px 0,rgb(152 162 179 / .25) 0 2px 6px 0;border-radius:8px;overflow:hidden;position:relative;z-index:1}.case-study__logo-bar\u003E.snowflake-flexible-column-container-items{background:#f7f9fa;padding:32px 16px 40px}.case-study__logo-bar .cmp-image__image{width:90%;margin:0 auto;max-width:240px}.hp-platform__text-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:first-child),.sc-sidebar__group .snowflake-button-link{margin-top:8px}.workloads_7.unistore{margin-left:auto;margin-right:auto}#homepageFootnotesInner .snowflake-simple-stat-disclaimer .snowflake-text p{color:#fff!important}.snowflake-simple-stat-disclaimer .snowflake-text p\u003Ea{border-bottom:1px solid var(--ui-03);color:var(--text-03)}.snowflake-card-v2-advanced{color:inherit}#workloadCardGridOuter .snowflake-card-v2-base-front{gap:0}.video-modal.snowflake-modal-window-open-inner{background-color:#fff0;padding:8px;border:none}.snowflake-container-arrow-dotted-faded .snowflake-container-arrow-dotted-faded-image{width:40%!important;max-width:420px;top:4%!important}.list--blue-bullets ul{margin:0!important;padding:0!important;list-style-type:none}.list--blue-bullets li{margin:0;padding:0 0 0 32px;position:relative}.list--blue-bullets li::before{content:\"\";display:block;border-radius:100%;background:#29b5e8;width:18px;height:18px;position:absolute;top:4px;left:0;border:5px solid #e5f2f7;box-sizing:border-box}.list--blue-bullets li:not(:last-child){margin-bottom:1rem}.logo-tabs .snowflake-navigation-container,.snowflake-simple-stat-content:empty,.summit-speaker-card .snowflake-card-v2-advanced-text{margin-bottom:0}#techResourceInner,#techResourceOuter,div.overflow-bottom--blue,div.overflow-bottom--gray,div.overflow-bottom--mid-blue,div.overflow-bottom--white,div.overflow-top--blue,div.overflow-top--gray,div.overflow-top--mid-blue,div.overflow-top--white,div[id$=overflowBottomGray],div[id$=overflowBottomMidBlue],div[id$=overflowTopBlue],div[id$=overflowTopGray]{position:relative}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{content:\"\";display:block;position:absolute;left:0;width:100%;height:40%}div.overflow-top--blue::after,div.overflow-top--gray::after,div.overflow-top--mid-blue::after,div.overflow-top--white::after,div[id$=overflowTopBlue]::after,div[id$=overflowTopGray]::after,div[id$=overflowTopWhite]::after{top:0}div.overflow-bottom--blue::after,div.overflow-bottom--gray::after,div.overflow-bottom--mid-blue::after,div.overflow-bottom--white::after,div[id$=overflowBottomGray]::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowBottomWhite]::after{bottom:0}div.overflow-bottom--white::after,div.overflow-top--white::after,div[id$=overflowBottomWhite]::after,div[id$=overflowTopWhite]::after{background:#fff!important}div.overflow-bottom--gray::after,div.overflow-top--gray::after,div[id$=overflowBottomGray]::after,div[id$=overflowTopGray]::after{background:#f6f9fa!important}div.overflow-bottom--mid-blue::after,div.overflow-top--mid-blue::after,div[id$=overflowBottomMidBlue]::after,div[id$=overflowTopMidBlue]::after{background:#11567f!important}div.overflow-bottom--blue::after,div.overflow-top--blue::after,div[id$=overflowBottomBlue]::after,div[id$=overflowTopBlue]::after{background:#259edc!important}.snowflake-premium-content-banner.promo-banner--no-shadow{box-shadow:none!important}#industryPartnerSlider .cmp-image__image,#industryPartnerSlider .section--partner-tabs .snowflake-image-container .cmp-image__image,#partnerSidebar,.has-shadow .cmp-image__image{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25)}.content-chip--has-desc{align-items:flex-start;padding:20px!important}.content-chip--has-desc .snowflake-content-chip-image{max-width:100px}.content-chip--has-desc .snowflake-content-chip-image__image{aspect-ratio:1}.content-chip--has-desc .snowflake-title-v2-line:first-child{font-size:18px!important}.content-chip--has-desc .snowflake-title-v2-line:nth-child(2){color:#000!important;font-weight:500!important;font-size:16px!important;line-height:22px!important;margin-top:2px!important}.content-chip--has-desc .snowflake-content-chip-button{margin-top:6px!important;font-size:18px!important;display:none}.square-image .snowflake-content-chip-image{aspect-ratio:1;max-width:120px}.section--logo-bar.smaller-logos .snowflake-image-container .cmp-image__image{max-width:200px;margin:0 auto}.snowflake-card-v2-advanced-tag,.snowflake-content-chip-tag{padding:3px 6px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-button,.snowflake-card-v2-advanced-title:first-child,.summit-pricing-block__aside ul{margin-top:0}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:40px;height:40px;display:flex;justify-content:center;align-items:center;margin:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{width:12px;height:12px;background:var(--ui-12);border-radius:100%}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p,.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{font-size:0!important}.dot-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{background:var(--ui-01)}.button-tabs .snowflake-navigation-container .swiper-wrapper{padding:8px 0}.button-tabs .snowflake-navigation-container .swiper-slide{margin:0 6px}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{padding:8px 24px;background-color:#f6f9fa;border-radius:48px;margin:0}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item p{text-transform:uppercase;font-family:Texta,sans-serif;font-weight:700}.button-tabs .border-top{border-top:1px solid #ccc}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{background-color:var(--ui-01);box-shadow:0 2px 6px 0 rgb(152 162 179 / .25),0 10px 20px 0 rgb(152 162 179 / .1)}.button-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active p{color:#fff}.button-tabs.has-icons .snowflake-navigation-container .snowflake-tabs-navigation-item p::before{content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-position:center center;margin-right:12px;vertical-align:middle;margin-top:-3px}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item{width:220px;padding-bottom:50%;height:0;margin:0 8px!important;background-size:cover;background-repeat:no-repeat;opacity:.5;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item:hover{opacity:.75;transition:opacity .3s}.logo-tabs .snowflake-navigation-container .snowflake-tabs-navigation-item.active{opacity:1;transition:opacity .3s}.dot-tabs .aem-container.cmp-tabs,.logo-tabs .aem-container.cmp-tabs{display:flex;flex-direction:column-reverse}.snowflake-icon.is-center{margin:0 auto;display:block}#industryPartnerSlider .snowflake-flexible-column-container-items,#partnerLogoSquare .snowflake-flexible-column-container-items{gap:24px}#techResourceOuter::after{content:\"\";display:block;position:absolute;top:0;left:0;width:100%;height:40%;background:#f6f9fa}#techResourceInner{z-index:1}.partner-tier-tag h6{display:inline-block!important;padding:2px 6px;border-radius:2px;color:#666}.partner-tier-tag.registered h6{background-color:#f6f9fa}.partner-tier-tag.elite h6{background-color:#11567f;color:#fff}.partner-tier-tag.premier h6{background-color:#b14c77;color:#fff}.partner-tier-tag.select h6{background-color:#5094a0;color:#fff}.partner-details\u003Espan{display:flex;gap:24px}.partner-details a{color:inherit!important;font-weight:400!important}.partner-details p::before{content:\"\";display:inline-block;vertical-align:middle;width:16px;height:16px;background-repeat:no-repeat;background-position:center;transform:translateY(-1px);background-size:auto 90%;margin-right:6px}.partner-details__location::before{background-image:url(\"data:image/svg+xml,%3Csvg width='13' height='18' viewBox='0 0 13 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M6.25 17.7531C6.4375 17.7531 6.6 17.6844 6.7375 17.5531C6.875 17.4219 6.95 17.2531 6.95 17.0531C6.95 16.8531 7.075 16.4281 7.3 15.7969C7.5875 15.0281 7.925 14.3156 8.30625 13.6406C8.8 12.7781 9.3125 12.1031 9.85 11.6094C10.75 10.7969 11.4125 9.96563 11.85 9.12188C12.2875 8.27813 12.5063 7.40313 12.5063 6.49063C12.5063 5.36563 12.2187 4.31563 11.6437 3.33438C11.0937 2.40313 10.3438 1.65938 9.4 1.10938C8.43125 .534376 7.375 .246876 6.24375 .246876C5.1125 .246876 4.06875 .534376 3.0875 1.10938C2.15625 1.65938 1.4125 2.40313 .862498 3.33438C.287498 4.31563 0 5.36563 0 6.49063C0 7.47188 .262499 8.42813 .787499 9.35938C1.14375 10.0031 1.65625 10.6656 2.3125 11.3344C2.75625 11.8031 3.24375 12.4781 3.78125 13.3656C4.225 14.0969 4.63125 14.8594 5 15.6656C5.35 16.3844 5.53125 16.8531 5.55625 17.0656C5.55625 17.2594 5.625 17.4156 5.7625 17.5531C5.9 17.6844 6.0625 17.7531 6.25 17.7531ZM6.16875 14.9156C5.775 14.0656 5.325 13.2469 4.825 12.4594C4.275 11.5594 3.7625 10.8719 3.28125 10.3969C2.625 9.71563 2.1375 9.05938 1.825 8.43438C1.5125 7.80313 1.35625 7.16563 1.35625 6.50313C1.35625 5.61563 1.575 4.80313 2.0125 4.05313C2.45 3.30313 3.04375 2.71563 3.7875 2.27813C4.5375 1.84063 5.35 1.62188 6.2375 1.62188C7.125 1.62188 7.9375 1.84063 8.6875 2.27813C9.4375 2.71563 10.0312 3.30313 10.475 4.04688C10.9187 4.80313 11.1375 5.62188 11.1375 6.50313C11.1375 7.90313 10.3937 9.26563 8.9125 10.5969C8.35 11.1094 7.8125 11.7906 7.3 12.6406C6.88125 13.3344 6.50625 14.0969 6.16875 14.9219V14.9156ZM6.26875 8.36563C6.65625 8.36563 7.01875 8.26563 7.35625 8.07188C7.69375 7.87813 7.95625 7.60938 8.14375 7.28438C8.3375 6.95313 8.43125 6.59063 8.43125 6.19688C8.43125 5.80313 8.33125 5.43438 8.1375 5.10313C7.9375 4.76563 7.675 4.50313 7.3375 4.31563C7 4.12813 6.6375 4.02813 6.24375 4.02813C5.85 4.02813 5.4875 4.12813 5.15625 4.32188C4.825 4.52188 4.56875 4.78438 4.375 5.12188C4.18125 5.45938 4.0875 5.82188 4.0875 6.20938C4.0875 6.59688 4.1875 6.95938 4.38125 7.29688C4.58125 7.63438 4.84375 7.89688 5.18125 8.08438C5.51875 8.27813 5.88125 8.37188 6.26875 8.37188V8.36563ZM6.24375 7.50313C5.8875 7.50313 5.575 7.37188 5.31875 7.11563C5.0625 6.85938 4.93125 6.55313 4.93125 6.19063C4.93125 5.82813 5.0625 5.52188 5.31875 5.26563C5.575 5.00938 5.88125 4.87813 6.24375 4.87813C6.60625 4.87813 6.9125 5.00938 7.16875 5.26563C7.425 5.52188 7.55625 5.82813 7.55625 6.19063C7.55625 6.55313 7.425 6.85938 7.16875 7.11563C6.9125 7.37188 6.60625 7.50313 6.24375 7.50313Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}.partner-details__website::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='16' viewBox='0 0 18 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M2.61587 2.96889C2.61587 2.75109 2.79633 2.57062 3.01413 2.57062C3.23192 2.57062 3.41238 2.75109 3.41238 2.96889C3.41238 3.18669 3.23192 3.36716 3.01413 3.36716C2.79633 3.36716 2.61587 3.18669 2.61587 2.96889ZM4.21512 2.96889C4.21512 2.75109 4.39558 2.57062 4.61338 2.57062C4.83117 2.57062 5.01163 2.75109 5.01163 2.96889C5.01163 3.18669 4.83117 3.36716 4.61338 3.36716C4.39558 3.36716 4.21512 3.18669 4.21512 2.96889ZM5.81438 2.96889C5.81438 2.75109 5.99484 2.57062 6.21264 2.57062C6.43043 2.57062 6.61089 2.75109 6.61089 2.96889C6.61089 3.18669 6.43043 3.36716 6.21264 3.36716C5.99484 3.36716 5.81438 3.18669 5.81438 2.96889ZM17.2518 .697559H1.19085C.811258 .697559 .506348 1.0025 .506348 1.38209V14.6179C.506348 14.9975 .811258 15.3024 1.19085 15.3024H17.2518C17.6314 15.3024 17.9363 14.9975 17.9363 14.6179V1.38209C17.9363 1.0025 17.6314 .697559 17.2518 .697559ZM16.5673 2.06035V3.90853H1.86914V2.06035H16.5673ZM1.86914 13.9334V4.78593H16.5673V13.9334H1.86914Z' fill='%2329B5E8'/%3E%3C/svg%3E%0A\")}#partnerSidebar{border-radius:4px;background-color:#fff;padding:24px 24px 32px;border-bottom:6px solid #29b5e8}#partnerSidebar h5,.newsletter-disclaimer p{font-size:14px!important}#partnerSidebar ul{margin-top:0;list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px}#partnerSidebar li{border:1px solid;border-radius:2px;padding:0 4px!important;font-size:11px!important;letter-spacing:.25px;text-transform:uppercase}div.snowflake-partner-hero-card{width:100%;margin:0}.partner-details__logo{max-width:380px;margin:0 auto}@media screen and (max-width:767px){.left-alignment .hp-hero__subheadline{margin-left:auto;margin-right:auto}.left-alignment .hp-hero__headline .snowflake-title-v2-line,.left-alignment .hp-hero__subheadline .snowflake-title-v2-line{text-align:center}.hero--home__inner .snowflake-flexible-column-container-items-top-padding-large{padding-top:var(--spacing-02)}.section--logo-bar\u003E.snowflake-flexible-column-container-items{display:flex;flex-wrap:wrap;flex-direction:row;justify-content:center;gap:8px}.section--logo-bar\u003E.snowflake-flexible-column-container-items\u003Ediv{width:calc(33.33% - 8px)}.partner-sidebar__mobile-expand{display:inline-block;color:#249edc;border-color:#249edc!important}#partnerSidebar li:nth-child(n+6),.summit-nav__links .snowflake-button-tertiary{display:none}.sc-body__sidebar{background-color:#f6f9fa;padding:24px}.sc-body__content{padding:0 24px 24px}.summit-speaker-card .snowflake-card-v2-advanced-content{padding:24px}}#partnerResources h6,.snowflake-tabs-navigation-item p.body-1{font-size:16px!important}#partnerResources .section--resource-hub{padding:0 16px}#partnerResources .section--resource-hub a,.bwalignl{text-align:left}@media screen and (max-width:1023px){.hero--workload .snowflake-hero-system-media-container{width:100%}}.section--timely-content .snowflake-content-chip,.snowflake-mega-nav-dropdown-footer-wrapper{align-items:center}.section--timely-content .snowflake-content-chip-image{max-width:94px}.section--timely-content .snowflake-content-chip-image__inner{line-height:0}.section--timely-content .snowflake-content-chip-image__image{aspect-ratio:1;height:auto}.section--workload-overview .workload-overview__headline{max-width:280px;margin:0 auto}#industryPartnerSlider .swiper-slide{margin-top:0!important;padding:0 12px}#industryPartnerSlider .snowflake-tabs-navigation-item{margin-left:0!important;margin-right:0!important}#industryPartnerSlider .snowflake-premium-content-banner-background-grad-white .snowflake-premium-content-banner{box-shadow:none}#industryPartnerSlider .logo-slider__slide .aem-container{display:flex;padding:0 8px!important;flex-wrap:wrap;gap:16px!important;justify-content:center}#industryPartnerSlider .logo-slider__slide .aem-container\u003Ediv{width:48%;max-width:200px}#useCaseTabs{padding-top:24px;padding-bottom:24px;padding-right:24px}#useCaseTabs .tab-content.is-active{display:block}#useCaseTabs .vert-tab{border-bottom:1px solid #a0bbcc;padding-bottom:16px}#useCaseTabs .vert-tab p{display:inline-block}#useCaseTabs .vert-tab p:hover{cursor:pointer}#useCaseTabs .vert-tab p,#useCaseTabs .vert-tab.is-active p.not-active{color:#249edc}#useCaseTabs .vert-tab p.is-active,#useCaseTabs .vert-tab.is-active p{color:#000}#industryPlatformSection{background-image:url(/adobe/dynamicmedia/deliver/dm-aid--db074ad5-7122-4c51-87a3-76c3aa466182/double-arrow-bg%403x.png);background-repeat:no-repeat}.snowflake-text p.featured-quote__source{font-weight:900!important;text-transform:uppercase;font-size:16px!important;margin-top:2rem!important}.snowflake-text p.featured-quote__title{margin-top:0!important;font-size:16px!important}.snowflake-case-study-card-logo img{width:auto!important;height:100px!important;transform:translateX(-15%)}.snowflake-quote-item-quote-text{font-weight:600!important}#customerStoryStatsInner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row}#customerStoryStat1,#customerStoryStat2{max-width:240px}#storyHighlights{border-radius:4px;padding:1rem}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line,.summit-pricing-block__tile .black-blue-text-color .snowflake-title-v2-line{color:#000!important}.snowflake-youtube-embedded-wrapper{border-radius:var(--small-border-radius)}#arcticNavItem::before,#offset::before,#open-source::before{color:var(--text-05);font-family:Texta,sans-serif!important}#offset,.sc-architecture-caption{margin-top:16px}.hero--press .snowflake-title-v2-line{text-transform:none!important}@media screen and (min-width:768px){.subpage-timely-content__inner\u003E.snowflake-flexible-column-container-items{box-shadow:0 10px 20px 0 rgb(152 162 179 / .1),0 2px 6px 0 rgb(152 162 179 / .25);padding:var(--spacing-04);border-radius:4px;overflow:hidden}#partnerLogoSquare{padding:0 0 0 48px}.hero--workload .snowflake-container{max-width:1440px;margin:0 auto!important;align-items:center}#industryPartnerSlider.snowflake-flexible-column-container-2-column-40-60\u003E.snowflake-flexible-column-container-items{grid-template-columns:minmax(40%,4fr) minmax(0,6fr)}#industryPartnerSlider .swiper-slide{padding:0 24px}.sc-body{padding:48px}.sc-body\u003E.snowflake-flexible-column-container-items{grid-template-columns:7fr 3fr;gap:124px}}.snowflake-button-container.has-icon{display:inline-flex;justify-content:center;align-items:center;text-align:left}.snowflake-button-container.has-icon::before{content:\"\";display:inline-block;width:20px;height:20px;margin-right:12px;background-size:contain;background-repeat:no-repeat;background-position:center}.snowflake-button-container.is-video::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M9 1.28663C13.2523 1.28663 16.7134 4.74768 16.7134 9C16.7134 13.2523 13.2523 16.7134 9 16.7134C4.74768 16.7198 1.28663 13.2588 1.28663 9C1.28663 4.74124 4.74768 1.28663 9 1.28663ZM9 0C4.0336 0 0 4.0336 0 9C0 13.9664 4.0336 18 9 18C13.9728 18 18 13.9664 18 9C18 4.0336 13.9728 0 9 0Z' fill='white'/%3E%3Cpath d='M7.75106 6.18211C7.42941 6.16925 7.16565 6.42658 7.16565 6.74823V11.2772C7.16565 11.7082 7.65457 11.9848 8.02126 11.7597L11.7975 9.4952C12.1578 9.27647 12.1578 8.74252 11.7975 8.52379L8.02126 6.25931C7.93763 6.21428 7.84756 6.18211 7.75106 6.18211Z' fill='white'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-github::before{background-image:url(\"data:image/svg+xml,%3Csvg width='20' height='21' viewBox='0 0 20 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10 .651794C4.475 .651794 0 5.12679 0 10.6518C0 15.0768 2.8625 18.8143 6.8375 20.1393C7.3375 20.2268 7.525 19.9268 7.525 19.6643C7.525 19.4268 7.5125 18.6393 7.5125 17.8018C5 18.2643 4.35 17.1893 4.15 16.6268C4.0375 16.3393 3.55 15.4518 3.125 15.2143C2.775 15.0268 2.275 14.5643 3.1125 14.5518C3.9 14.5393 4.4625 15.2768 4.65 15.5768C5.55 17.0893 6.9875 16.6643 7.5625 16.4018C7.65 15.7518 7.9125 15.3143 8.2 15.0643C5.975 14.8143 3.65 13.9518 3.65 10.1268C3.65 9.03929 4.0375 8.13929 4.675 7.43929C4.575 7.18929 4.225 6.16429 4.775 4.78929C4.775 4.78929 5.6125 4.52679 7.525 5.81429C8.325 5.58929 9.175 5.47679 10.025 5.47679C10.875 5.47679 11.725 5.58929 12.525 5.81429C14.4375 4.51429 15.275 4.78929 15.275 4.78929C15.825 6.16429 15.475 7.18929 15.375 7.43929C16.0125 8.13929 16.4 9.02679 16.4 10.1268C16.4 13.9643 14.0625 14.8143 11.8375 15.0643C12.2 15.3768 12.5125 15.9768 12.5125 16.9143C12.5125 18.2518 12.5 19.3268 12.5 19.6643C12.5 19.9268 12.6875 20.2393 13.1875 20.1393C17.1375 18.8143 20 15.0643 20 10.6518C20 5.12679 15.525 .651794 10 .651794Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-quickstart::before{background-image:url(\"data:image/svg+xml,%3Csvg width='15' height='21' viewBox='0 0 15 21' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M13.8489 2.79368H11.6439V2.38493C11.6439 1.71368 11.1451 .967427 10.4251 .967427H8.94762C8.80887 .359927 8.37387 .299927 7.89762 .299927H7.23012C6.85512 .299927 6.26637 .299927 6.08637 .967427H4.68387C3.94887 .967427 3.35637 1.74368 3.35637 2.38493V2.79368H1.15137C.738867 2.79368 .401367 3.13118 .401367 3.54368V20.2537C.401367 20.6662 .738867 21.0037 1.15137 21.0037H13.8489C14.2614 21.0037 14.5989 20.6662 14.5989 20.2537V3.54368C14.5989 3.13118 14.2614 2.79368 13.8489 2.79368ZM4.29387 2.38493C4.29387 2.18243 4.54137 1.90493 4.68387 1.90493H6.50262C6.76137 1.90493 6.97137 1.69493 6.97137 1.43618C6.97137 1.33868 6.97887 1.27868 6.98637 1.24118C7.05012 1.23368 7.15512 1.23368 7.23387 1.23368H7.90137C7.95012 1.23368 8.00637 1.23368 8.05137 1.23368C8.05512 1.27868 8.05887 1.34243 8.05887 1.43243C8.05887 1.69118 8.26887 1.90118 8.52762 1.90118H10.4289C10.5301 1.90118 10.7101 2.14493 10.7101 2.38118V2.78993H4.29762V2.38118L4.29387 2.38493ZM13.0989 19.4999H1.90137V4.29368H13.0989V19.5037V19.4999Z' fill='%23249EDC'/%3E%3Cpath d='M3.82512 16.0424H11.1751C11.4339 16.0424 11.6439 15.8324 11.6439 15.5736V6.88486C11.6439 6.62611 11.4339 6.41611 11.1751 6.41611H3.82512C3.56637 6.41611 3.35637 6.62611 3.35637 6.88486V15.5736C3.35637 15.8324 3.56637 16.0424 3.82512 16.0424ZM4.29387 15.1049V13.3686H10.7064V15.1049H4.29387ZM10.7101 7.35361V12.4311H4.29762V7.35361H10.7101Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 9.35989H8.83887C9.09762 9.35989 9.30762 9.14989 9.30762 8.89114C9.30762 8.63239 9.09762 8.42239 8.83887 8.42239H6.16512C5.90637 8.42239 5.69637 8.63239 5.69637 8.89114C5.69637 9.14989 5.90637 9.35989 6.16512 9.35989Z' fill='%23249EDC'/%3E%3Cpath d='M6.16512 11.3624H8.83887C9.09762 11.3624 9.30762 11.1524 9.30762 10.8937C9.30762 10.6349 9.09762 10.4249 8.83887 10.4249H6.16512C5.90637 10.4249 5.69637 10.6349 5.69637 10.8937C5.69637 11.1524 5.90637 11.3624 6.16512 11.3624Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-download::before{background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='18' viewBox='0 0 16 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M15.2017 17.1637H.798265C.364425 17.1637 0 16.7993 0 16.3655V12.3568C0 11.923 .364425 11.5585 .798265 11.5585C1.2321 11.5585 1.59653 11.923 1.59653 12.3568V15.5498H14.4035V12.3568C14.4035 11.923 14.7679 11.5585 15.2017 11.5585C15.6356 11.5585 16 11.923 16 12.3568V16.3655C16 16.7993 15.6529 17.1637 15.2017 17.1637Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.84381 12.9642 7.73969 12.9468 7.63557 12.8947C7.34056 12.7733 7.14967 12.4783 7.14967 12.1485L7.18437 .938127C7.18437 .504287 7.5488 .139862 7.98264 .139862C8.41648 .139862 8.7809 .504287 8.7809 .938127L8.7462 10.257L12.8416 6.33509C13.154 6.02273 13.6746 6.04008 13.9696 6.35244C14.282 6.66481 14.2646 7.18542 13.9523 7.48043L8.50325 12.7386C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3Cpath d='M7.94793 12.9642C7.73969 12.9642 7.54881 12.8947 7.39262 12.7386L2.03037 7.53249C1.718 7.22012 1.70065 6.71687 2.01301 6.40451C2.32538 6.09214 2.82863 6.07479 3.141 6.38715L8.50325 11.5932C8.81562 11.9056 8.83297 12.4088 8.52061 12.7212C8.36442 12.8774 8.15617 12.9642 7.94793 12.9642Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\")}.snowflake-button-container.is-expand::before{background-image:url(\"data:image/svg+xml,%3Csvg width='18' height='18' viewBox='0 0 18 18' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M6.64375 10.9125C6.9375 11.2062 6.93125 11.6812 6.64375 11.9687L2.57502 16H3.79375C4.20625 16 4.54376 16.3375 4.54376 16.75C4.54376 17.1625 4.20625 17.5 3.79375 17.5H.756264C.556264 17.5 .36876 17.4187 .22501 17.2812C.22501 17.2812 .206248 17.25 .193748 17.2375C.143748 17.1812 .100004 17.1125 .0625038 17.0437C.0375038 16.9687 .0187492 16.8937 .0187492 16.8187C.0187492 16.8 .0062561 16.7813 .0062561 16.7625V13.725C.0187561 13.3125 .356257 