{"templateName":"base-page-template54","cssClassNames":"page basicpage summit-page","canonicalLink":"https://www.snowflake.com/fr/analytics/ai-analytics/predictive-analytics/","robotsTags":[],"allowedRenditionsWidth":["320","480","640","768","960","1200","1440","1920"],"description":"L’analyse prédictive utilise les schémas présents dans les données historiques pour estimer ce qui pourrait se produire ensuite.  Découvrez comment fonctionnent les modèles prédictifs, quels sont les types de modèles et leurs cas d’usage, et comment les exécuter.","language":"fr