12.9875 .768757 12.9937C1.16876 13 1.48752 13.325 1.50002 13.725V14.9688L5.5875 10.9187C5.88125 10.6312 6.35 10.6312 6.64375 10.9187V10.9125ZM17.5063 .743732C17.5063 .543732 17.425 .356235 17.2875 .218735C17.2875 .218735 17.2562 .199998 17.2437 .193748C17.1875 .137498 17.1188 .0937347 17.0438 .0624847C16.9688 .0374847 16.8938 .0187492 16.8188 .0187492C16.8 .0187492 16.7813 .00623703 16.7625 .00623703H13.725C13.3125 .00623703 12.975 .343745 12.975 .756245C12.975 1.16874 13.3125 1.50623 13.725 1.50623H14.9688L11.1312 5.37498C10.8437 5.67498 10.8563 6.14999 11.1563 6.43124C11.45 6.71249 11.9063 6.70624 12.1938 6.43124L16.0125 2.575V3.79375C16.0125 4.20625 16.35 4.54372 16.7625 4.54372C17.175 4.54372 17.5125 4.20625 17.5125 3.79375V.756245L17.5063 .743732ZM16.7562 12.9688C16.3437 12.9688 16.0063 13.3063 16.0063 13.7188V14.8937L12.1938 10.925C11.9063 10.625 11.4375 10.6188 11.1375 10.9063C10.8375 11.1938 10.8313 11.6625 11.1188 11.9625L15 16.0062H13.7188C13.3063 16.0062 12.9688 16.3437 12.9688 16.7562C12.9688 17.1687 13.3063 17.5063 13.7188 17.5063H16.7562C16.85 17.5063 16.95 17.4875 17.0375 17.45C17.0875 17.425 17.1313 17.3937 17.175 17.3625C17.2063 17.3437 17.2438 17.325 17.275 17.3C17.3313 17.2375 17.375 17.1687 17.4125 17.1C17.4188 17.0875 17.4375 17.075 17.4438 17.0562C17.45 17.025 17.4563 16.9938 17.4625 16.9625C17.4813 16.9 17.5 16.8375 17.5 16.7687V13.725C17.5 13.3125 17.1687 12.975 16.7562 12.975V12.9688ZM.750008 4.53125C1.16251 4.53125 1.50002 4.19374 1.50002 3.78124V2.5L5.59376 6.43124C5.89376 6.71874 6.36251 6.70626 6.65001 6.41251C6.93751 6.11876 6.92501 5.64375 6.63126 5.35625L2.61251 1.49998H3.7875C4.2 1.49998 4.53751 1.16249 4.53751 .749989C4.53751 .337489 4.2 0 3.7875 0H.743752C.668752 0 .600004 .0187355 .531254 .0437355C.506254 .0499855 .481263 .0437477 .462513 .0562477C.443763 .0687477 .425015 .0812462 .406265 .0937462C.337515 .124996 .275004 .168741 .218754 .224991H.212498C.212498 .224991 .175 .28125 .15625 .3125C.11875 .3625 .0812477 .4125 .0562477 .46875C.0374977 .525 .0249992 .587499 .0187492 .643749C.0124992 .674999 0 .712482 0 .743732V3.78124C0 4.19374 .337508 4.53125 .750008 4.53125Z' fill='white'/%3E%3C/svg%3E%0A\")}@keyframes slow-scroll{100%{transform:translateY(-50%)}}.sc-hero{overflow:hidden;background-color:#212d35;background-repeat:repeat-y;background-image:url(\"data:image/svg+xml,%3Csvg width='389' height='17' viewBox='0 0 389 17' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M.638672 7.80824L.638672 9.2566C.638672 9.52364 .85538 9.74024 1.12262 9.74024H2.57204C2.83928 9.74024 3.05598 9.52364 3.05598 9.2566V7.80824C3.05598 7.54119 2.83928 7.32472 2.57204 7.32472L1.12262 7.32472C.85538 7.32472 .638672 7.54119 .638672 7.80824Z' fill='url(%23paint0_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M10.9639 7.80824V9.2566C10.9639 9.52364 11.1806 9.74024 11.4478 9.74024L12.8972 9.74024C13.1645 9.74024 13.3812 9.52364 13.3812 9.2566V7.80824C13.3812 7.54119 13.1645 7.32471 12.8972 7.32471L11.4478 7.32471C11.1806 7.32471 10.9639 7.54119 10.9639 7.80824Z' fill='url(%23paint1_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M21.2891 7.80823V9.2566C21.2891 9.52364 21.5058 9.74024 21.773 9.74024L23.2224 9.74024C23.4897 9.74024 23.7064 9.52364 23.7064 9.2566V7.80823C23.7064 7.54119 23.4897 7.32471 23.2224 7.32471L21.773 7.32471C21.5058 7.32471 21.2891 7.54119 21.2891 7.80823Z' fill='url(%23paint2_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M31.6143 7.80823V9.2566C31.6143 9.52364 31.831 9.74024 32.0982 9.74024H33.5476C33.8149 9.74024 34.0316 9.52364 34.0316 9.2566V7.80823C34.0316 7.54119 33.8149 7.32471 33.5476 7.32471L32.0982 7.32471C31.831 7.32471 31.6143 7.54119 31.6143 7.80823Z' fill='url(%23paint3_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M41.9395 7.80823V9.2566C41.9395 9.52364 42.1562 9.74024 42.4234 9.74024H43.8728C44.1401 9.74024 44.3568 9.52364 44.3568 9.2566V7.80823C44.3568 7.54119 44.1401 7.32471 43.8728 7.32471L42.4234 7.32471C42.1562 7.32471 41.9395 7.54119 41.9395 7.80823Z' fill='url(%23paint4_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M52.5076 7.80823V9.2566C52.5076 9.52364 52.7243 9.74024 52.9916 9.74024H54.441C54.7082 9.74024 54.9249 9.52364 54.9249 9.2566V7.80823C54.9249 7.54119 54.7082 7.32471 54.441 7.32471L52.9916 7.32471C52.7243 7.32471 52.5076 7.54119 52.5076 7.80823Z' fill='url(%23paint5_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M62.8331 7.80823V9.2566C62.8331 9.52364 63.0493 9.74024 63.3165 9.74024H64.7664C65.0332 9.74024 65.2504 9.52364 65.2504 9.2566V7.80823C65.2504 7.54119 65.0332 7.32471 64.7664 7.32471L63.3165 7.32471C63.0493 7.32471 62.8331 7.54119 62.8331 7.80823Z' fill='url(%23paint6_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M73.1583 7.80823V9.2566C73.1583 9.52364 73.3745 9.74024 73.6417 9.74024H75.0916C75.3584 9.74024 75.5756 9.52364 75.5756 9.2566V7.80823C75.5756 7.54119 75.3584 7.32471 75.0916 7.32471L73.6417 7.32471C73.3745 7.32471 73.1583 7.54119 73.1583 7.80823Z' fill='url(%23paint7_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M83.4835 7.80823V9.2566C83.4835 9.52364 83.6997 9.74024 83.9669 9.74024H85.4168C85.6836 9.74024 85.9008 9.52364 85.9008 9.2566V7.80823C85.9008 7.54119 85.6836 7.32471 85.4168 7.32471L83.9669 7.32471C83.6997 7.32471 83.4835 7.54119 83.4835 7.80823Z' fill='url(%23paint8_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M93.8087 7.80823V9.2566C93.8087 9.52364 94.0249 9.74024 94.2921 9.74024H95.742C96.0088 9.74024 96.226 9.52364 96.226 9.2566V7.80823C96.226 7.54119 96.0088 7.32471 95.742 7.32471L94.2921 7.32471C94.0249 7.32471 93.8087 7.54119 93.8087 7.80823Z' fill='url(%23paint9_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M104.134 7.80823V9.2566C104.134 9.52364 104.35 9.74024 104.617 9.74024H106.067C106.334 9.74024 106.551 9.52364 106.551 9.2566V7.80823C106.551 7.54119 106.334 7.32471 106.067 7.32471L104.617 7.32471C104.35 7.32471 104.134 7.54119 104.134 7.80823Z' fill='url(%23paint10_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M114.702 7.80823V9.2566C114.702 9.52364 114.918 9.74024 115.185 9.74024L116.635 9.74024C116.902 9.74024 117.119 9.52364 117.119 9.25659V7.80823C117.119 7.54119 116.902 7.32471 116.635 7.32471L115.185 7.32471C114.918 7.32471 114.702 7.54119 114.702 7.80823Z' fill='url(%23paint11_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M125.027 7.80823V9.25659C125.027 9.52364 125.243 9.74024 125.511 9.74024L126.961 9.74024C127.227 9.74024 127.445 9.52364 127.445 9.25659V7.80823C127.445 7.54119 127.227 7.32471 126.961 7.32471L125.511 7.32471C125.243 7.32471 125.027 7.54119 125.027 7.80823Z' fill='url(%23paint12_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M135.352 7.80823V9.25659C135.352 9.52364 135.569 9.74024 135.836 9.74024H137.286C137.553 9.74024 137.77 9.52364 137.77 9.25659V7.80823C137.77 7.54119 137.553 7.32471 137.286 7.32471L135.836 7.32471C135.569 7.32471 135.352 7.54119 135.352 7.80823Z' fill='url(%23paint13_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M145.678 7.80823V9.25659C145.678 9.52364 145.894 9.74024 146.161 9.74024H147.611C147.878 9.74024 148.095 9.52364 148.095 9.25659V7.80823C148.095 7.54119 147.878 7.32471 147.611 7.32471L146.161 7.32471C145.894 7.32471 145.678 7.54119 145.678 7.80823Z' fill='url(%23paint14_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M156.003 7.80823V9.25659C156.003 9.52364 156.219 9.74024 156.486 9.74024H157.936C158.203 9.74024 158.42 9.52364 158.42 9.25659V7.80823C158.42 7.54119 158.203 7.32471 157.936 7.32471L156.486 7.32471C156.219 7.32471 156.003 7.54119 156.003 7.80823Z' fill='url(%23paint15_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M166.328 7.80823V9.25659C166.328 9.52363 166.544 9.74024 166.811 9.74024H168.261C168.528 9.74024 168.745 9.52363 168.745 9.25659V7.80823C168.745 7.54119 168.528 7.32471 168.261 7.32471L166.811 7.32471C166.544 7.32471 166.328 7.54119 166.328 7.80823Z' fill='url(%23paint16_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M176.896 7.80823V9.25659C176.896 9.52363 177.112 9.74023 177.38 9.74023H178.83C179.096 9.74023 179.313 9.52363 179.313 9.25659V7.80823C179.313 7.54119 179.096 7.32471 178.83 7.32471L177.38 7.32471C177.112 7.32471 176.896 7.54119 176.896 7.80823Z' fill='url(%23paint17_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M187.221 7.80823V9.25659C187.221 9.52363 187.438 9.74023 187.705 9.74023H189.155C189.421 9.74023 189.639 9.52363 189.639 9.25659V7.80823C189.639 7.54119 189.421 7.32471 189.155 7.32471L187.705 7.32471C187.438 7.32471 187.221 7.54119 187.221 7.80823Z' fill='url(%23paint18_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M199.639 7.80824V9.2566C199.639 9.52364 199.855 9.74024 200.123 9.74024H201.572C201.839 9.74024 202.056 9.52364 202.056 9.2566V7.80824C202.056 7.54119 201.839 7.32472 201.572 7.32472L200.123 7.32472C199.855 7.32472 199.639 7.54119 199.639 7.80824Z' fill='url(%23paint19_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M209.964 7.80824V9.2566C209.964 9.52364 210.181 9.74024 210.448 9.74024L211.897 9.74024C212.164 9.74024 212.381 9.52364 212.381 9.2566V7.80824C212.381 7.54119 212.164 7.32471 211.897 7.32471L210.448 7.32471C210.181 7.32471 209.964 7.54119 209.964 7.80824Z' fill='url(%23paint20_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M220.289 7.80823V9.2566C220.289 9.52364 220.506 9.74024 220.773 9.74024L222.222 9.74024C222.49 9.74024 222.706 9.52364 222.706 9.2566V7.80823C222.706 7.54119 222.49 7.32471 222.222 7.32471L220.773 7.32471C220.506 7.32471 220.289 7.54119 220.289 7.80823Z' fill='url(%23paint21_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M230.614 7.80823V9.2566C230.614 9.52364 230.831 9.74024 231.098 9.74024H232.548C232.815 9.74024 233.032 9.52364 233.032 9.2566V7.80823C233.032 7.54119 232.815 7.32471 232.548 7.32471L231.098 7.32471C230.831 7.32471 230.614 7.54119 230.614 7.80823Z' fill='url(%23paint22_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M240.939 7.80823V9.2566C240.939 9.52364 241.156 9.74024 241.423 9.74024H242.873C243.14 9.74024 243.357 9.52364 243.357 9.2566V7.80823C243.357 7.54119 243.14 7.32471 242.873 7.32471L241.423 7.32471C241.156 7.32471 240.939 7.54119 240.939 7.80823Z' fill='url(%23paint23_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M251.508 7.80823V9.2566C251.508 9.52364 251.724 9.74024 251.992 9.74024H253.441C253.708 9.74024 253.925 9.52364 253.925 9.2566V7.80823C253.925 7.54119 253.708 7.32471 253.441 7.32471L251.992 7.32471C251.724 7.32471 251.508 7.54119 251.508 7.80823Z' fill='url(%23paint24_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M261.833 7.80823V9.2566C261.833 9.52364 262.049 9.74024 262.317 9.74024H263.766C264.033 9.74024 264.25 9.52364 264.25 9.2566V7.80823C264.25 7.54119 264.033 7.32471 263.766 7.32471L262.317 7.32471C262.049 7.32471 261.833 7.54119 261.833 7.80823Z' fill='url(%23paint25_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M272.158 7.80823V9.2566C272.158 9.52364 272.374 9.74024 272.642 9.74024H274.092C274.358 9.74024 274.576 9.52364 274.576 9.2566L274.576 7.80823C274.576 7.54119 274.358 7.32471 274.092 7.32471L272.642 7.32471C272.374 7.32471 272.158 7.54119 272.158 7.80823Z' fill='url(%23paint26_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M282.483 7.80823V9.2566C282.483 9.52364 282.7 9.74024 282.967 9.74024H284.417C284.684 9.74024 284.901 9.52364 284.901 9.2566V7.80823C284.901 7.54119 284.684 7.32471 284.417 7.32471L282.967 7.32471C282.7 7.32471 282.483 7.54119 282.483 7.80823Z' fill='url(%23paint27_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M292.809 7.80823L292.809 9.2566C292.809 9.52364 293.025 9.74024 293.292 9.74024H294.742C295.009 9.74024 295.226 9.52364 295.226 9.2566V7.80823C295.226 7.54119 295.009 7.32471 294.742 7.32471L293.292 7.32471C293.025 7.32471 292.809 7.54119 292.809 7.80823Z' fill='url(%23paint28_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M303.134 7.80823L303.134 9.2566C303.134 9.52364 303.35 9.74024 303.617 9.74024H305.067C305.334 9.74024 305.551 9.52364 305.551 9.2566V7.80823C305.551 7.54119 305.334 7.32471 305.067 7.32471L303.617 7.32471C303.35 7.32471 303.134 7.54119 303.134 7.80823Z' fill='url(%23paint29_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M313.702 7.80823L313.702 9.2566C313.702 9.52364 313.918 9.74024 314.185 9.74024L315.635 9.74024C315.902 9.74024 316.119 9.52364 316.119 9.25659V7.80823C316.119 7.54119 315.902 7.32471 315.635 7.32471L314.185 7.32471C313.918 7.32471 313.702 7.54119 313.702 7.80823Z' fill='url(%23paint30_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M324.027 7.80823V9.25659C324.027 9.52364 324.243 9.74024 324.511 9.74024L325.961 9.74024C326.227 9.74024 326.445 9.52364 326.445 9.25659V7.80823C326.445 7.54119 326.227 7.32471 325.961 7.32471L324.511 7.32471C324.243 7.32471 324.027 7.54119 324.027 7.80823Z' fill='url(%23paint31_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M334.352 7.80823V9.25659C334.352 9.52364 334.569 9.74024 334.836 9.74024H336.286C336.553 9.74024 336.77 9.52364 336.77 9.25659L336.77 7.80823C336.77 7.54119 336.553 7.32471 336.286 7.32471L334.836 7.32471C334.569 7.32471 334.352 7.54119 334.352 7.80823Z' fill='url(%23paint32_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M344.678 7.80823V9.25659C344.678 9.52364 344.894 9.74024 345.161 9.74024H346.611C346.878 9.74024 347.095 9.52364 347.095 9.25659L347.095 7.80823C347.095 7.54119 346.878 7.32471 346.611 7.32471L345.161 7.32471C344.894 7.32471 344.678 7.54119 344.678 7.80823Z' fill='url(%23paint33_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M355.003 7.80823V9.25659C355.003 9.52364 355.219 9.74024 355.486 9.74024H356.936C357.203 9.74024 357.42 9.52364 357.42 9.25659L357.42 7.80823C357.42 7.54119 357.203 7.32471 356.936 7.32471L355.486 7.32471C355.219 7.32471 355.003 7.54119 355.003 7.80823Z' fill='url(%23paint34_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M365.328 7.80823V9.25659C365.328 9.52363 365.544 9.74024 365.811 9.74024H367.261C367.528 9.74024 367.745 9.52363 367.745 9.25659V7.80823C367.745 7.54119 367.528 7.32471 367.261 7.32471L365.811 7.32471C365.544 7.32471 365.328 7.54119 365.328 7.80823Z' fill='url(%23paint35_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M375.896 7.80823V9.25659C375.896 9.52363 376.112 9.74023 376.38 9.74023H377.83C378.096 9.74023 378.313 9.52363 378.313 9.25659V7.80823C378.313 7.54119 378.096 7.32471 377.829 7.32471L376.38 7.32471C376.112 7.32471 375.896 7.54119 375.896 7.80823Z' fill='url(%23paint36_linear_8295_70635)'/%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M386.221 7.80823V9.25659C386.221 9.52363 386.438 9.74023 386.705 9.74023H388.155C388.421 9.74023 388.639 9.52363 388.639 9.25659V7.80823C388.639 7.54119 388.421 7.32471 388.155 7.32471L386.705 7.32471C386.438 7.32471 386.221 7.54119 386.221 7.80823Z' fill='url(%23paint37_linear_8295_70635)'/%3E%3Cdefs%3E%3ClinearGradient id='paint0_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint1_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint2_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint3_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint4_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint5_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint6_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint7_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint8_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint9_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint10_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint11_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint12_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint13_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint14_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint15_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint16_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint17_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint18_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint19_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint20_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint21_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint22_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint23_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint24_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint25_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint26_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint27_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint28_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint29_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint30_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint31_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint32_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint33_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint34_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint35_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint36_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3ClinearGradient id='paint37_linear_8295_70635' x1='-47.5' y1='8.99989' x2='332' y2='8.99989' gradientUnits='userSpaceOnUse'%3E%3Cstop stop-color='%2329B5E8' stop-opacity='.8'/%3E%3Cstop offset='1' stop-color='%2329B5E8' stop-opacity='0'/%3E%3C/linearGradient%3E%3C/defs%3E%3C/svg%3E%0A\")}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:first-child{position:relative;z-index:3}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:absolute;height:100%;width:100%;top:0;left:-24px}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{content:\"\";display:block;z-index:1;position:absolute;top:-64px;left:0;width:150%;height:calc(100% + 160px);background-color:rgb(32 44 53 / .9)}.sc-body__content .heading-3-v2,.sc-hero__headline .heading-1-v2{text-transform:none}.sc-body__content span.snowflake-image-caption{display:block!important;font-style:italic}.sc-body__content .snowflake-text p+ul{margin-top:24px!important;padding-left:16px!important}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#e9eaeb!important;font-size:16px}.white-blue-text-color .snowflake-title-v2.solution-center-hero__certification.is-large .snowflake-typographyv2\u003Espan.snowflake-title-v2-line{color:#fff!important;font-size:18px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child{display:flex;justify-content:flex-start;align-items:center;gap:8px}.solution-center-hero__certification\u003E.snowflake-title-v2-line\u003Espan:first-child::before{content:\"\";display:inline-block;width:16px;height:16px;background-image:url(\"data:image/svg+xml,%3Csvg width='16' height='16' viewBox='0 0 16 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M8 0C3.58146 0 0 3.58146 0 8C0 12.4185 3.58146 16 8 16C12.4185 16 16 12.4185 16 8C16 3.58146 12.4185 0 8 0ZM12.7184 5.91984L7.33471 11.3026C7.31293 11.3244 7.31293 11.3454 7.29198 11.3454L7.20653 11.4308C6.94933 11.688 6.54132 11.7525 6.21962 11.6235C6.11238 11.5808 6.00514 11.5163 5.9197 11.4308L5.83425 11.3454C5.83425 11.3454 5.83425 11.3236 5.81246 11.3236L3.28149 8.79347C2.93799 8.44997 2.93799 7.87107 3.28149 7.50664L3.36694 7.42119C3.71044 7.07769 4.28934 7.07769 4.65377 7.42119L6.58401 9.35143L11.3877 4.5477C11.7312 4.2042 12.3101 4.2042 12.6746 4.5477L12.76 4.63315C13.0826 4.99758 13.0828 5.55541 12.7184 5.91984Z' fill='%230E8A16'/%3E%3C/svg%3E%0A\");background-size:contain;background-repeat:no-repeat;background-color:#fff;border-radius:100%}.sc-hero__byline{padding-top:8px}.sc-hero__byline p{color:#e2e2e2;margin-top:0!important}.sc-hero pre[class*=language-]{overflow:visible}.snowflake-code-snippet,.snowflake-code-snippet code,.snowflake-code-snippet pre{font-size:16px}.sc-hero__code-snippet:not(pre)\u003Ecode[class*=language-],.sc-hero__code-snippet pre[class*=language-]{background:0 0}.sc-hero__code-snippet{opacity:.8;background-color:transparent!important;position:absolute;top:0;right:0;width:100%;animation:240s linear 1s forwards slow-scroll}.sc-hero__button-container .snowflake-flexible-column-container-items{padding:0 0 24px;margin-top:-8px;margin-left:24px}.sc-sidebar__partner-logo{width:100%;max-width:140px;margin-top:8px}.sc-sidebar__partner-logo .cmp-image__image{border-radius:0}.sc-tag-cluster.snowflake-text ul{list-style-type:none;padding:0;display:flex;flex-wrap:wrap;gap:8px;margin:0}.sc-tag-cluster.snowflake-text li{color:#373f41;border-radius:4px;display:inline-block;padding:6px;text-transform:uppercase;letter-spacing:1px;font-size:12px!important;line-height:12px!important;margin:0!important;background-color:#f3f3f3}.sc-body .share-icon svg{height:24px;cursor:pointer}.sc-body .share-icon svg:hover path{fill:var(--ui-02)}.sc-overview__webinar-promo-banner{align-items:center;border:1px solid #ccc;padding:var(--spacing-02)}.sc-overview__webinar-promo-banner .snowflake-content-chip-image{max-width:32px;margin-right:var(--spacing-02);line-height:0}.sc-overview__webinar-promo-banner .snowflake-content-chip-image__image,.summit-speaker-card .snowflake-card-v2-advanced-image__image{aspect-ratio:1}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{font-size:14px;font-family:Lato,sans-serif}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child){font-weight:400}.sc-overview__webinar-promo-banner .snowflake-content-chip-button .snowflake-button-container{font-size:14px!important}.diagram-group__button{position:absolute;bottom:24px;right:24px;background-color:#212c35!important}.section--mountains-bottom,.summit-hp-hero{position:relative}.sc-cert-banner{background-color:#212d35;border-radius:8px;padding:24px;overflow:hidden}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;align-items:center}:root{--text-secondary:#706f6f;--summit-bg-ltblue:#eaf8fd;--summit-bg-blue:#249edc;--summit-border:#d2d1d4;--summit-border-radius:8px;--summit-card-padding:32px;--summit-card-padding-sm:28px}.section--mountains-bottom::after,.section--mountains-bottom::before{content:\"\";display:block;position:absolute;bottom:-1px;max-width:400px;background-size:100% auto;height:100%;width:30%;line-height:0;background-repeat:no-repeat}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container{justify-content:center;align-items:center}.button-group\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;margin:0 8px!important}.button-group .snowflake-button-container{font-family:Texta,sans-serif}.section--summit-bg-ltblue{background-color:var(--summit-bg-ltblue)}.section--summit-bg-blue,.summit-hero-secondary{background-color:var(--summit-bg-blue)}.section--mountains-bottom::before{left:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M401.523 308.761H0V0L181.63 182.431L228.479 135.531L401.523 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom left}.section--mountains-bottom::after{right:0;background-image:url(\"data:image/svg+xml,%3Csvg width='402' height='309' viewBox='0 0 402 309' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M0 308.761H401.523V0L219.893 182.431L173.044 135.531L0 308.761Z' fill='%23249EDC'/%3E%3C/svg%3E%0A\");background-position:bottom right}.summit-hp-hero{overflow:hidden}.summit-hero__bg-video{position:absolute;top:50%;left:50%;width:120%;height:100%;opacity:.3;transform:translate(-50%,-50%)}.summit-hero__bg-svg,.summit-prefooter__bg-image,.summit-secondary-hero__bg-image{position:absolute;bottom:0;left:0;width:100%}.summit-hp-promo-banner__headline .heading-4-v2{font-weight:900}.summit-hero-secondary .hero-lottie__left{position:absolute;bottom:0;left:0;width:30%;line-height:0}.summit-timeline__card::after,.summit-timeline__card::before{bottom:0;left:50%;position:absolute;display:block;background-color:var(--ui-01);content:\"\"}.summit-hero-secondary .snowflake-text p{font-size:24px!important;line-height:32px!important;max-width:720px;margin:0 auto}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;justify-content:center}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:auto!important;max-width:25%}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid #fff}.summit-timeline__card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding);position:relative;background-color:#fff}.summit-timeline__card::before{width:20px;height:20px;border-radius:100%;transform:translate(-50%,50%)}.summit-timeline__card::after{width:3px;height:50px;transform:translate(-50%,100%)}.summit-timeline-card__icon{width:48px;height:48px}.summit-timeline-card__headline .heading-3-v2{font-size:32px}.faq-group{border:1px solid var(--ui-12);border-radius:4px;background-color:#fff}.faq-group__question{padding:24px}.faq-group__question:hover{color:var(--ui-01);cursor:pointer}.faq-group__question .heading-4-v2,.faq-group__question .heading-5-v2{position:relative;padding-right:64px}.faq-group__question .heading-4-v2::after,.faq-group__question .heading-5-v2::after{content:\"\";display:block;width:32px;height:32px;background-image:url(\"data:image/svg+xml,%3Csvg width='29' height='16' viewBox='0 0 29 16' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M14.16 14.6807C14.2537 14.7957 14.3719 14.8884 14.506 14.952C14.64 15.0157 14.7866 15.0487 14.935 15.0487C15.0834 15.0487 15.2299 15.0157 15.3639 14.952C15.498 14.8884 15.6162 14.7957 15.71 14.6807V14.6807L28.51 2.00068C29.07 1.43068 29.07 .92068 28.51 .44068C27.95 -.0393204 27.43 -.11932 26.96 .44068L14.94 12.0007L2.99996 .45068C2.90725 .322624 2.7855 .218374 2.6447 .146483C2.50389 .0745926 2.34805 .0371094 2.18996 .0371094C2.03187 .0371094 1.87603 .0745926 1.73522 .146483C1.59442 .218374 1.47267 .322624 1.37996 .45068C.819961 .93068 .819961 1.45068 1.37996 2.01068L14.16 14.6807Z' fill='black'/%3E%3C/svg%3E%0A\");background-size:80% auto;background-repeat:no-repeat;background-position:center;position:absolute;top:-2px;right:0;transition:.3s 150ms}.faq-group__question .heading-5-v2::after{top:-4px}.faq-group__answer{max-height:0;overflow:hidden;width:95%;padding:0 24px;transition:.5s}.faq-group__answer\u003Espan{display:block;padding-bottom:24px}.is-open .faq-group__answer{max-height:600px;transition:1s}.is-open .faq-group__question .heading-4-v2::after,.is-open .faq-group__question .heading-5-v2::after{transform:rotate(180deg);transition:.3s}.summit-agenda{box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);border-radius:8px;background-color:#fff;max-width:980px;margin-left:auto;margin-right:auto;padding:40px;width:90%}.agenda-item{border-radius:8px;background-color:#d4f0fa;padding:16px;border-left:4px solid var(--ui-01);position:relative}.summit-pricing-block__tile.is-past,.summit-pricing-block__tile.is-upcoming{pointer-events:none;border-color:#d2d1d4}p.agenda-item__time{width:25%;font-family:Texta!important;font-size:32px!important;font-weight:900!important;text-transform:uppercase!important;max-width:140px}@media screen and (max-width:991px){#partnerResources .section--resource-hub .snowflake-button-link .snowflake-button-container{font-size:14px!important;line-height:20px!important;margin-top:4px}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items{display:flex;flex-direction:column}#industryPartnerSlider\u003E.snowflake-flexible-column-container-items\u003Ediv{width:100%}.sc-cert-banner__left{text-align:center}.sc-cert-banner__left .solution-center-hero__certification .snowflake-title-v2-line{justify-content:center}.summit-hero__bg-video{width:200%}.summit-leadership-grid .snowflake-flexible-column-container-items{grid-template-columns:repeat(2,1fr)}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:50%!important;max-width:50%!important}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:none!important}.summit-agenda{padding:24px}p.agenda-item__time{font-size:24px!important;width:auto;white-space:nowrap;padding-right:24px}}.agenda-item\u003Espan{display:flex;align-items:center}.summit-add-on-block,.summit-pricing-block{border:1px solid #d2d1d4;border-radius:8px;overflow:hidden;box-shadow:2px 4px 10px 0 rgb(156 156 156 / .52);background-color:#fff}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 20px 20px}.summit-pricing-block__tile{padding:24px 20px;border-radius:4px;background:#fff;border:1px solid var(--ui-01);position:relative;transition:background-color .3s}.summit-pricing-block__tile:hover{background-color:var(--ui-01);transition:background-color .3s}.summit-pricing-block__tile.is-past{background-color:#d4f0fa}.summit-pricing-block__tile:hover .black-blue-text-color .snowflake-title-v2-line{color:#fff!important;transition:color .3s}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::after,.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::after,.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container::before,.summit-pricing-block__tile.is-past .snowflake-content-chip-button,.summit-pricing-block__tile.is-upcoming .snowflake-content-chip-button,.summit-speaker-card .snowflake-card-v2-advanced-tag-indicator{display:none}.summit-pricing-block__tile.is-past .black-blue-text-color .snowflake-title-v2-line{color:#7cc7eb!important}.summit-pricing-block__tile.is-upcoming .black-blue-text-color .snowflake-title-v2-line{color:#8c8c8c!important}.summit-pricing-block__aside{background-color:#d4f0fa;border:1px solid #d2d1d4;border-radius:8px;padding:24px;width:100%}.summit-pricing-block__aside li::marker{color:var(--ui-01)}.summit-pricing-block__aside-headline .heading-5-v2{font-weight:900;margin-bottom:12px}.summit-pricing-block__header{background:#000;padding:24px 40px}.summit-pricing-block__header .heading-4-v2{font-weight:900;letter-spacing:.5px}.bwwidth100,.snowflake-mega-nav-dropdown-footer-content,.summit-pricing-block__tile .black-blue-text-color{width:100%}.summit-pricing-block__tile .heading-5-v2{position:static}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:first-child{text-transform:uppercase;font-weight:900!important;letter-spacing:.25px;font-size:24px!important}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:nth-child(2){margin-top:8px;font-family:Lato,sans-serif;font-size:14px;font-style:normal;font-weight:400;line-height:16px}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{font-weight:900!important;font-size:40px!important}.snowflake-mega-nav-nav-item\u003Ea:hover .snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title,.summit-pricing-block__tile:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:var(--ui-01)!important}.summit-pricing-block__tile:hover:not(.is-upcoming):not(.is-past) .heading-5-v2 span.snowflake-title-v2-line:last-child{color:#fff!important}.summit-pricing-block__tile.is-past .heading-5-v2 span.snowflake-title-v2-line:last-child{text-decoration:line-through}.summit-pricing-block__tile .snowflake-content-chip-button{margin-top:0;white-space:nowrap;display:none}.snowflake-card-v2-advanced.no-link{pointer-events:none!important}.snowpro-card{border:1px solid var(--summit-border);border-radius:var(--summit-border-radius);padding:var(--summit-card-padding-sm);display:flex;height:100%}.snowpro-card__headline{margin:24px 0 12px}.snowpro-card__pricing{margin-top:48px}.snowpro-card .snowflake-text .snowpro-card__price{color:var(--ui-01);font-weight:900;font-size:40px!important;font-family:Texta,sans-serif}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap}.summit-stat-container\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:not(:last-child){border-right:1px solid var(--summit-border)}.summit-stat-card{padding:0 40px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:first-child{font-size:64px;line-height:52px;margin-bottom:8px}.summit-stat .heading-2-v2 .snowflake-title-v2-line:last-child{font-size:32px;line-height:30px;margin-bottom:16px}.summit-speaker-card .snowflake-card-v2-advanced-title{margin-bottom:var(--spacing-01)}.summit-add-on-card{padding:24px;border:1px solid #d2d1d4;border-radius:8px}.summit-add-on__subhead{padding-left:40px;padding-right:40px}.partner-card__logo-grid,.partner-card__logo-single{padding:40px}.partner-card__logo-grid .snowflake-image-container .cmp-image__image,.partner-card__logo-single .snowflake-image-container .cmp-image__image{border-radius:0;max-width:240px;margin:0 auto}.partner-card\u003E.container,.partner-card\u003E.container\u003E.aem-container,.partner-card\u003E.container\u003E.cmp-container{height:100%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;gap:24px;align-items:stretch}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container{display:flex;flex-direction:row;flex-wrap:wrap;gap:40px 24px;justify-content:center;align-items:center}.partner-card__logo-grid\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important}.partner-card{border-radius:8px;border:1px solid #d2d1d4;overflow:hidden;height:100%;background-color:#fff}.partner-card__header{padding:16px 24px;border-bottom:1px solid #d2d1d4}.partner-card__header.is-purple{background-color:#7d44cf}.partner-card__header h4{display:flex;flex-direction:row!important;align-items:center;gap:12px}.partner-card__header h4::before{vertical-align:middle;content:\"\";display:inline-block;width:20px;height:20px;background-size:contain;background-repeat:no-repeat;background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='black'/%3E%3C/svg%3E%0A\")}.partner-card__header.is-purple h4::before{background-image:url(\"data:image/svg+xml,%3Csvg width='21' height='23' viewBox='0 0 21 23' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M20.0375 12.8374C20.1644 12.439 20.2172 12.0289 20.2077 11.6237C20.193 11.3305 20.1548 11.0373 20.0712 10.7441C19.8196 9.83306 19.223 9.01989 18.3294 8.50724L5.61817 1.2017C3.82388 .173815 1.53618 .784335 .506483 2.56804C-.533615 4.34915 .0797871 6.62351 1.87408 7.65398L8.97715 11.7427L1.87408 15.8201C.0797871 16.8527 -.531016 19.1271 .506483 20.9156C1.53618 22.6941 3.82388 23.302 5.61817 22.2746L18.3294 14.9643C19.1871 14.4728 19.7693 13.7027 20.0375 12.8374Z' fill='white'/%3E%3C/svg%3E%0A\")}.sf-blue-mountains{background-size:90% auto;background-repeat:no-repeat;background-position:center bottom;background-image:url(\"data:image/svg+xml,%3Csvg width='1361' height='410' viewBox='0 0 1361 410' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M1360.25 410L1065.53 114.309L976.256 203.875L773.049 0L364.393 410H1360.25Z' fill='%233AA8DF'/%3E%3Cpath d='M274.778 410L137.467 272.238L.15625 410H274.778Z' fill='%233AA8DF'/%3E%3C/svg%3E%0A\")}.bwalignr,.main-pr-body .bwalignr{text-align:right}.bwblockalignl{margin-left:0;margin-right:auto}.bwcellpmargin{margin-top:0;margin-bottom:0}.bwlistdisc{list-style-type:disc}.bwpadb3{padding-bottom:4px}.bwpadb4{padding-bottom:5px}.bwpadl0{padding-left:0}.bwpadl3{padding-left:15px}.bwpadl6{padding-left:30px}.bwpadl9{padding-left:45px}.bwpadl12{padding-left:60px}.bwpadr0{padding-right:0}.bwtablemarginb{margin-bottom:10px}.bwvertalignb{vertical-align:bottom}.bwvertalignt{vertical-align:top}.bwsinglebottom{border-bottom:1pt solid #000}.bwdoublebottom{border-bottom:2.25pt double #000}.bwwidth1{width:1%}.bwwidth2{width:2%}.bwwidth6{width:6%}.bwwidth7{width:7%}.bwwidth8{width:8%}.bwwidth10{width:10%}.bwwidth12{width:12%}.bwwidth32{width:32%}.bwwidth44{width:44%}.bwwidth72{width:72%}.bwwidth97{width:97%}.main-pr-body{font-size:18px;line-height:26px}.main-pr-body img{display:block;width:100%;height:auto!important;border-radius:var(--small-border-radius)}.main-pr-body table{width:100%;display:block}.main-pr-body tbody{background-color:#f7f7f7}.main-pr-body .bwsinglebottom{border-bottom:1pt solid #000!important}.main-pr-body td.bwwidth44{padding-right:40px}.main-pr-body .bw-release-story{font-family:Lato,sans-serif}.main-pr-body .bw-release-story sup,.snowflake-mega-nav-dropdown-header-content-right a{white-space:nowrap}.main-pr-body .bw-release-story\u003E*,.main-pr-body\u003Espan\u003E*{margin-bottom:2rem!important}.snowflake-text.main-pr-body tbody,.snowflake-text.main-pr-body tbody p{font-size:14px!important;line-height:20px!important;width:100%;display:block}.press-body .snowflake-flexible-column-container-items{gap:var(--spacing-08)}.about-snowflake{border:1px solid #ccc;background-color:var(--ui-background-05);padding:24px;border-radius:8px;margin-top:0}.about-snowflake__logo{max-width:140px;margin-top:16px}.hero--press .snowflake-hero-system-inner{max-width:1408px;margin:0 auto!important}#arcticNavItem{flex-direction:column}#arcticNavItem::before{content:\"Featured Open Source Technologies\";display:block;margin-top:48px;margin-bottom:24px;font-size:16px!important;line-height:16px!important;font-weight:800!important;text-transform:uppercase}@media screen and (min-width:768px){.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:relative;height:100%;top:auto;left:auto;width:auto}.sc-hero__inner\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child::before{background:linear-gradient(180deg,#202c35 -7.5%,#fff0 51.25%,#202c35 107.69%)}.sc-hero__byline\u003Espan{display:flex;flex-wrap:wrap}.sc-hero__byline p:not(:last-child)::after{content:\"|\";margin:0 12px;opacity:.5}.sc-hero__button-container .snowflake-flexible-column-container-items{position:absolute;bottom:0;padding:0;margin:0 24px 0 0}.sc-hero__button-container .hero-watch-the-demo{padding:12px 16px!important;float:right;margin-bottom:48px;background-color:rgb(35 45 54 / .8)}.summit-overview-stat{padding:0 40px}.summit-timeline{border-bottom:3px solid var(--ui-01);margin-bottom:64px}.summit-add-on-block__content,.summit-pricing-block__content{padding:0 40px 40px}#arcticNavItem::before{font-size:12px!important;margin-bottom:8px;margin-top:16px}.snowflake-mega-nav-nav-item-title-wrapper\u003E.snowflake-mega-nav-nav-item-title{line-height:20px!important}.snowflake-card .heading-2.snowflake-title-line{font-size:24px!important;line-height:28px!important}}@media screen and (min-width:992px){.hp-hero__eyebrow a{gap:12px;margin-left:0;margin-right:0}.hp-hero__eyebrow a::after{content:\"\";background-image:url(\"data:image/svg+xml,%3Csvg width='6' height='11' viewBox='0 0 6 11' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath fill-rule='evenodd' clip-rule='evenodd' d='M5.49134 5.79438C5.53447 5.75922 5.56923 5.71489 5.5931 5.66463C5.61697 5.61436 5.62935 5.55941 5.62935 5.50376C5.62935 5.44811 5.61697 5.39316 5.5931 5.34289C5.56923 5.29263 5.53447 5.2483 5.49134 5.21314L.736339 .413136C.522589 .203135 .331339 .203135 .151339 .413136C-.0286612 .623135 -.0586612 .818135 .151339 .994386L4.48634 5.50188L.155089 9.97938C.107068 10.0142 .0679743 10.0598 .0410153 10.1126C.0140562 10.1654 0 10.2238 0 10.2831C0 10.3424 .0140562 10.4009 .0410153 10.4537C.0679743 10.5065 .107068 10.5521 .155089 10.5869C.335089 10.7969 .530089 10.7969 .740089 10.5869L5.49134 5.79438Z' fill='black'/%3E%3C/svg%3E%0A\");display:inline-block;width:12px;height:12px;background-repeat:no-repeat;background-size:auto 100%;background-position:left center}.promo-banner--homepage{padding-top:32px}.homepage-banner-offset-container::after{height:50%}#storyHighlights{padding:2rem}.body-display-v2.snowflake-quote-item-quote-text{line-height:28px!important}.snowflake-hero-system-headline .heading-1-v2{line-height:48px;font-size:54px!important}.sc-overview__webinar-promo-banner .snowflake-content-chip-content{flex-direction:row;justify-content:space-between;align-items:center;width:100%}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .heading-5-v2{flex-direction:row}.sc-overview__webinar-promo-banner .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child)::before{content:\"|\";margin:0 6px}.sc-cert-banner{padding:40px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{margin:0!important;width:50%}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;padding-right:24px}.sc-cert-banner\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:240px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{width:70%;padding-left:40px}.summit-pricing-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{width:30%}.summit-add-on-block__content\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv{width:calc(33.3333% - 24px);margin:0!important;display:flex}.summit-pricing-block__tile .snowflake-content-chip-content{display:flex;flex-direction:row;align-items:center;width:calc(100% - 200px)}.summit-pricing-block__tile .heading-5-v2 span.snowflake-title-v2-line:last-child{position:absolute;top:50%;transform:translate(0,-50%);right:40px}.press-body\u003E.snowflake-flexible-column-container-items\u003Ediv:last-child{position:sticky;top:120px}.snowflake-mega-nav-navigation-title:hover{color:var(--ui-01)}}@media screen and (min-width:1024px){.about-snowflake{padding:28px}.about-snowflake__logo{max-width:none;padding:0 0 0 48px;margin-bottom:0}.hero--press .snowflake-hero-system-layout-70-30 .snowflake-hero-system-content-container{width:85%}.snowflake-hero-system{padding-bottom:var(--spacing-04);padding-top:var(--spacing-07)}.hero--press .display-2-v2{font-size:64px;line-height:56px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container{flex-direction:row;flex-wrap:nowrap;align-items:center}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:last-child{max-width:280px}.about-snowflake\u003E.container\u003E.cmp-container\u003E.aem-container\u003Ediv:first-child{flex-grow:1;margin-bottom:0!important}#polarisNavItem{margin-top:40px}.snowflake-mega-nav-nav-item-description{line-height:18px!important}.snowflake-mega-nav-column-items{gap:var(--spacing-01);grid-gap:var(--spacing-01)}.snowflake-mega-nav-navigation-title{text-transform:none}}div[id*=blueIcon] .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01);padding:8px}div[id*=blueIcon]:hover .snowflake-mega-nav-nav-item-icon__inner{background:var(--ui-01)!important}.snowflake-mega-nav-nav-item-icon__inner{border-radius:4px;background:var(--ui-background-05);padding:6px}.snowflake-mega-nav-nav-item:hover .snowflake-mega-nav-nav-item-icon__inner{background:#fff!important}.snowflake-mega-nav-nav-item-icon.snowflake-image-container{height:40px;width:40px}.snowflake-mega-nav-dropdown-footer-links\u003E.snowflake-button-link\u003E.snowflake-button-container{font-size:16px!important;font-family:Texta!important;font-weight:800!important}.snowflake-mega-nav-dropdown-footer-icon.snowflake-image-container{margin-right:8px;width:40px!important;height:40px!important}#viewAllCapabilities a:hover{background:0 0!important}#platformFooter .snowflake-title-v2 .snowflake-title-v2-line:last-child{font-family:Lato;font-size:14px;font-weight:500}#platformFooter .snowflake-mega-nav-dropdown-footer-links{flex-grow:1;justify-content:flex-end;align-items:center}#platformFooter .snowflake-mega-nav-dropdown-footer-content{flex-direction:row}#offset,#open-source{flex-direction:column;border-top:1px solid #ccc}#offset::before,#open-source::before{content:\" \";display:block;width:100%;font-weight:800!important;font-size:12px!important;line-height:14px;text-transform:uppercase;white-space:nowrap;margin-top:16px;margin-bottom:8px}#open-source::before{content:\"Open Source Technologies\"}.snowflake-mega-nav-dropdown-menu-close-button{margin:var(--spacing-04) 0 var(--spacing-03)}.snowflake-mega-nav-column{gap:var(--spacing-02)!important}.snowflake-mega-nav-nav-item\u003Ea{width:100%;margin-left:-8px;padding:8px;border-radius:4px}.snowflake-mega-nav-nav-item\u003Ea:hover{background-color:var(--ui-background-05)}.snowflake-mega-nav-nav-item-description{margin-top:2px;display:block}#promobanner_overflowBottomDarkBlue::before{content:'';display:block;position:absolute;bottom:0;left:0;width:100%;height:50%;background:#212d35}#promobanner_overflowTopDarkBlue::before{content:'';display:block;position:absolute;top:0;left:0;width:100%;height:50%;background:#212d35}.overview-card\u003Ediv{box-shadow:0 0 14px 0 rgba(0,0,0,.10);background-color:#fff;border-radius:16px;overflow:hidden}.overview-card-text{padding:40px}.overview-card-image img{border-radius:0 !important}.overview-card-text h3,.overview-card-text .heading-3-v2{font-size:18px;line-height:1.1;margin-top:0}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"mega_header":{"additionalClasses":"heap-nav-header","layout":"SIMPLE","id":"container-ef897b5d79","appliedCssClassNames":"snowflake-header-container white",":type":"snowflake-site/components/mega-header",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-d3697bb085",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-8161f6dd62","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-8cd304d948",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"additionalClasses":"nav-platform-sidebar","numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-4555b6c4be",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-0fbbaf7b6f","additionalClasses":"nav-item__platform-parent","linkDescription":"Développez des produits d’IA, des applications et bien plus encore sur une plateforme entièrement gérée qui relie les entreprises du monde entier, en toute sécurité, quel que soit le type ou le volume de leurs données.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/platform/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"La plateforme de Snowflake"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-e07d6d6aef","additionalClasses":"nav-item nav-item--si","linkDescription":"Disponible Toutes vos connaissances. Un agent de confiance.","flag":"Disponible","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/snowflake-cowork/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake CoWork"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_836345186":{"id":"nav-item-7466788642","additionalClasses":"blue-icon","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/analytics/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Analytics"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2":{"id":"nav-item-8c756cd103","additionalClasses":"blue-icon","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"IA"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1314771042":{"id":"nav-item-abda159d23","additionalClasses":"blue-icon","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/data-engineering/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Data engineering"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634":{"id":"nav-item-363e90dec1","additionalClasses":"blue-icon","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/applications-and-collaboration/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Applications et collaboration"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy_2_1792474035":{"id":"nav-item-961d762003","additionalClasses":"blue-icon","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/transactions/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Transactions"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646","nav_item_copy","nav_item_copy_copy_2_836345186","nav_item_copy_copy_2","nav_item_copy_copy_2_1314771042","nav_item_copy_144634","nav_item_copy_copy_2_1792474035"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","navColumnTitle":"Fonctionnalités phares","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-387213e967",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_1686013634":{"id":"nav-item-b421c66563","propertiesId":"testID","linkDescription":"Agent de codage IA natif de Snowflake","flag":"nouveau","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/snowflake-coco/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake CoCo"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-5b9d377421","linkDescription":"Postgres open source entièrement compatible sur Snowflake","flag":"disponible","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/postgres/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Postgres"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-8f8d90756b","propertiesId":"testID","linkDescription":"Un accès instantané aux plus grands LLM","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/cortex/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Cortex AI"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_1504746663":{"id":"nav-item-e5261bfe20","linkDescription":"Déplacement de données simplifié pour l'intégration","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/openflow/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Openflow"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590635":{"id":"nav-item-5222f6832d","linkDescription":"Des données tierces et des produits d’IA à l’essai et à l’achat","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/marketplace/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Marketplace"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_2121336733":{"id":"nav-item-e0ae75e329","linkDescription":"L’environnement de développement interactif des équipes data et IA","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/notebooks/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Notebooks"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-e4772cc9ea","linkDescription":"Des bibliothèques et environnements d’exécution en Python, Java, Scala","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/snowpark/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowpark"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-6106abf86f","linkDescription":"Une collaboration préservant la confidentialité","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/data-clean-rooms/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Data clean rooms"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590_983061516":{"id":"nav-item-d4547b0a0b","linkDescription":"Un environnement pour convertir les scripts Python en applications web","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/product/features/streamlit-in-snowflake/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Streamlit (EN)"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-6a8e427c9c","propertiesId":"workload-nav-1","linkDescription":"La création et la distribution de Snowflake Native App de bout en bout","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/native-apps/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Native Apps"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_1686013634","nav_item_copy_185565","nav_item","nav_item_copy_185565_1504746663","nav_item_copy_660590635","nav_item_copy_660590_2121336733","nav_item_copy_660590","nav_item_copy_1855651246","nav_item_copy_660590_983061516","nav_item_258535199"]},"nav_column_676020780":{"navColumnTitle":"     ","numberOfSubColumns":"one-column","maxWidth":"300","layout":"SIMPLE","id":"container-59ef5d3653",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_511717659":{"id":"nav-item-88be1b6157","linkDescription":"Catalogue d'IA universel","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/product/features/horizon/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Horizon Catalog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_660590":{"id":"nav-item-e75b9e6a7f","linkDescription":"L’interface unifiée pour le développement optimisé de modèles et MLOps","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/product/features/end-to-end-ml-workflows/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Snowflake ML"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_41538387":{"id":"nav-item-d913fc46c2","linkDescription":"Simplifiez vos charges de travail transactionnelles et analytiques en les unifiant dans Snowflake ","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/features/unistore/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Unistore (EN)"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-4e4e8a52d4","additionalClasses":"is-light-gray-icon","linkDescription":"L'observabilité par l'IA pour gérer plus rapidement les problèmes de production","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/observe/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Observe (EN)"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-592997fe61","additionalClasses":"is-light-gray-icon","linkDescription":"Utilisez les moteurs de votre choix sur un seul jeu de données gouverné","flag":"disponible","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/product/use-cases/interoperable-lakehouse/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Interoperable Lakehouse (EN)"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_511717659","nav_item_copy_660590","nav_item_41538387","nav_item_copy","nav_item_copy_copy"]}},":itemsOrder":["nav_column","nav_column_copy_copy","nav_column_676020780"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Produit"},"nav_dropdown_menu_2":{"id":"nav-dropdown-menu-01b6bdfe4a","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-0fe9e5b468",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"navColumnTitle":"Par secteur","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-52eca2edef","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_361384_1796955541":{"id":"nav-item-2e91bca938","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Tous les secteurs (EN)"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-e63ba205c9","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/advertising-media-entertainment/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Publicité, médias et divertissement"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-ef5de1d1bd","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/financial-services/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Services financiers"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-7c3e2af3c6","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/healthcare-and-life-sciences/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Santé et sciences de la vie"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1533429516":{"id":"nav-item-ff7530b612","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/manufacturing/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Industrie"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1444458226":{"id":"nav-item-c62478bade","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/public-sector/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Secteur public"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1149488919":{"id":"nav-item-d1d3400f15","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/retail-consumer-goods/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Retail et grande distribution"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_57417040":{"id":"nav-item-36dfed8c0c","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/technology/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Technologie"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384674":{"id":"nav-item-be7cf0bbf3","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/industries/telecom/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Télécommunications"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_361384":{"id":"nav-item-6edee13c68","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/solutions/industries/travel-hospitality/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Tourisme et hôtellerie (EN)"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_361384_1796955541","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"]},"nav_column_copy":{"navColumnTitle":"Par département","numberOfSubColumns":"one-column","minWidth":"200","layout":"SIMPLE","id":"container-db641f19ac",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-46a1e67b7e","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/departments/finance/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Directions financières"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-b059f1f4b1","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/departments/information-technology/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"IT"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-c79de9c137","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/departments/marketing/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Marketing"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]},"nav_column_833417450":{"navColumnTitle":"Solutions d’accompagnement","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-46d8f82593",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_107772":{"id":"nav-item-3468660c85","linkDescription":"Une migration sécurisée vers une plateforme unifiée","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/migrate-to-the-cloud/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Migration vers l’AI Data Cloud"},"icon":{"id":"icon","height":"64","alt":"Cloud icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1752092772450/nav-icon-cloud.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-4fd2af0e5a","linkDescription":"Des experts Snowflake pour atteindre vos objectifs plus rapidement","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/solutions/professional-services/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake Professional Services"},"icon":{"id":"icon","height":"64","alt":"Migrate icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633192106/nav-icon--migrate.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_107772","nav_item_copy_copy"]},"nav_column_copy_copy":{"navColumnTitle":"Solutions partenaires","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-cf934b63b4",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-61024575c3","linkDescription":"Nos programmes de partenariats produit, solutions et cloud","button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake Partner Network"},"icon":{"id":"icon","height":"64","alt":"Partner Network icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633209119/nav-icon--partner-network.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-2e37db8314","linkDescription":"Des partenaires, apps et solutions pour un déploiement optimisé","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/all-partners/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Trouver un partenaire"},"icon":{"id":"icon","height":"64","alt":"Partner Finder icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633219582/nav-icon--partner-finder.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1970515619":{"id":"nav-item-5dda7248ba","linkDescription":"Nos événements virtuels et en présentiel","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/partners/event-partnership-opportunities/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Devenir partenaire d’événement"},"icon":{"id":"icon","height":"64","alt":"Calendar icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633239518/nav-icon--events.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_1970515619"]}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-e91bc11bcd","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-dbec3fe2ce",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-57d315e5e7",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-a8c1e01e37","additionalClasses":"nav-item__platform-parent-why-sf","linkDescription":"Collaborez localement et partout dans le monde pour révéler de nouvelles perspectives, créer des opportunités commerciales inédites et identifier vos clients avec des expériences fluides.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/why-snowflake/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Pourquoi Snowflake"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item_copy_copy_2_793631646"]},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-187eb9be4c",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-358ec1a5eb","propertiesId":"testID","linkDescription":"Découvrez des études de cas et des vidéos illustrant comment des organisations partout dans le monde utilisent Snowflake.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/customers/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Clients"},"icon":{"id":"icon","height":"64","alt":"Partner Network icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633414597/nav-icon--partner-network.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-edadc79385","propertiesId":"workload-nav-1","linkDescription":"Découvrez comment connecter, partager et intégrer vos données et vos applications sur l’AI Data Cloud.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/why-snowflake/what-is-data-cloud/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"L’AI Data Cloud en détail"},"icon":{"id":"icon","height":"64","alt":"Cloud icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633382450/nav-icon-cloud.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565":{"id":"nav-item-631720f62e","linkDescription":"Assurez-vous une sécurité de bout en bout grâce à des fonctionnalités intégrées et la protection fiable de votre infrastructure cloud.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/why-snowflake/snowflake-security-hub/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Hub de sécurité"},"icon":{"id":"icon","height":"64","alt":"User with security lock icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633346611/user-security-admins-ciso-icon.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-765911122e","additionalClasses":"is-light-gray-icon","linkDescription":"Optimisez la valeur économique de votre solution en réduisant votre TCO et en optimisant son rapport prix/performance.","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/pricing-options/cost-and-performance-optimization/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Optimisation des coûts et des performances"},"icon":{"id":"icon","height":"64","alt":"Cost Optimization icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633358687/nav-icon-cost-optimization-performance.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_185565_2043246825":{"id":"nav-item-3d7b6c34c0","linkDescription":"Des startups créent des applications dans l'AI Data Cloud","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/why-snowflake/startup-program/startup-accelerator/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake pour les startups (EN)"},"icon":{"id":"icon","height":"64","alt":"Launch","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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_2043246825/icon.coreimg.svg/1781195269193/launch.svg","lazyEnabled":true,"isLcpImage":false,"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_2043246825"]}},":itemsOrder":["nav_column","nav_column_copy_copy"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Pourquoi Snowflake"},"item_1719961362824":{"id":"nav-dropdown-menu-650882f202","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-8f6dfcce02",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy":{"navColumnTitle":"Restez connecté","numberOfSubColumns":"one-column","minWidth":"124","layout":"SIMPLE","id":"container-8bc5f54a48",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-f0a39b12e6","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/blog/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Blog"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_180298689":{"id":"nav-item-4571f735a3","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/events/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Événements"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_1639361946":{"id":"nav-item-e23055ef1d","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/support/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Support (EN)"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_680912746":{"id":"nav-item-05428efcae","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/contact/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Nous contacter"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_180298689","nav_item_1639361946","nav_item_680912746"]},"nav_column_44600420__826130542":{"navColumnTitle":"Découvrez","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-5aa1dc6329",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item_copy":{"id":"nav-item-d827926e7f","linkDescription":"Nos ebooks, podcasts, vidéos, livres blancs...","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/resources/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Bibliothèque de ressources"},"icon":{"id":"icon","height":"64","alt":"Notebooks icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633534453/nav-icon--notebooks.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-31571d5bc6","linkDescription":"Nos offres de formation Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/training/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Formation (EN)"},"icon":{"id":"icon","height":"64","alt":"Training icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633560429/nav-icon--training.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_144634_1984107859":{"id":"nav-item-164d3ec24b","linkDescription":"Des démos animées par des experts pour divers secteurs et cas d’usage","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/webinars/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Webinaires"},"icon":{"id":"icon","height":"64","alt":"Webinars icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1773153892753/nav-icon--webinars.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1438098918":{"id":"nav-item-c3a4eba1fa","linkDescription":"Les certifications techniques professionnelles Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/resources/learn/certifications/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Certifications (EN)"},"icon":{"id":"icon","height":"64","alt":"Certification icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633567051/nav-icon--cert.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_143809":{"id":"nav-item-04785bc4c3","linkDescription":"Des démos hebdomadaires de fonctionnalités clés avec Q&A en direct","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/demo/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Démos live (EN)"},"icon":{"id":"icon","height":"64","alt":"Live Demo icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1759424114915/nav-icon--live-demo.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_333890638":{"id":"nav-item-659ffea482","linkDescription":"Des formations tous niveaux, à la demande ou animées par un formateur","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://learn.snowflake.com/en/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Snowflake University (EN)"},"icon":{"id":"icon","height":"64","alt":"Education icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633573941/nav-icon--education.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945_1598900354":{"id":"nav-item-dc75b08933","linkDescription":"Des ateliers pratiques virtuels dirigés autour de fonctionnalités clés","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/webinars/virtual-hands-on-lab/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Ateliers pratiques (EN)"},"icon":{"id":"icon","height":"64","alt":"Hands-on Labs icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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_1598900354/icon.coreimg.svg/1759424138774/nav-icon--labs.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945_2106466912":{"id":"nav-item-c43959a06f","linkDescription":"Publications académiques écrites par les chercheurs de Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/resources/publications/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Travaux de recherche de Snowflake"},"icon":{"id":"icon","height":"64","alt":"Copy","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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_2106466912/icon.coreimg.svg/1781190308976/copy.svg","lazyEnabled":true,"isLcpImage":false,"width":"65",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_189945":{"id":"nav-item-f0e2fa8de9","linkDescription":"Articles informatifs sur l’IA et la data","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/fundamentals/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Concepts fondamentaux"},"icon":{"id":"icon","height":"64","alt":"Data Sheet","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1781183138045/data-sheet.svg","lazyEnabled":true,"isLcpImage":false,"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_1598900354","nav_item_copy_189945_2106466912","nav_item_copy_189945"]}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Ressources"},"item_1719963657751":{"id":"nav-dropdown-menu-f23b6a2533","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-ed39fad187",":type":"snowflake-site/components/nav/nav-column/nav-column-container",":items":{"nav_column_copy_copy":{"navColumnTitle":"Créez","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-af038826e7",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-fc7096a158","propertiesId":"testID","linkDescription":"Toutes les ressources dont vous avez besoin pour développer et scaler","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Snowflake pour les développeurs (EN)"},"icon":{"id":"icon","height":"64","alt":"Developers icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633594414/nav-icon--devs.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-8a71c9beda","linkDescription":"Des architectures, des cas d’usage et des meilleures pratiques","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/developers/guides/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Guides pour les développeurs"},"icon":{"id":"icon","height":"64","alt":"Solution Center icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1761755207063/nav-icon--solution-center.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-f3c1374097","additionalClasses":"is-light-gray-icon","linkDescription":"Les derniers logiciels, drivers, bibliothèques et de la documentation","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/downloads/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Téléchargements (EN)"},"icon":{"id":"icon","height":"28","alt":"Download icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633607963/nav-icon-download.svg","lazyEnabled":true,"isLcpImage":false,"width":"28",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246","nav_item_copy"]},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Découvrez","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-9ff57885f7",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-e5d8c21b19","propertiesId":"testID","linkDescription":"De la doc et des guides de référence, des tutos et des annonces","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://docs.snowflake.com/fr"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Documentation"},"icon":{"id":"icon","height":"64","alt":"Docs icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633616509/nav-icon--docs.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-fc08ec5780","additionalClasses":"is-light-gray-icon","linkDescription":"Des projets clés que nos ingénieurs soutiennent","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/open-source/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Open Source (EN)"},"icon":{"id":"icon","height":"32","alt":"Open Source icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633637844/nav-icon-open-source.svg","lazyEnabled":true,"isLcpImage":false,"width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_copy":{"id":"nav-item-88757da4eb","additionalClasses":"is-light-gray-icon","linkDescription":"Des cours et ateliers en ligne et en présentiel pour aller plus loin","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/developers/northstar/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Formations pour les développeurs (EN)"},"icon":{"id":"icon","height":"32","alt":"Northstar logo","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633648735/nav-icon--northstar.svg","lazyEnabled":true,"isLcpImage":false,"width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy","nav_item_copy_copy"]},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Restez connecté","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-18c621e27e",":type":"snowflake-site/components/nav/nav-column",":items":{"nav_item":{"id":"nav-item-b389894ba8","propertiesId":"testID","linkDescription":"Nos experts révèlent comment, quand et pourquoi ils créent des features","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://www.snowflake.com/engineering-blog/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Blog engineering (EN)"},"icon":{"id":"icon","height":"32","alt":"Developers icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633654098/nav-icon--developer-center.svg","lazyEnabled":true,"isLcpImage":false,"width":"32",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy_1855651246":{"id":"nav-item-4f1d09c33a","linkDescription":"Des trucs et astuces et des échanges avec des développeurs Snowflake","button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://community.snowflake.com/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Communauté (EN)"},"icon":{"id":"icon","height":"64","alt":"Partner Network icon","src":"/content/experience-fragments/snowflake-site/language-masters/fr/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/1740633661417/nav-icon--partner-network.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"}},":itemsOrder":["nav_item","nav_item_copy_1855651246"]}},":itemsOrder":["nav_column_copy_copy","nav_column_copy_copy_1367930678","nav_column_copy_copy_1101894776"]},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Développeurs"},"item_1718247180324":{"id":"nav-dropdown-menu-e2564d704a","enableDropdown":false,"link_url":"/fr/pricing-options/",":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Tarification"}},":itemsOrder":["item_1719963657751_c_663444255","nav_dropdown_menu_2","item_1719963657751_c","item_1719961362824","item_1719963657751","item_1718247180324"]},"languagenavigation":{"id":"language-navigation-1040f2620d","languageNavItems":[{"title":"English","path":"/en/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/","locale":"en","active":false},{"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/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/","locale":"fr","active":true},{"title":"Español","path":"/es/","locale":"es","active":false},{"title":"Italiano","path":"/it/","locale":"it","active":false}],":type":"snowflake-site/components/nav/language-navigation"},"button":{"id":"button-067abcdf66","heapButtonClasses":["contact_nav","heap-nav-contact"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"/fr/contact-sales/"},"linkTargetContentType":"GENERIC","appliedCssClassNames":"snowflake-button-secondary snowflake-button-blue snowflake-button-compact",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Contact"},"button_288358396":{"id":"button-e9bc6edcec","heapButtonClasses":["start_for_free_nav","heap-nav-start-for-free"],"showOutboundIcon":true,"buttonLink":{"valid":true,"url":"https://signup.snowflake.com/"},"linkTargetContentType":"GENERIC","appliedCssClassNames":"snowflake-button-primary snowflake-button-blue snowflake-button-compact",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Essai gratuit"}},":itemsOrder":["nav_mega","languagenavigation","button","button_288358396"]}},":itemsOrder":["markup_editor","mega_header"]}},":itemsOrder":["root"],"classNames":"aem-xf"},"markup_editor_1950346551":{"id":"markup-editor-1018a33920","title":" ","cssUrl":"/conf/snowflake-site/settings/wcm/templates/quickstart-page-template/structure/_jcr_content/root/markup_editor_1950346551.e08bb1bc93839db7.css",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false},"responsivegrid":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},":items":{"quickstart_hero":{"id":"quickstart-hero-c5e32506a8","fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/getting-started-with-dataengineering-ml-using-snowpark-python-fr",":type":"snowflake-site/components/quickstart/quickstart-hero","quickstartHeroFirstCertifiedTag":{"tagText":"Quickstart","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/quickstart","tagIcon":""},"isDeveloperGuidesPage":false,"quickstartHeroTitle":{"lines":["Premiers pas avec le Data Engineering et le Machine Learning à l’aide de Snowpark for Python"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Dash Desai","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"fonctions de ML","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/snowflake-feature/ml-functions","tagIcon":""},"quickstartHeroForkRepoLink":{"id":"button-283168297d","showOutboundIcon":false,"buttonLink":{"valid":true,"attributes":{"target":"_blank"},"url":"https://github.com/Snowflake-Labs/sfquickstarts/tree/master/site/sfguides/src/getting-started-with-dataengineering-ml-using-snowpark-python-fr"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Fork Repo"},"quickstartHeroBreadcrumbs":[{"title":"Premiers pas avec le Data Engineering et le Machine Learning à l’aide de Snowpark for Python","url":"https://www.snowflake.com/content/snowflake-site/global/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr","currentPage":true},{"title":"Guides pour les développeurs","url":"https://www.snowflake.com/content/snowflake-site/global/fr/developers/guides","currentPage":false},{"title":"Snowflake pour les développeurs","url":"https://www.snowflake.com/content/snowflake-site/global/fr/developers","currentPage":false}]},"flexible_column_cont":{"id":"flexible-column-container-6fd04445c1","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-33435ea050",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-1c854cc068","paragraphs":["<!-- ------------------------ -->\n","\u003Ch2\u003EPrésentation\u003C/h2\u003E\n","\u003Cp\u003ECe guide va vous permettre de passer de données brutes à une application interactive capable d’aider une organisation à optimiser l’allocation de son budget publicitaire.\u003C/p\u003E\n","\u003Cp\u003EVoici un résumé de ce que vous allez apprendre dans chacune des étapes de ce guide Quickstart :\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConfiguration de l’environnement\u003C/strong\u003E : utiliser des zones de préparation et des tables pour intégrer et organiser des données brutes issues de S3 dans Snowflake\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EData Engineering\u003C/strong\u003E : utiliser des DataFrames Snowpark for Python pour transformer des données (par exemple avec des méthodes de type grouper par, agréger, faire pivoter et associer) afin de les préparer pour les applications en aval\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPipelines de données\u003C/strong\u003E : utiliser Snowflake Tasks pour convertir un code de pipeline de données en pipelines opérationnels, avec surveillance intégrée\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMachine Learning\u003C/strong\u003E : préparer des données et entraîner un modèle de ML dans Snowflake à l’aide de Snowpark ML, et déployer le modèle en tant que fonction définie par l’utilisateur (UDF) Snowpark\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EApplication Streamlit\u003C/strong\u003E : créer une application interactive à l’aide de Python (aucune expérience en développement Web requise) pour aider à visualiser le retour sur investissement des différents budgets de dépenses publicitaires\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESi vous ne connaissez pas certaines technologies mentionnées ci-dessus, en voici un rapide résumé, accompagné de liens vers la documentation correspondante.\u003C/p\u003E\n","\u003Ch3\u003EQu’est-ce que Snowpark ?\u003C/h3\u003E\n","\u003Cp\u003EIl s’agit d’un ensemble de bibliothèques et d’environnements d’exécution dans Snowflake qui garantit un déploiement et un traitement sécurisé de tout code non-SQL (Python, Java et Scala, par exemple).\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBibliothèques familières côté client\u003C/strong\u003E : Snowpark apporte une programmation de type DataFrame profondément intégrée et des API compatibles OSS aux langages privilégiés par les professionnels des données. Snowpark prend également en charge des API ML, dont ML Modeling (public preview) et ML Operations (private preview), qui garantissent un développement et un déploiement plus efficaces des modèles.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConstructions d’exécution flexibles\u003C/strong\u003E : Snowpark fournit des constructions d’exécution flexibles qui permettent aux utilisateurs d’ajouter et d’exécuter une logique personnalisée. Les développeurs peuvent créer facilement des pipelines de données, des modèles de ML et des applications de données avec des fonctions définies par l’utilisateur et des procédures stockées.\u003C/p\u003E\n","\u003Cp\u003EPour en savoir plus sur Snowpark, cliquez \u003Ca href=\"/fr/data-cloud/snowpark/\"\u003Eici\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowpark.png?v=4816f22b\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EQu’est-ce que Snowpark ML ?\u003C/h3\u003E\n","\u003Cp\u003ESnowpark ML est une nouvelle bibliothèque qui assure un développement de bout en bout plus rapide et plus intuitif du ML dans Snowflake. Snowpark ML compte 2 API : ML Modeling Snowpark (en public preview) et ML Operations Snowpark (en private preview), respectivement, pour le développement et le déploiement de modèles.\u003C/p\u003E\n","\u003Cp\u003ECe guide Quickstart porte sur l’API ML Modeling Snowpark, qui dimensionne le feature engineering et simplifie l’entraînement de modèles de ML dans Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EQu’est-ce que Streamlit ?\u003C/h3\u003E\n","\u003Cp\u003EStreamlit est un framework d’application \u003Ca href=\"https://github.com/streamlit/streamlit\"\u003Eopen source\u003C/a\u003E Python qui permet aux développeurs de créer, de partager et de déployer rapidement et facilement des applications de données. Pour en savoir plus sur Streamlit, cliquez \u003Ca href=\"https://streamlit.io/\"\u003Eici\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EVous allez apprendre :\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComment analyser des données et réaliser des tâches de Data Engineering à l’aide d’API et de classes DataFrame Snowpark\u003C/li\u003E\u003Cli\u003EComment utiliser des bibliothèques Python open source depuis un canal Anaconda Snowflake soigneusement sélectionné\u003C/li\u003E\u003Cli\u003EComment entraîner un modèle de ML à l’aide de Snowpark ML dans Snowflake\u003C/li\u003E\u003Cli\u003EComment créer des fonctions définies par l’utilisateur (UDF) Snowpark Python scalaires et vectorisées, respectivement, pour l’inférence en ligne et hors ligne\u003C/li\u003E\u003Cli\u003EComment créer des tâches Snowflake Tasks pour automatiser des pipelines de données\u003C/li\u003E\u003Cli\u003EComment créer une application Web Streamlit qui utilise des UDF scalaires pour l’inférence basée sur les entrées des utilisateurs\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EConditions préalables\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://git-scm.com/book/en/v2/Getting-Started-Installing-Git\"\u003EGit\u003C/a\u003E doit être installé.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.python.org/downloads/\"\u003EPython 3.9\u003C/a\u003E doit être installé.\n\u003Cul\u003E\u003Cli\u003ERemarque : vous allez créer un environnement Python avec la version 3.9 lors de l’étape \u003Cstrong\u003EPremiers pas\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EVous devez posséder un compte Snowflake comprenant des \u003Ca href=\"https://docs.snowflake.com/fr/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda\"\u003Epackages Anaconda activés par ORGADMIN\u003C/a\u003E. Si vous n’avez pas de compte Snowflake, vous pouvez créer un \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003Ecompte d’essai gratuit\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EVous devez vous connecter à un compte Snowflake avec le rôle ACCOUNTADMIN. Si ce rôle est disponible dans votre environnement, vous pouvez choisir de l’utiliser. Sinon, vous devrez 1) créer un compte d’essai gratuit, 2) utiliser un autre rôle capable de créer une base de données, un schéma, des tables, des zones de préparation, des tâches, des fonctions définies par l’utilisateur et des procédures stockées OU 3) utiliser une base de données et un schéma existants dans lesquels vous pouvez créer les objets mentionnés.\u003C/li\u003E\u003C/ul\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConfiguration de l’environnement\u003C/h2\u003E\n","\u003Ch3\u003ECréer des tables, charger des données et configurer des zones de préparation\u003C/h3\u003E\n","\u003Cp\u003EConnectez-vous à \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/ui-snowsight.html\"\u003ESnowsight\u003C/a\u003E à l’aide de vos identifiants pour créer des tables, charger des données depuis Amazon S3 et configurer des zones de préparation internes Snowflake.\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003Cul\u003E\u003Cli\u003E\n","\u003Cp\u003ESi vous utilisez des noms différents pour les objets créés dans cette section, assurez-vous de mettre à jour les scripts et le code dans les sections suivantes en conséquence.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EPour chaque bloc de script SQL ci-dessous, sélectionnez toutes les instructions du bloc et exécutez-les de haut en bas.\u003C/p\u003E\n\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EExécutez les commandes SQL suivantes pour créer un \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/create-warehouse.html\"\u003Eentrepôt\u003C/a\u003E, une \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/create-database.html\"\u003Ebase de données\u003C/a\u003E et un \u003Ca href=\"https://docs.snowflake.com/fr/sql-reference/sql/create-schema.html\"\u003Eschéma\u003C/a\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE DASH_L; \nCREATE OR REPLACE DATABASE DASH_DB; \nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\n\nUSE DASH_DB.DASH_SCHEMA; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExécutez les commandes SQL suivantes pour créer une table \u003Cstrong\u003ECAMPAIGN_SPEND\u003C/strong\u003E à partir de données hébergées sur un compartiment S3 accessible au public.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE file format csvformat \n  skip_header = 1 \n  type = 'CSV';\n\nCREATE or REPLACE stage campaign_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\n\nCREATE or REPLACE TABLE CAMPAIGN_SPEND ( \n  CAMPAIGN VARCHAR(60), \n  CHANNEL VARCHAR(60), \n  DATE DATE, \n  TOTAL_CLICKS NUMBER(38,0), \n  TOTAL_COST NUMBER(38,0), \n  ADS_SERVED NUMBER(38,0) \n);\n\nCOPY into CAMPAIGN_SPEND \n  from @campaign_data_stage; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExécutez les commandes SQL suivantes pour créer une table \u003Cstrong\u003EMONTHLY_REVENUE\u003C/strong\u003E à partir de données hébergées sur un compartiment S3 accessible au public.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE stage monthly_revenue_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\n\nCREATE or REPLACE TABLE MONTHLY_REVENUE ( \n  YEAR NUMBER(38,0), \n  MONTH NUMBER(38,0), \n  REVENUE FLOAT \n);\n\nCOPY into MONTHLY_REVENUE \n  from @monthly_revenue_data_stage; \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExécutez les commandes SQL suivantes pour créer une table \u003Cstrong\u003EBUDGET_ALLOCATIONS_AND_ROI\u003C/strong\u003E qui contient les six derniers mois d’allocations budgétaires et de retour sur investissement.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI ( \n  MONTH varchar(30), \n  SEARCHENGINE integer, \n  SOCIALMEDIA integer, \n  VIDEO integer, \n  EMAIL integer, \n  ROI float \n)\nCOMMENT = '{"origin":"sf_sit-is", "name":"aiml_notebooks_ad_spend_roi", "version":{"major":1, "minor":0}, "attributes":{"is_quickstart":1, "source":"streamlit"}}';\n\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI) \nVALUES \n('January',35,50,35,85,8.22), \n('February',75,50,35,85,13.90), \n('March',15,50,35,15,7.34), \n('April',25,80,40,90,13.23), \n('May',95,95,10,95,6.246), \n('June',35,50,35,85,8.22); \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EExécutez les commandes suivantes afin de créer des \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/data-load-local-file-system-create-stage\"\u003Ezones de préparation internes\u003C/a\u003E Snowflake pour stocker des procédures stockées, des UDF et des fichiers de modèles de ML.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE STAGE dash_sprocs;\nCREATE OR REPLACE STAGE dash_models;\nCREATE OR REPLACE STAGE dash_udfs;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EVous pouvez également ouvrir le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql\"\u003Esetup.sql\u003C/a\u003E dans Snowsight et exécuter toutes les instructions SQL pour créer les objets et charger les données depuis AWS S3.\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPremiers pas\u003C/h2\u003E\n","\u003Cp\u003ECette section explique comment cloner le référentiel GitHub et configurer votre environnement Snowpark for Python.\u003C/p\u003E\n","\u003Ch3\u003ECloner le référentiel GitHub\u003C/h3\u003E\n","\u003Cp\u003ELa première étape consiste à cloner le \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003Eréférentiel GitHub\u003C/a\u003E. Le référentiel contient tout le code dont vous aurez besoin pour terminer ce guide Quickstart.\u003C/p\u003E\n","\u003Cp\u003EAvec HTTPS :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOU avec SSH :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Egit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESnowpark for Python\u003C/h3\u003E\n","\u003Cp\u003EPour réaliser les étapes \u003Cstrong\u003EData Engineering\u003C/strong\u003E et \u003Cstrong\u003EMachine Learning\u003C/strong\u003E, vous pouvez installer tous les éléments localement (option 1) ou utiliser Hex (option 2) comme décrit ci-dessous.\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOption 1 – Installation locale\u003C/h4\u003E\n","\u003Cp\u003ECette option va vous permettre de réaliser toutes les étapes de ce guide Quickstart.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 1 :\u003C/strong\u003E téléchargez et installez le programme d’installation miniconda depuis le lien \u003Ca href=\"https://conda.io/miniconda.html\"\u003Ehttps://conda.io/miniconda.html\u003C/a\u003E. \u003Cem\u003E(Avec Python 3.9, vous pouvez également utiliser un autre environnement Python, par exemple, \u003Ca href=\"https://virtualenv.pypa.io/en/latest/\"\u003Evirtualenv\u003C/a\u003E.)\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 2 :\u003C/strong\u003E ouvrez une nouvelle fenêtre de terminal et exécutez-y les commandes suivantes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 3 :\u003C/strong\u003E créez un environnement conda Python 3.9 appelé \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E en exécutant la commande suivante dans la même fenêtre de terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 4 :\u003C/strong\u003E activez l’environnement conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E en exécutant la commande suivante dans la même fenêtre de terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda activate snowpark-de-ml\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 5 :\u003C/strong\u003E installez Snowpark Python et d’autres bibliothèques dans l’environnement conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E depuis le \u003Ca href=\"https://repo.anaconda.com/pkgs/snowflake/\"\u003Ecanal Anaconda Snowflake\u003C/a\u003E en exécutant la commande suivante dans la même fenêtre de terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 6 :\u003C/strong\u003E installez une bibliothèque Streamlit dans l’environnement conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E en exécutant la commande suivante dans la même fenêtre de terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install streamlit\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 7 :\u003C/strong\u003E installez une bibliothèque Snowpark ML dans l’environnement conda \u003Cstrong\u003Esnowpark-de-ml\u003C/strong\u003E en exécutant la commande suivante dans la même fenêtre de terminal.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Epip install snowflake-ml-python\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EÉtape 9 :\u003C/strong\u003E mettez à jour le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E avec les détails et identifiants de votre compte Snowflake.\u003C/p\u003E\n","\u003Cp\u003EVoici un exemple de fichier \u003Cem\u003E\u003Cstrong\u003Econnection.json\u003C/strong\u003E\u003C/em\u003E basé sur les noms d’objets mentionnés à l’étape \u003Cstrong\u003EConfiguration de l’environnement\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-json\"\u003E{\n  "account"   : "<your_account_identifier_goes_here>",\n  "user"      : "<your_username_goes_here>",\n  "password"  : "<your_password_goes_here>",\n  "role"      : "ACCOUNTADMIN",\n  "warehouse" : "DASH_L",\n  "database"  : "DASH_DB",\n  "schema"    : "DASH_SCHEMA"\n}\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOption 2 – Utilisation de Hex\u003C/h4\u003E\n","\u003Cp\u003ESi vous choisissez d’utiliser votre compte \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E existant ou de \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecréer un compte d’essai gratuit de 30 jours\u003C/a\u003E, Snowpark for Python est intégré. Vous n’avez donc pas à créer d’environnement Python, ni à installer Snowpark for Python et d’autres bibliothèques localement sur votre ordinateur. Vous pourrez ainsi réaliser les étapes \u003Cstrong\u003EData Engineering\u003C/strong\u003E et \u003Cstrong\u003EMachine Learning\u003C/strong\u003E de ce guide Quickstart directement dans Hex. (Pour en savoir plus sur le chargement des notebooks de Data Engineering et de Machine Learning dans Hex, consultez les sections correspondantes.)\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EData Engineering\u003C/h2\u003E\n","\u003Cp\u003ELe notebook dont le lien est fourni ci-dessous traite des tâches de Data Engineering suivantes.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EÉtablir une connexion sécurisée entre Snowpark Python et Snowflake\u003C/li\u003E\u003Cli\u003ECharger des données depuis des tables Snowflake dans des DataFrames Snowpark\u003C/li\u003E\u003Cli\u003EEffectuer une analyse exploratoire des données sur les DataFrames Snowpark\u003C/li\u003E\u003Cli\u003EFaire pivoter et associer des données de plusieurs tables à l’aide de DataFrames Snowpark\u003C/li\u003E\u003Cli\u003EAutomatiser des tâches de pipeline de données à l’aide de tâches Snowflake Tasks\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ENotebook de Data Engineering dans Jupyter ou Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPour commencer, suivez les étapes suivantes :\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EDans une fenêtre de terminal, accédez à ce dossier et exécutez \u003Ccode\u003Ejupyter notebook\u003C/code\u003E sur la ligne de commande. (Vous pouvez également utiliser d’autres outils et EDI, comme Visual Studio Code.)\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOuvrez et parcourez les cellules du fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENotebook de Data Engineering dans Hex\u003C/h3\u003E\n","\u003Cp\u003ESi vous choisissez d’utiliser votre compte \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E existant ou de \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecréer un compte d’essai gratuit de 30 jours\u003C/a\u003E, suivez les étapes suivantes pour charger le notebook et créer une connexion de données entre Snowflake et Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImportez le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003ESnowpark_For_Python_DE.ipynb\u003C/a\u003E en tant que projet dans votre compte. Pour plus d’informations sur le processus d’importation, consultez la \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentation correspondante\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnsuite, au lieu d’utiliser le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E pour vous connecter à Snowflake, créez une \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econnexion de données\u003C/a\u003E et utilisez-la dans le notebook de Data Engineering comme illustré ci-dessous.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/hex_data_connection.png?v=4816f22b\" alt=\"Connexion de données HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EDans le notebook, remplacez l’extrait de code suivant :\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Epar...\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPipelines de données\u003C/h2\u003E\n","\u003Cp\u003EVous pouvez également exploiter les transformations de données sous forme de pipelines de données automatisés qui s’exécutent dans Snowflake.\u003C/p\u003E\n","\u003Cp\u003EDans le \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb\"\u003Enotebook de Data Engineering\u003C/a\u003E, une section montre comment créer et exécuter les transformations de données en tant que \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/tasks-intro\"\u003Etâches Snowflake Tasks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EÀ des fins de référence, voici les extraits de code.\u003C/p\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ETâche racine/parent\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ECette tâche automatise le chargement des données relatives aux dépenses de campagne et l’exécution de plusieurs transformations.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef campaign_spend_data_pipeline(session: Session) -> str:\n  # TRANSFORMATIONS DE DONNÉES \n  # Effectuez les actions suivantes pour transformer les données\n\n  # Chargez les données sur les dépenses de campagnes \n  snow_df_spend_t = session.table('campaign_spend')\n\n  # Transformez les données afin de dégager le coût total annuel/mensuel par canal à l’aide des fonctions DataFrame Snowpark group_by() et agg() \n  snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\\\n      with_column_renamed('"YEAR(DATE)"',"YEAR").with_column_renamed('"MONTH(DATE)"',"MONTH").sort('YEAR','MONTH')\n\n  # Transformez les données afin que chaque ligne représente le coût total annuel/mensuel tous canaux confondus à l’aide des fonctions DataFrame Snowpark pivot() et sum() \n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH') \n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select( \n      col("YEAR"), \n      col("MONTH"), \n      col("'search_engine'").as_("SEARCH_ENGINE"), \n      col("'social_media'").as_("SOCIAL_MEDIA"), \n      col("'video'").as_("VIDEO"), \n      col("'email'").as_("EMAIL") \n  )\n\n  # Enregistrez les données transformées \n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\n\n# Enregistrez la fonction de création de pipelines de données en tant que procédure stockée afin de pouvoir l’exécuter en tant que tâche.\nsession.sproc.register( \n  func=campaign_spend_data_pipeline, \n  name="campaign_spend_data_pipeline", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location="@dash_sprocs", \n  replace=True)\n\ncampaign_spend_data_pipeline_task = """ \nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task \n    WAREHOUSE = 'DASH_L' \n    SCHEDULE = '3 MINUTE' \nAS \n    CALL campaign_spend_data_pipeline() \n""" \nsession.sql(campaign_spend_data_pipeline_task).collect() \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Cstrong\u003ETâche enfant/dépendante\u003C/strong\u003E\u003C/h3\u003E\n","\u003Cp\u003ECette tâche automatise le chargement des données relatives aux revenus mensuels, l’exécution de plusieurs transformations et leur association avec les données transformées relatives aux dépenses de campagne.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Edef monthly_revenue_data_pipeline(session: Session) -> str: \n  # Chargez la table des revenus et transformez les données afin de dégager les revenus annuels/mensuels à l’aide des fonctions group_by et agg() \n  snow_df_spend_per_month_t = session.table('spend_per_month') \n  snow_df_revenue_t = session.table('monthly_revenue') s\n  now_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\n\n  # Associez les données relatives aux revenus aux données transformées des dépenses de campagne pour pouvoir charger nos fonctionnalités d’entrée (coût par canal, par exemple) et notre variable cible (revenus, par exemple) dans une seule et même table à des fins d’entraînement de modèle \n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, ["YEAR","MONTH"])\n\n  # Enregistrez dans une nouvelle table pour la tâche suivante \n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\n\n# Enregistrez la fonction de création de pipelines de données en tant que procédure stockée afin de pouvoir l’exécuter en tant que tâche.\nsession.sproc.register( \n  func=monthly_revenue_data_pipeline, \n  name="monthly_revenue_data_pipeline", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location="@dash_sprocs", \n  replace=True)\n\nmonthly_revenue_data_pipeline_task = """ \n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task \n      WAREHOUSE = 'DASH_L' \n      AFTER campaign_spend_data_pipeline_task \n  AS \n      CALL monthly_revenue_data_pipeline() \n  """ \nsession.sql(monthly_revenue_data_pipeline_task).collect() \n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003ELancer des tâches\u003C/h4\u003E\n","\u003Cp\u003ELes tâches Snowflake Tasks ne se lancent pas automatiquement. Vous devez donc exécuter les instructions suivantes pour les lancer/reprendre.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task monthly_revenue_data_pipeline_task resume").collect()\nsession.sql("alter task campaign_spend_data_pipeline_task resume").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch4\u003ESuspendre des tâches\u003C/h4\u003E\n","\u003Cp\u003ESi vous reprenez les tâches ci-dessus, suspendez-les pour éviter toute utilisation inutile des ressources en exécutant les commandes suivantes :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EObservabilité des tâches\u003C/h3\u003E\n","\u003Cp\u003EIl est possible d’afficher ces tâches et leurs \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/tasks-intro#label-task-dag\"\u003EDAG\u003C/a\u003E dans \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs\"\u003ESnowsight\u003C/a\u003E comme illustré ci-dessous.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowflake_tasks.png?v=4816f22b\" alt=\"Observabilité des tâches\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ENotifications d’erreurs pour les tâches\u003C/h3\u003E\n","\u003Cp\u003EVous pouvez également activer les notifications push sur un service de messagerie cloud quand des erreurs se produisent lors de l’exécution de tâches. Pour plus d’informations, consultez la \u003Ca href=\"https://docs.snowflake.com/fr/user-guide/tasks-errors\"\u003Edocumentation correspondante\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EMachine Learning\u003C/h2\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ELe notebook associé ci-dessous traite des tâches de Machine Learning suivantes.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EÉtablir une connexion sécurisée entre Snowpark Python et Snowflake\u003C/li\u003E\u003Cli\u003ECharger des fonctionnalités et une cible depuis une table Snowflake dans un DataFrame Snowpark\u003C/li\u003E\u003Cli\u003EPréparer des fonctionnalités pour l’entraînement de modèles\u003C/li\u003E\u003Cli\u003EEntraîner un modèle de ML à l’aide de Snowpark ML sur Snowflake\u003C/li\u003E\u003Cli\u003ECréer des \u003Ca href=\"https://docs.snowflake.com/fr/developer-guide/snowpark/python/creating-udfs\"\u003Efonctions définies par l’utilisateur (UDF) Python\u003C/a\u003E scalaires et vectorisées (aussi appelées « batchs ») pour toute inférence sur des nouveaux points de données, en ligne et hors ligne, respectivement\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowpark_e2e_ml.png?v=4816f22b\" alt=\"ML de bout en bout\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ENotebook de Machine Learning dans Jupyter ou Visual Studio Code\u003C/h3\u003E\n","\u003Cp\u003EPour commencer, suivez les étapes suivantes :\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EDans une fenêtre de terminal, accédez à ce dossier et exécutez \u003Ccode\u003Ejupyter notebook\u003C/code\u003E sur la ligne de commande. (Vous pouvez également utiliser d’autres outils et EDI, comme Visual Studio Code.)\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOuvrez et exécutez le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ENotebook de Machine Learning dans Hex\u003C/h3\u003E\n","\u003Cp\u003ESi vous choisissez d’utiliser votre compte \u003Ca href=\"https://app.hex.tech/login\"\u003EHex\u003C/a\u003E existant ou de \u003Ca href=\"https://app.hex.tech/signup/quickstart-30\"\u003Ecréer un compte d’essai gratuit de 30 jours\u003C/a\u003E, suivez les étapes suivantes pour charger le notebook et créer une connexion de données entre Snowflake et Hex.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EImportez le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb\"\u003ESnowpark_For_Python_ML.ipynb\u003C/a\u003E en tant que projet dans votre compte. Pour plus d’informations sur le processus d’importation, consultez la \u003Ca href=\"https://learn.hex.tech/docs/versioning/import-export\"\u003Edocumentation correspondante\u003C/a\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EEnsuite, au lieu d’utiliser le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json\"\u003Econnection.json\u003C/a\u003E pour vous connecter à Snowflake, créez une \u003Ca href=\"https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database\"\u003Econnexion de données\u003C/a\u003E et utilisez-la dans le notebook de Machine Learning comme illustré ci-dessous.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/hex_data_connection.png?v=4816f22b\" alt=\"Connexion de données HEX\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EDans le notebook, remplacez l’extrait de code suivant :\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Econnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003Epar...\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EApplication Streamlit\u003C/h2\u003E\n","\u003Ch3\u003EExécuter localement l’application Streamlit\u003C/h3\u003E\n","\u003Cp\u003EDans une fenêtre de terminal, accédez à ce dossier et exécutez la commande suivante pour exécuter l’application Streamlit \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py\"\u003ESnowpark_Streamlit_Revenue_Prediction.py\u003C/a\u003E localement sur votre machine.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-shell\"\u003Estreamlit run Snowpark_Streamlit_Revenue_Prediction.py\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESi tout se passe bien, une fenêtre de navigateur doit s’ouvrir et l’application doit y être chargée, comme illustré ci-dessous.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/app.png?v=4816f22b\" alt=\"Application Streamlit\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EExécuter l’application Streamlit dans Snowflake – Streamlit-in-Snowflake (SiS)\u003C/h3\u003E\n","\u003Cp\u003ESi l’application SiS est activée dans votre compte, suivez les étapes suivantes pour l’exécuter dans Snowsight, et non localement sur votre machine.\u003C/p\u003E\n\u003Cblockquote\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003ECliquez sur \u003Cstrong\u003EStreamlit Apps\u003C/strong\u003E (Applications Streamlit) dans le menu de navigation de gauche.\u003C/li\u003E\u003Cli\u003ECliquez sur \u003Cstrong\u003E+ Streamlit App\u003C/strong\u003E (+ Application Streamlit) dans le coin supérieur droit.\u003C/li\u003E\u003Cli\u003ESaisissez le \u003Cstrong\u003Enom de l’application\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ESélectionnez l’\u003Cstrong\u003Eentrepôt\u003C/strong\u003E et l’\u003Cstrong\u003Eemplacement de l’application\u003C/strong\u003E (Base de données et Schéma) où vous voulez créer l’application Streamlit.\u003C/li\u003E\u003Cli\u003ECliquez sur \u003Cstrong\u003ECreate\u003C/strong\u003E (Créer).\u003C/li\u003E\u003Cli\u003EVous recevrez alors un code pour un exemple d’application Streamlit. Ouvrez le fichier \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py\"\u003ESnowpark_Streamlit_Revenue_Prediction_SiS.py\u003C/a\u003E et copiez-collez le code dans l’exemple d’application Streamlit.\u003C/li\u003E\u003Cli\u003ECliquez sur \u003Cstrong\u003ERun\u003C/strong\u003E (Exécuter) dans le coin supérieur droit.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ESi tout se passe bien, l’application suivante doit apparaître dans Snowsight, comme illustré ci-dessous.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/app_sis.png?v=4816f22b\" alt=\"Streamlit-in-Snowflake\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EEnregistrer des données dans Snowflake\u003C/h3\u003E\n","\u003Cp\u003EDans les deux applications, modifiez les curseurs du budget publicitaire pour voir le retour sur investissement prévu pour ces allocations. Vous pouvez également cliquer sur le bouton \u003Cstrong\u003ESave to Snowflake\u003C/strong\u003E (Enregistrer dans Snowflake) pour enregistrer les allocations actuelles et le retour sur investissement prévu dans la table Snowflake BUDGET_ALLOCATIONS_AND_ROI.\u003C/p\u003E\n","\u003Ch3\u003EDifférences entre les deux applications Streamlit\u003C/h3\u003E\n","\u003Cp\u003ELa principale différence entre le fait d’exécuter l’application Streamlit localement ou dans Snowflake (SiS) est la façon dont vous créez l’objet de session et dont vous y accédez.\u003C/p\u003E\n","\u003Cp\u003ELorsque vous l’exécutez localement, vous créez le nouvel objet de session et vous y accédez comme suit :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Fonction pour créer une session Snowflake à connecter à Snowflake\ndef create_session(): \n    if "snowpark_session" not in st.session_state: \n        session = Session.builder.configs(json.load(open("connection.json"))).create() \n        st.session_state['snowpark_session'] = session \n    else: \n        session = st.session_state['snowpark_session'] \n    return session \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ELorsque vous l’exécutez dans Snowflake (SiS), vous accédez à l’objet de session actuel comme suit :\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esession = snowpark.session._get_active_session()\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ENettoyage\u003C/h2\u003E\n","\u003Cp\u003ESi vous avez lancé/repris les deux tâches \u003Ccode\u003Emonthly_revenue_data_pipeline_task\u003C/code\u003E et \u003Ccode\u003Ecampaign_spend_data_pipeline_task\u003C/code\u003E dans le cadre des sections \u003Cstrong\u003EData Engineering\u003C/strong\u003E ou \u003Cstrong\u003EPipelines de données\u003C/strong\u003E, il est important que vous exécutiez les commandes suivantes pour suspendre ces tâches afin d’éviter toute utilisation inutile des ressources.\u003C/p\u003E\n","\u003Cp\u003EDans le notebook, à l’aide de l’API Snowpark Python\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Esession.sql("alter task campaign_spend_data_pipeline_task suspend").collect()\nsession.sql("alter task monthly_revenue_data_pipeline_task suspend").collect()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDans Snowsight\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003Ealter task campaign_spend_data_pipeline_task suspend;\nalter task monthly_revenue_data_pipeline_task suspend;\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusion et ressources\u003C/h2\u003E\n","\u003Cp\u003EFélicitations ! Vous avez réalisé les tâches de Data Engineering et entraîné un modèle de régression linéaire pour prévoir le futur RSI (retour sur investissement) des budgets variables de dépenses publicitaires sur plusieurs canaux (moteurs de recherche, vidéos, réseaux sociaux et e-mails, par exemple) à l’aide de Snowpark for Python et de scikit-learn. Vous avez ensuite créé une application Streamlit qui utilise ce modèle pour générer des prévisions sur les nouvelles allocations budgétaires sur la base des entrées des utilisateurs.\u003C/p\u003E\n","\u003Cp\u003EN’hésitez pas à nous faire part de vos commentaires sur ce guide Quickstart ! Envoyez-nous vos commentaires à l’aide de ce \u003Ca href=\"https://forms.gle/XKd8rXPUNs2G1yM28\"\u003Eformulaire de commentaires\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EVous avez appris :\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComment analyser des données et réaliser des tâches de Data Engineering à l’aide d’API et de classes DataFrame Snowpark\u003C/li\u003E\u003Cli\u003EComment utiliser des bibliothèques Python open source depuis un canal Anaconda Snowflake soigneusement sélectionné\u003C/li\u003E\u003Cli\u003EComment entraîner un modèle de ML à l’aide de Snowpark ML dans Snowflake\u003C/li\u003E\u003Cli\u003EComment créer des fonctions définies par l’utilisateur (UDF) Snowpark Python scalaires et vectorisées, respectivement, pour l’inférence en ligne et hors ligne\u003C/li\u003E\u003Cli\u003EComment créer des tâches Snowflake Tasks pour automatiser la création de pipelines de données et l’entraînement (nouveau ou non) du modèle\u003C/li\u003E\u003Cli\u003EComment créer une application Web Streamlit qui utilise les UDF scalaires pour l’inférence\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERessources connexes\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn\"\u003ECode source sur GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EAvancé : Guide de Data Engineering avec Snowpark for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAvancé : Guide de Machine Learning avec Snowpark for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md\"\u003EDémos de Snowpark for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/fr/developer-guide/snowpark/python/index.html\"\u003EGuide du développeur Snowpark for Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.streamlit.io/\"\u003EDocuments Streamlit\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Premiers pas avec le Data Engineering et le Machine Learning à l’aide de Snowpark for Python",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n## Présentation\n\n\nCe guide va vous permettre de passer de données brutes à une application interactive capable d’aider une organisation à optimiser l’allocation de son budget publicitaire.\n\nVoici un résumé de ce que vous allez apprendre dans chacune des étapes de ce guide Quickstart :\n\n- **Configuration de l’environnement** : utiliser des zones de préparation et des tables pour intégrer et organiser des données brutes issues de S3 dans Snowflake\n- **Data Engineering** : utiliser des DataFrames Snowpark for Python pour transformer des données (par exemple avec des méthodes de type grouper par, agréger, faire pivoter et associer) afin de les préparer pour les applications en aval\n- **Pipelines de données** : utiliser Snowflake Tasks pour convertir un code de pipeline de données en pipelines opérationnels, avec surveillance intégrée  \n- **Machine Learning** : préparer des données et entraîner un modèle de ML dans Snowflake à l’aide de Snowpark ML, et déployer le modèle en tant que fonction définie par l’utilisateur (UDF) Snowpark\n- **Application Streamlit** : créer une application interactive à l’aide de Python (aucune expérience en développement Web requise) pour aider à visualiser le retour sur investissement des différents budgets de dépenses publicitaires\n\nSi vous ne connaissez pas certaines technologies mentionnées ci-dessus, en voici un rapide résumé, accompagné de liens vers la documentation correspondante.\n\n### Qu’est-ce que Snowpark ?\n\nIl s’agit d’un ensemble de bibliothèques et d’environnements d’exécution dans Snowflake qui garantit un déploiement et un traitement sécurisé de tout code non-SQL (Python, Java et Scala, par exemple).\n\n**Bibliothèques familières côté client** : Snowpark apporte une programmation de type DataFrame profondément intégrée et des API compatibles OSS aux langages privilégiés par les professionnels des données. Snowpark prend également en charge des API ML, dont ML Modeling (public preview) et ML Operations (private preview), qui garantissent un développement et un déploiement plus efficaces des modèles.\n\n**Constructions d’exécution flexibles** : Snowpark fournit des constructions d’exécution flexibles qui permettent aux utilisateurs d’ajouter et d’exécuter une logique personnalisée. Les développeurs peuvent créer facilement des pipelines de données, des modèles de ML et des applications de données avec des fonctions définies par l’utilisateur et des procédures stockées.\n\nPour en savoir plus sur Snowpark, cliquez [ici](/fr/data-cloud/snowpark/).\n\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowpark.png?v=4816f22b)\n\n### Qu’est-ce que Snowpark ML ?\n\nSnowpark ML est une nouvelle bibliothèque qui assure un développement de bout en bout plus rapide et plus intuitif du ML dans Snowflake. Snowpark ML compte 2 API : ML Modeling Snowpark (en public preview) et ML Operations Snowpark (en private preview), respectivement, pour le développement et le déploiement de modèles.\n\nCe guide Quickstart porte sur l’API ML Modeling Snowpark, qui dimensionne le feature engineering et simplifie l’entraînement de modèles de ML dans Snowflake.\n\n### Qu’est-ce que Streamlit ?\n\nStreamlit est un framework d’application [open source](https://github.com/streamlit/streamlit) Python qui permet aux développeurs de créer, de partager et de déployer rapidement et facilement des applications de données. Pour en savoir plus sur Streamlit, cliquez [ici](https://streamlit.io/).\n\n### Vous allez apprendre :\n\n- Comment analyser des données et réaliser des tâches de Data Engineering à l’aide d’API et de classes DataFrame Snowpark\n- Comment utiliser des bibliothèques Python open source depuis un canal Anaconda Snowflake soigneusement sélectionné\n- Comment entraîner un modèle de ML à l’aide de Snowpark ML dans Snowflake\n- Comment créer des fonctions définies par l’utilisateur (UDF) Snowpark Python scalaires et vectorisées, respectivement, pour l’inférence en ligne et hors ligne\n- Comment créer des tâches Snowflake Tasks pour automatiser des pipelines de données\n- Comment créer une application Web Streamlit qui utilise des UDF scalaires pour l’inférence basée sur les entrées des utilisateurs\n\n### Conditions préalables\n\n- [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) doit être installé.\n- [Python 3.9](https://www.python.org/downloads/) doit être installé.\n  - Remarque : vous allez créer un environnement Python avec la version 3.9 lors de l’étape **Premiers pas**.\n- Vous devez posséder un compte Snowflake comprenant des [packages Anaconda activés par ORGADMIN](https://docs.snowflake.com/fr/developer-guide/udf/python/udf-python-packages.html#using-third-party-packages-from-anaconda). Si vous n’avez pas de compte Snowflake, vous pouvez créer un [compte d’essai gratuit](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides).\n- Vous devez vous connecter à un compte Snowflake avec le rôle ACCOUNTADMIN. Si ce rôle est disponible dans votre environnement, vous pouvez choisir de l’utiliser. Sinon, vous devrez 1) créer un compte d’essai gratuit, 2) utiliser un autre rôle capable de créer une base de données, un schéma, des tables, des zones de préparation, des tâches, des fonctions définies par l’utilisateur et des procédures stockées OU 3) utiliser une base de données et un schéma existants dans lesquels vous pouvez créer les objets mentionnés.\n\n\u003E \n\n\u003C!-- ------------------------ --\u003E\n## Configuration de l’environnement\n\n\n### Créer des tables, charger des données et configurer des zones de préparation\n\nConnectez-vous à [Snowsight](https://docs.snowflake.com/fr/user-guide/ui-snowsight.html) à l’aide de vos identifiants pour créer des tables, charger des données depuis Amazon S3 et configurer des zones de préparation internes Snowflake.\n\n\u003E \n\u003E\n\u003E - Si vous utilisez des noms différents pour les objets créés dans cette section, assurez-vous de mettre à jour les scripts et le code dans les sections suivantes en conséquence.\n\u003E\n\u003E - Pour chaque bloc de script SQL ci-dessous, sélectionnez toutes les instructions du bloc et exécutez-les de haut en bas.\n\nExécutez les commandes SQL suivantes pour créer un [entrepôt](https://docs.snowflake.com/fr/sql-reference/sql/create-warehouse.html), une [base de données](https://docs.snowflake.com/fr/sql-reference/sql/create-database.html) et un [schéma](https://docs.snowflake.com/fr/sql-reference/sql/create-schema.html).\n\n```sql \nUSE ROLE ACCOUNTADMIN;\n\nCREATE OR REPLACE WAREHOUSE DASH_L; \nCREATE OR REPLACE DATABASE DASH_DB; \nCREATE OR REPLACE SCHEMA DASH_SCHEMA;\n\nUSE DASH_DB.DASH_SCHEMA; \n```\n\nExécutez les commandes SQL suivantes pour créer une table **CAMPAIGN_SPEND** à partir de données hébergées sur un compartiment S3 accessible au public.\n\n```sql \nCREATE or REPLACE file format csvformat \n  skip_header = 1 \n  type = 'CSV';\n\nCREATE or REPLACE stage campaign_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/campaign_spend/';\n\nCREATE or REPLACE TABLE CAMPAIGN_SPEND ( \n  CAMPAIGN VARCHAR(60), \n  CHANNEL VARCHAR(60), \n  DATE DATE, \n  TOTAL_CLICKS NUMBER(38,0), \n  TOTAL_COST NUMBER(38,0), \n  ADS_SERVED NUMBER(38,0) \n);\n\nCOPY into CAMPAIGN_SPEND \n  from @campaign_data_stage; \n```\n\nExécutez les commandes SQL suivantes pour créer une table **MONTHLY_REVENUE** à partir de données hébergées sur un compartiment S3 accessible au public.\n\n```sql \nCREATE or REPLACE stage monthly_revenue_data_stage \n  file_format = csvformat \n  url = 's3://sfquickstarts/ad-spend-roi-snowpark-python-scikit-learn-streamlit/monthly_revenue/';\n\nCREATE or REPLACE TABLE MONTHLY_REVENUE ( \n  YEAR NUMBER(38,0), \n  MONTH NUMBER(38,0), \n  REVENUE FLOAT \n);\n\nCOPY into MONTHLY_REVENUE \n  from @monthly_revenue_data_stage; \n```\n\nExécutez les commandes SQL suivantes pour créer une table **BUDGET_ALLOCATIONS_AND_ROI** qui contient les six derniers mois d’allocations budgétaires et de retour sur investissement.\n\n```sql \nCREATE or REPLACE TABLE BUDGET_ALLOCATIONS_AND_ROI ( \n  MONTH varchar(30), \n  SEARCHENGINE integer, \n  SOCIALMEDIA integer, \n  VIDEO integer, \n  EMAIL integer, \n  ROI float \n)\nCOMMENT = '{\"origin\":\"sf_sit-is\", \"name\":\"aiml_notebooks_ad_spend_roi\", \"version\":{\"major\":1, \"minor\":0}, \"attributes\":{\"is_quickstart\":1, \"source\":\"streamlit\"}}';\n\nINSERT INTO BUDGET_ALLOCATIONS_AND_ROI (MONTH, SEARCHENGINE, SOCIALMEDIA, VIDEO, EMAIL, ROI) \nVALUES \n('January',35,50,35,85,8.22), \n('February',75,50,35,85,13.90), \n('March',15,50,35,15,7.34), \n('April',25,80,40,90,13.23), \n('May',95,95,10,95,6.246), \n('June',35,50,35,85,8.22); \n```\n\nExécutez les commandes suivantes afin de créer des [zones de préparation internes](https://docs.snowflake.com/fr/user-guide/data-load-local-file-system-create-stage) Snowflake pour stocker des procédures stockées, des UDF et des fichiers de modèles de ML.\n\n```sql\nCREATE OR REPLACE STAGE dash_sprocs;\nCREATE OR REPLACE STAGE dash_models;\nCREATE OR REPLACE STAGE dash_udfs;\n```\n\nVous pouvez également ouvrir le fichier [setup.sql](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/setup.sql) dans Snowsight et exécuter toutes les instructions SQL pour créer les objets et charger les données depuis AWS S3.\n\n\u003E \n\n\u003C!-- ------------------------ --\u003E\n## Premiers pas\n\n\nCette section explique comment cloner le référentiel GitHub et configurer votre environnement Snowpark for Python.\n\n### Cloner le référentiel GitHub\n\nLa première étape consiste à cloner le [référentiel GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn). Le référentiel contient tout le code dont vous aurez besoin pour terminer ce guide Quickstart.\n\nAvec HTTPS :\n\n```shell\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n```\n\nOU avec SSH :\n\n```shell\ngit clone git@github.com:Snowflake-Labs/sfguide-getting-started-dataengineering-ml-snowpark-python.git\n```\n\n### Snowpark for Python\n\nPour réaliser les étapes **Data Engineering** et **Machine Learning**, vous pouvez installer tous les éléments localement (option 1) ou utiliser Hex (option 2) comme décrit ci-dessous.\n\n\u003E \n\n#### Option 1 – Installation locale\n\nCette option va vous permettre de réaliser toutes les étapes de ce guide Quickstart.\n\n**Étape 1 :** téléchargez et installez le programme d’installation miniconda depuis le lien [https://conda.io/miniconda.html](https://conda.io/miniconda.html). *(Avec Python 3.9, vous pouvez également utiliser un autre environnement Python, par exemple, [virtualenv](https://virtualenv.pypa.io/en/latest/).)*\n\n**Étape 2 :** ouvrez une nouvelle fenêtre de terminal et exécutez-y les commandes suivantes.\n\n**Étape 3 :** créez un environnement conda Python 3.9 appelé **snowpark-de-ml** en exécutant la commande suivante dans la même fenêtre de terminal.\n\n```python\nconda create --name snowpark-de-ml -c https://repo.anaconda.com/pkgs/snowflake python=3.9\n```\n\n**Étape 4 :** activez l’environnement conda **snowpark-de-ml** en exécutant la commande suivante dans la même fenêtre de terminal.\n\n```python\nconda activate snowpark-de-ml\n```\n\n**Étape 5 :** installez Snowpark Python et d’autres bibliothèques dans l’environnement conda **snowpark-de-ml** depuis le [canal Anaconda Snowflake](https://repo.anaconda.com/pkgs/snowflake/) en exécutant la commande suivante dans la même fenêtre de terminal.\n\n```python\nconda install -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python pandas notebook scikit-learn cachetools\n```\n\n**Étape 6 :** installez une bibliothèque Streamlit dans l’environnement conda **snowpark-de-ml** en exécutant la commande suivante dans la même fenêtre de terminal.\n\n```python\npip install streamlit\n```\n\n**Étape 7 :** installez une bibliothèque Snowpark ML dans l’environnement conda **snowpark-de-ml** en exécutant la commande suivante dans la même fenêtre de terminal.\n\n```python\npip install snowflake-ml-python\n```\n\n**Étape 9 :** mettez à jour le fichier [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) avec les détails et identifiants de votre compte Snowflake.\n\nVoici un exemple de fichier ***connection.json*** basé sur les noms d’objets mentionnés à l’étape **Configuration de l’environnement**.\n\n```json\n{\n  \"account\"   : \"\u003Cyour_account_identifier_goes_here\u003E\",\n  \"user\"      : \"\u003Cyour_username_goes_here\u003E\",\n  \"password\"  : \"\u003Cyour_password_goes_here\u003E\",\n  \"role\"      : \"ACCOUNTADMIN\",\n  \"warehouse\" : \"DASH_L\",\n  \"database\"  : \"DASH_DB\",\n  \"schema\"    : \"DASH_SCHEMA\"\n}\n```\n\n\u003E \n\n#### Option 2 – Utilisation de Hex\n\nSi vous choisissez d’utiliser votre compte [Hex](https://app.hex.tech/login) existant ou de [créer un compte d’essai gratuit de 30 jours](https://app.hex.tech/signup/quickstart-30), Snowpark for Python est intégré. Vous n’avez donc pas à créer d’environnement Python, ni à installer Snowpark for Python et d’autres bibliothèques localement sur votre ordinateur. Vous pourrez ainsi réaliser les étapes **Data Engineering** et **Machine Learning** de ce guide Quickstart directement dans Hex. (Pour en savoir plus sur le chargement des notebooks de Data Engineering et de Machine Learning dans Hex, consultez les sections correspondantes.)\n\n\u003E \n\n\u003C!-- ------------------------ --\u003E\n## Data Engineering\n\n\nLe notebook dont le lien est fourni ci-dessous traite des tâches de Data Engineering suivantes.\n\n1) Établir une connexion sécurisée entre Snowpark Python et Snowflake \n2) Charger des données depuis des tables Snowflake dans des DataFrames Snowpark \n3) Effectuer une analyse exploratoire des données sur les DataFrames Snowpark \n4) Faire pivoter et associer des données de plusieurs tables à l’aide de DataFrames Snowpark \n5) Automatiser des tâches de pipeline de données à l’aide de tâches Snowflake Tasks\n\n### Notebook de Data Engineering dans Jupyter ou Visual Studio Code\n\nPour commencer, suivez les étapes suivantes :\n\n1) Dans une fenêtre de terminal, accédez à ce dossier et exécutez `jupyter notebook` sur la ligne de commande. (Vous pouvez également utiliser d’autres outils et EDI, comme Visual Studio Code.)\n\n2) Ouvrez et parcourez les cellules du fichier [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb).\n\n\u003E \n\n### Notebook de Data Engineering dans Hex\n\nSi vous choisissez d’utiliser votre compte [Hex](https://app.hex.tech/login) existant ou de [créer un compte d’essai gratuit de 30 jours](https://app.hex.tech/signup/quickstart-30), suivez les étapes suivantes pour charger le notebook et créer une connexion de données entre Snowflake et Hex.\n\n1) Importez le fichier [Snowpark_For_Python_DE.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb) en tant que projet dans votre compte. Pour plus d’informations sur le processus d’importation, consultez la [documentation correspondante](https://learn.hex.tech/docs/versioning/import-export).\n\n2) Ensuite, au lieu d’utiliser le fichier [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) pour vous connecter à Snowflake, créez une [connexion de données](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) et utilisez-la dans le notebook de Data Engineering comme illustré ci-dessous.\n\n![Connexion de données HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/hex_data_connection.png?v=4816f22b)\n\n\u003E \n\n3) Dans le notebook, remplacez l’extrait de code suivant :\n\n```python\nconnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n```\n\n**par...**\n\n```python\nimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Pipelines de données\n\nVous pouvez également exploiter les transformations de données sous forme de pipelines de données automatisés qui s’exécutent dans Snowflake.\n\nDans le [notebook de Data Engineering](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_DE.ipynb), une section montre comment créer et exécuter les transformations de données en tant que [tâches Snowflake Tasks](https://docs.snowflake.com/fr/user-guide/tasks-intro).\n\nÀ des fins de référence, voici les extraits de code.\n\n### **Tâche racine/parent**\n\nCette tâche automatise le chargement des données relatives aux dépenses de campagne et l’exécution de plusieurs transformations.\n\n```python \ndef campaign_spend_data_pipeline(session: Session) -\u003E str:\n  # TRANSFORMATIONS DE DONNÉES \n  # Effectuez les actions suivantes pour transformer les données\n\n  # Chargez les données sur les dépenses de campagnes \n  snow_df_spend_t = session.table('campaign_spend')\n\n  # Transformez les données afin de dégager le coût total annuel/mensuel par canal à l’aide des fonctions DataFrame Snowpark group_by() et agg() \n  snow_df_spend_per_channel_t = snow_df_spend_t.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).\\\n      with_column_renamed('\"YEAR(DATE)\"',\"YEAR\").with_column_renamed('\"MONTH(DATE)\"',\"MONTH\").sort('YEAR','MONTH')\n\n  # Transformez les données afin que chaque ligne représente le coût total annuel/mensuel tous canaux confondus à l’aide des fonctions DataFrame Snowpark pivot() et sum() \n  snow_df_spend_per_month_t = snow_df_spend_per_channel_t.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH') \n  snow_df_spend_per_month_t = snow_df_spend_per_month_t.select( \n      col(\"YEAR\"), \n      col(\"MONTH\"), \n      col(\"'search_engine'\").as_(\"SEARCH_ENGINE\"), \n      col(\"'social_media'\").as_(\"SOCIAL_MEDIA\"), \n      col(\"'video'\").as_(\"VIDEO\"), \n      col(\"'email'\").as_(\"EMAIL\") \n  )\n\n  # Enregistrez les données transformées \n  snow_df_spend_per_month_t.write.mode('overwrite').save_as_table('SPEND_PER_MONTH')\n\n# Enregistrez la fonction de création de pipelines de données en tant que procédure stockée afin de pouvoir l’exécuter en tant que tâche.\nsession.sproc.register( \n  func=campaign_spend_data_pipeline, \n  name=\"campaign_spend_data_pipeline\", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location=\"@dash_sprocs\", \n  replace=True)\n\ncampaign_spend_data_pipeline_task = \"\"\" \nCREATE OR REPLACE TASK campaign_spend_data_pipeline_task \n    WAREHOUSE = 'DASH_L' \n    SCHEDULE = '3 MINUTE' \nAS \n    CALL campaign_spend_data_pipeline() \n\"\"\" \nsession.sql(campaign_spend_data_pipeline_task).collect() \n```\n\n### **Tâche enfant/dépendante**\n\nCette tâche automatise le chargement des données relatives aux revenus mensuels, l’exécution de plusieurs transformations et leur association avec les données transformées relatives aux dépenses de campagne.\n\n```python \ndef monthly_revenue_data_pipeline(session: Session) -\u003E str: \n  # Chargez la table des revenus et transformez les données afin de dégager les revenus annuels/mensuels à l’aide des fonctions group_by et agg() \n  snow_df_spend_per_month_t = session.table('spend_per_month') \n  snow_df_revenue_t = session.table('monthly_revenue') s\n  now_df_revenue_per_month_t = snow_df_revenue_t.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\n\n  # Associez les données relatives aux revenus aux données transformées des dépenses de campagne pour pouvoir charger nos fonctionnalités d’entrée (coût par canal, par exemple) et notre variable cible (revenus, par exemple) dans une seule et même table à des fins d’entraînement de modèle \n  snow_df_spend_and_revenue_per_month_t = snow_df_spend_per_month_t.join(snow_df_revenue_per_month_t, [\"YEAR\",\"MONTH\"])\n\n  # Enregistrez dans une nouvelle table pour la tâche suivante \n  snow_df_spend_and_revenue_per_month_t.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\n\n# Enregistrez la fonction de création de pipelines de données en tant que procédure stockée afin de pouvoir l’exécuter en tant que tâche.\nsession.sproc.register( \n  func=monthly_revenue_data_pipeline, \n  name=\"monthly_revenue_data_pipeline\", \n  packages=['snowflake-snowpark-python'], \n  is_permanent=True, \n  stage_location=\"@dash_sprocs\", \n  replace=True)\n\nmonthly_revenue_data_pipeline_task = \"\"\" \n  CREATE OR REPLACE TASK monthly_revenue_data_pipeline_task \n      WAREHOUSE = 'DASH_L' \n      AFTER campaign_spend_data_pipeline_task \n  AS \n      CALL monthly_revenue_data_pipeline() \n  \"\"\" \nsession.sql(monthly_revenue_data_pipeline_task).collect() \n```\n\n\u003E \n\n#### Lancer des tâches\n\nLes tâches Snowflake Tasks ne se lancent pas automatiquement. Vous devez donc exécuter les instructions suivantes pour les lancer/reprendre.\n\n```sql\nsession.sql(\"alter task monthly_revenue_data_pipeline_task resume\").collect()\nsession.sql(\"alter task campaign_spend_data_pipeline_task resume\").collect()\n```\n\n#### Suspendre des tâches\n\nSi vous reprenez les tâches ci-dessus, suspendez-les pour éviter toute utilisation inutile des ressources en exécutant les commandes suivantes :\n\n```sql\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\n```\n\n### Observabilité des tâches\n\nIl est possible d’afficher ces tâches et leurs [DAG](https://docs.snowflake.com/fr/user-guide/tasks-intro#label-task-dag) dans [Snowsight](https://docs.snowflake.com/fr/user-guide/ui-snowsight-tasks#viewing-individual-task-graphs) comme illustré ci-dessous.\n\n---\n\n![Observabilité des tâches](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowflake_tasks.png?v=4816f22b)\n\n---\n\n### Notifications d’erreurs pour les tâches\n\nVous pouvez également activer les notifications push sur un service de messagerie cloud quand des erreurs se produisent lors de l’exécution de tâches. Pour plus d’informations, consultez la [documentation correspondante](https://docs.snowflake.com/fr/user-guide/tasks-errors).\n\n\u003C!-- ------------------------ --\u003E\n## Machine Learning\n\n\n\u003E \n\nLe notebook associé ci-dessous traite des tâches de Machine Learning suivantes.\n\n1) Établir une connexion sécurisée entre Snowpark Python et Snowflake \n2) Charger des fonctionnalités et une cible depuis une table Snowflake dans un DataFrame Snowpark \n3) Préparer des fonctionnalités pour l’entraînement de modèles \n4) Entraîner un modèle de ML à l’aide de Snowpark ML sur Snowflake \n5) Créer des [fonctions définies par l’utilisateur (UDF) Python](https://docs.snowflake.com/fr/developer-guide/snowpark/python/creating-udfs) scalaires et vectorisées (aussi appelées « batchs ») pour toute inférence sur des nouveaux points de données, en ligne et hors ligne, respectivement\n\n---\n\n![ML de bout en bout](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/snowpark_e2e_ml.png?v=4816f22b)\n\n---\n\n### Notebook de Machine Learning dans Jupyter ou Visual Studio Code\n\nPour commencer, suivez les étapes suivantes :\n\n1) Dans une fenêtre de terminal, accédez à ce dossier et exécutez `jupyter notebook` sur la ligne de commande. (Vous pouvez également utiliser d’autres outils et EDI, comme Visual Studio Code.)\n\n2) Ouvrez et exécutez le fichier [Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb).\n\n\u003E \n\n### Notebook de Machine Learning dans Hex\n\nSi vous choisissez d’utiliser votre compte [Hex](https://app.hex.tech/login) existant ou de [créer un compte d’essai gratuit de 30 jours](https://app.hex.tech/signup/quickstart-30), suivez les étapes suivantes pour charger le notebook et créer une connexion de données entre Snowflake et Hex.\n\n1) Importez le fichier [Snowpark_For_Python_ML.ipynb](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_For_Python_ML.ipynb) en tant que projet dans votre compte. Pour plus d’informations sur le processus d’importation, consultez la [documentation correspondante](https://learn.hex.tech/docs/versioning/import-export).\n\n2) Ensuite, au lieu d’utiliser le fichier [connection.json](https://github.com/Snowflake-Labs/sfguide-ml-model-snowpark-python-scikit-learn-streamlit/blob/main/connection.json) pour vous connecter à Snowflake, créez une [connexion de données](https://learn.hex.tech/tutorials/connect-to-data/get-your-data#set-up-a-data-connection-to-your-database) et utilisez-la dans le notebook de Machine Learning comme illustré ci-dessous.\n\n![Connexion de données HEX](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/hex_data_connection.png?v=4816f22b)\n\n\u003E \n\n3) Dans le notebook, remplacez l’extrait de code suivant :\n\n```python\nconnection_parameters = json.load(open('connection.json'))\nsession = Session.builder.configs(connection_parameters).create()\n```\n\n**par...**\n\n```python\nimport hextoolkit\nhex_snowflake_conn = hextoolkit.get_data_connection('YOUR_DATA_CONNECTION_NAME')\nsession = hex_snowflake_conn.get_snowpark_session()\nsession.sql('USE SCHEMA DASH_SCHEMA').collect()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Application Streamlit\n\n\n### Exécuter localement l’application Streamlit\n\nDans une fenêtre de terminal, accédez à ce dossier et exécutez la commande suivante pour exécuter l’application Streamlit [Snowpark_Streamlit_Revenue_Prediction.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction.py) localement sur votre machine.\n\n```shell\nstreamlit run Snowpark_Streamlit_Revenue_Prediction.py\n```\n\nSi tout se passe bien, une fenêtre de navigateur doit s’ouvrir et l’application doit y être chargée, comme illustré ci-dessous.\n\n---\n\n![Application Streamlit](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/app.png?v=4816f22b)\n\n---\n\n### Exécuter l’application Streamlit dans Snowflake – Streamlit-in-Snowflake (SiS)\n\nSi l’application SiS est activée dans votre compte, suivez les étapes suivantes pour l’exécuter dans Snowsight, et non localement sur votre machine.\n\n\u003E \n\n  1) Cliquez sur **Streamlit Apps** (Applications Streamlit) dans le menu de navigation de gauche. \n  2) Cliquez sur **+ Streamlit App** (+ Application Streamlit) dans le coin supérieur droit. \n  3) Saisissez le **nom de l’application**. \n  4) Sélectionnez l’**entrepôt** et l’**emplacement de l’application** (Base de données et Schéma) où vous voulez créer l’application Streamlit. \n  5) Cliquez sur **Create** (Créer). \n  6) Vous recevrez alors un code pour un exemple d’application Streamlit. Ouvrez le fichier [Snowpark_Streamlit_Revenue_Prediction_SiS.py](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn/blob/main/Snowpark_Streamlit_Revenue_Prediction_SiS.py) et copiez-collez le code dans l’exemple d’application Streamlit. \n  7) Cliquez sur **Run** (Exécuter) dans le coin supérieur droit.\n\nSi tout se passe bien, l’application suivante doit apparaître dans Snowsight, comme illustré ci-dessous.\n\n---\n\n![Streamlit-in-Snowflake](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/app_sis.png?v=4816f22b)\n\n---\n\n### Enregistrer des données dans Snowflake\n\nDans les deux applications, modifiez les curseurs du budget publicitaire pour voir le retour sur investissement prévu pour ces allocations. Vous pouvez également cliquer sur le bouton **Save to Snowflake** (Enregistrer dans Snowflake) pour enregistrer les allocations actuelles et le retour sur investissement prévu dans la table Snowflake BUDGET_ALLOCATIONS_AND_ROI.\n\n### Différences entre les deux applications Streamlit\n\nLa principale différence entre le fait d’exécuter l’application Streamlit localement ou dans Snowflake (SiS) est la façon dont vous créez l’objet de session et dont vous y accédez.\n\nLorsque vous l’exécutez localement, vous créez le nouvel objet de session et vous y accédez comme suit :\n\n```python\n# Fonction pour créer une session Snowflake à connecter à Snowflake\ndef create_session(): \n    if \"snowpark_session\" not in st.session_state: \n        session = Session.builder.configs(json.load(open(\"connection.json\"))).create() \n        st.session_state['snowpark_session'] = session \n    else: \n        session = st.session_state['snowpark_session'] \n    return session \n```\n\nLorsque vous l’exécutez dans Snowflake (SiS), vous accédez à l’objet de session actuel comme suit :\n\n```python\nsession = snowpark.session._get_active_session()\n```\n\n\u003C!-- ------------------------ --\u003E\n## Nettoyage\n\nSi vous avez lancé/repris les deux tâches `monthly_revenue_data_pipeline_task` et `campaign_spend_data_pipeline_task` dans le cadre des sections **Data Engineering** ou **Pipelines de données**, il est important que vous exécutiez les commandes suivantes pour suspendre ces tâches afin d’éviter toute utilisation inutile des ressources.\n\nDans le notebook, à l’aide de l’API Snowpark Python\n\n```sql\nsession.sql(\"alter task campaign_spend_data_pipeline_task suspend\").collect()\nsession.sql(\"alter task monthly_revenue_data_pipeline_task suspend\").collect()\n```\n\nDans Snowsight\n\n```sql\nalter task campaign_spend_data_pipeline_task suspend;\nalter task monthly_revenue_data_pipeline_task suspend;\n```\n\n\u003C!-- ------------------------ --\u003E\n## Conclusion et ressources\n\n\nFélicitations ! Vous avez réalisé les tâches de Data Engineering et entraîné un modèle de régression linéaire pour prévoir le futur RSI (retour sur investissement) des budgets variables de dépenses publicitaires sur plusieurs canaux (moteurs de recherche, vidéos, réseaux sociaux et e-mails, par exemple) à l’aide de Snowpark for Python et de scikit-learn. Vous avez ensuite créé une application Streamlit qui utilise ce modèle pour générer des prévisions sur les nouvelles allocations budgétaires sur la base des entrées des utilisateurs.\n\nN’hésitez pas à nous faire part de vos commentaires sur ce guide Quickstart ! Envoyez-nous vos commentaires à l’aide de ce [formulaire de commentaires](https://forms.gle/XKd8rXPUNs2G1yM28).\n\n### Vous avez appris :\n\n- Comment analyser des données et réaliser des tâches de Data Engineering à l’aide d’API et de classes DataFrame Snowpark\n- Comment utiliser des bibliothèques Python open source depuis un canal Anaconda Snowflake soigneusement sélectionné\n- Comment entraîner un modèle de ML à l’aide de Snowpark ML dans Snowflake\n- Comment créer des fonctions définies par l’utilisateur (UDF) Snowpark Python scalaires et vectorisées, respectivement, pour l’inférence en ligne et hors ligne\n- Comment créer des tâches Snowflake Tasks pour automatiser la création de pipelines de données et l’entraînement (nouveau ou non) du modèle\n- Comment créer une application Web Streamlit qui utilise les UDF scalaires pour l’inférence\n\n### Ressources connexes\n\n- [Code source sur GitHub](https://github.com/Snowflake-Labs/sfguide-ad-spend-roi-snowpark-python-streamlit-scikit-learn)\n- [Avancé : Guide de Data Engineering avec Snowpark for Python ](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/)\n- [Avancé : Guide de Machine Learning avec Snowpark for Python ](/en/developers/guides/getting-started-snowpark-machine-learning/)\n- [Démos de Snowpark for Python](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\n- [Guide du développeur Snowpark for Python](https://docs.snowflake.com/fr/developer-guide/snowpark/python/index.html)\n- [Documents Streamlit](https://docs.streamlit.io/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-760cad34e6","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-37f54ee4d1",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-55803872e6","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-07",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-a4d9e5fa62","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-8b4b9d39aa",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["contentfragment","flexible_column_cont"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-52297b07db",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-7a9a2677dd","isDeveloperGuidesPage":false,":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container",":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-59e13f4527","headings":["\u003Ch2\u003EPrésentation\u003C/h2\u003E","\u003Ch2\u003EConfiguration de l’environnement\u003C/h2\u003E","\u003Ch2\u003EPremiers pas\u003C/h2\u003E","\u003Ch2\u003EData Engineering\u003C/h2\u003E","\u003Ch2\u003EPipelines de données\u003C/h2\u003E","\u003Ch2\u003EMachine Learning\u003C/h2\u003E","\u003Ch2\u003EApplication Streamlit\u003C/h2\u003E","\u003Ch2\u003ENettoyage\u003C/h2\u003E","\u003Ch2\u003EConclusion et ressources\u003C/h2\u003E"],"fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/getting-started-with-dataengineering-ml-using-snowpark-python-fr",":type":"snowflake-site/components/quickstart/quickstart-table-of-content"},"quickstart_button":{"id":"quickstart-button-3cf00821c8","fragmentPath":"/content/dam/snowflake-site/fr/content-fragments/quickstarts/getting-started-with-dataengineering-ml-using-snowpark-python-fr",":type":"snowflake-site/components/quickstart/quickstart-button","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"]}},":itemsOrder":["quickstart_table_of_"]},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false},"markup_editor":{"id":"markup-editor-345f716528","title":"Page CSS","cssUrl":"/content/snowflake-site/global/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/_jcr_content/root/responsivegrid/markup_editor.eb24a609da50a43b.css",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["quickstart_hero","flexible_column_cont","markup_editor"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"layout":"SIMPLE","id":"container-87c15a9ed0",":type":"snowflake-site/components/modal/modal-container",":items":{},":itemsOrder":[]},"experiencefragment-footer":{"id":"experiencefragment-99c7d366a0","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/footer/master/jcr:content","configured":true,":type":"snowflake-site/components/experiencefragment",":items":{"root":{"additionalClasses":"sf-footer","layout":"SIMPLE","id":"container-b39246b248","appliedCssClassNames":"ui-background-02",":type":"snowflake-site/components/container",":items":{"container_copy_1510208642":{"additionalClasses":"sf-footer__inner","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-455da07a28","appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-1b473c1443","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"sf-footer-grid","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-888f6ce6d9",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","container_1622723482":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy":"aem-GridColumn aem-GridColumn--default--12","container_copy":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-4f284e42c2","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-accac96983","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-da9b50fdd2","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-7ac3be17ba","additionalClasses":"sf-footer__newsletter-title","text":"\u003Cp\u003E\u003Cb\u003EInscrivez-vous à notre newsletter mensuelle\u003C/b\u003E\u003C/p\u003E\r\n\u003Cp\u003ERestez informé(e) des dernières informations produit, des conseils d'experts et des ressources de Snowflake, directement dans votre boîte mail !\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-04"},"marketo_v2":{"id":"marketo-v2-6292e61019","marketoForm":{"hidden":null,"formId":"45871","successUrl":null,"edit":false,"script":null,"values":null},"partnerCampaignPage":false,"formConfigured":true,"marketoConfigured":true,"serverInstance":"252-RFO-227.mktoweb.com","munchkinId":"252-RFO-227",":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"]}},":itemsOrder":["container"]},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-53929a113f","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-0bd8222f47","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EProduit\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/platform/\"\u003EPlateforme\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/data-engineering/\"\u003EData engineering\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/analytics/\"\u003EAnalytics\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/ai/\"\u003EIA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/applications-and-collaboration/\"\u003EApplications et collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/pricing-options/\"\u003ETarification\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-475fb7b4cf","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/support/\"\u003ESupport (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/support/\"\u003EPriority Support (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EStatut (EN)\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"]},"container_copy_copy":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-2e943a5b7e","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-4e2ef9a04f","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EPar secteur\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/advertising-media-entertainment/\"\u003EPublicité, médias et divertissement\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/financial-services/\"\u003EServices financiers\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/healthcare-and-life-sciences/\"\u003ESanté et sciences de la vie\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/manufacturing/\"\u003EIndustrie\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/public-sector/\"\u003ESecteur public\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/retail-consumer-goods/\"\u003ERetail et grande distribution\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/solutions/industries/technology/\"\u003ETechnologie\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"]},"container_copy":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-0c095e2be3","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-0af3b284ce","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EEntreprise\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/company/overview/about-snowflake/\"\u003EÀ propos de Snowflake\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/leadership-and-board/\"\u003EDirection et CA (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://careers.snowflake.com/us/en?_ga=2.189098923.1024280027.1746985324-1783381883.1746382047\"\u003ENous rejoindre (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://investors.snowflake.com/overview/default.aspx\"\u003ERelations investisseurs (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://trust.snowflake.com/\"\u003ETrust Center (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/brand-guidelines/\" target=\"_blank\"\u003EMarque (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/contact/\"\u003ENous contacter\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/news/\"\u003EActualités\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/esg/\"\u003EESG (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/company/overview/snowflake-ventures/\"\u003ESnowflake Ventures (EN)\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\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"}},":itemsOrder":["text"]},"container_copy_copy_":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-4c926c9da8","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"text":{"id":"text-a52317eeef","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003EDécouvrez\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://snowflake.com/fr/resources/\"\u003EBibliothèque de ressources \u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/en/webinars/demo/\"\u003EDémos live (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/fr/fundamentals/\"\u003EConcepts de base\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/training/\"\u003EFormation (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/resources/learn/certifications/\"\u003ECertifications (EN) \u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://learn.snowflake.com/en/\" target=\"_blank\"\u003ESnowflake University (EN)\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://www.snowflake.comfr/developers/guides/\" target=\"_self\"\u003EGuides pour les développeurs\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca rel=\"noopener noreferrer\" href=\"https://docs.snowflake.com/fr\" target=\"_blank\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"/fr/data-governance/\"\u003EGouvernance des données\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"]}},":itemsOrder":["container_1622723482","container","container_copy_copy","container_copy","container_copy_copy_"]}},":itemsOrder":["container"]},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["flexible_column_cont"]},"container_573483281_":{"additionalClasses":"sf-footer__bottom","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-0acfe5b11f","appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":type":"snowflake-site/components/container",":items":{"container_112062425":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-0f0e6f292b","appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container",":items":{"flexible_column_cont":{"id":"flexible-column-container-be49108b2b","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-0a6f552539",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"additionalClasses":"sf-footer__legal-container","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_16360_26716402":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-b017fcbc1b","appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":type":"snowflake-site/components/container",":items":{"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-5b60ba9208","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container",":items":{"image":{"id":"image-deec352f5e","additionalClasses":"sf-footer__logo","height":"64","alt":"Snowflake logo","imageLink":{"valid":true,"url":"/en/"},"src":"/content/experience-fragments/snowflake-site/language-masters/fr/site/footer/master/_jcr_content/root/container_573483281_/container_112062425/flexible_column_cont/flexible_column_content_container_1/container/container/image.coreimg.svg/1747882370694/nav-icon-snowflake-bug.svg","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"]},"text_copy_copy_16360_26716402":{"id":"text-2f91a1ee71","additionalClasses":"sf-footer__legal-links","text":"\u003Cul\u003E\r\n\u003Cli\u003E© 2026 Snowflake Inc. Tous droits réservés\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/legal/privacy/privacy-policy/\"\u003EAvis de confidentialité\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/snowflake-site-terms/\"\u003EConditions d’utilisation du site\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://info.snowflake.com/Preference-center.html\"\u003EPréférences de communication\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Cbutton id=\"ot-sdk-btn\" class=\"ot-sdk-show-settings\"\u003EParamètres des cookies\u003C/button\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/legal/privacy/privacy-policy/#12\"\u003ENe pas partager mes informations personnelles\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/legal/\"\u003EDocuments légaux\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-e15011a7e6","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","cssUrl":"/content/experience-fragments/snowflake-site/language-masters/fr/site/footer/master/_jcr_content/root/container_573483281_/container_112062425/flexible_column_cont/flexible_column_content_container_1/container/markup_editor.a1b733f3995ad01d.css",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container","text_copy_copy_16360_26716402","markup_editor"]}},":itemsOrder":["container"]},":type":"snowflake-site/components/flexible-column-container","isActiveTOC":false,"isBlogPage":false}},":itemsOrder":["flexible_column_cont"]}},":itemsOrder":["container_112062425"]},"markup_editor_copy":{"id":"markup-editor-fefb224f4b","title":"New css","cssContent":"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}.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:1.4}.related-chip-25 .snowflake-content-chip-image{width:48px}.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:hover::after{right:24px;transition:300ms ease right}.related-chip-25 .snowflake-content-chip-content-without-tag{flex-grow:1;padding-right:24px}.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}.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}.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}",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["container_copy_1510208642","container_573483281_","markup_editor_copy"]}},":itemsOrder":["root"],"classNames":"aem-xf"},"markup_editor":{"id":"markup-editor-17a10c4eaa","title":"Quickstarts Overrides","cssUrl":"/conf/snowflake-site/settings/wcm/templates/quickstart-page-template/structure/_jcr_content/root/markup_editor.45e3b816710d8d5a.css",":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"],"isPasswordProtected":false,"analyticsEnabled":true,":hierarchyType":"page",":path":"/content/snowflake-site/global/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr","coveoConfig":{"searchHub":"snowflake.com","organizationId":"snowflakecomputingproduction8neljofn","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","pipeline":"snowflake.com"},"analyticsContentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/product/ai","snowflake-site:taxonomy/snowflake-feature/ml-functions"],"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"guides","pageType":"developer","templateName":"quickstart-page-template","siteName":"snowflake","pageUrl":"/fr/developers/guides/getting-started-with-dataengineering-ml-using-snowpark-python-fr/","language":"fr","category":"developers","pageName":"Premiers pas avec le Data Engineering et le Machine Learning à l’aide de Snowpark for Python","contentTags":["snowflake-site:taxonomy/solution-center/certification/quickstart","snowflake-site:taxonomy/product/ai","snowflake-site:taxonomy/snowflake-feature/ml-functions"]},"publicationThemeClass":"","locale":"fr"}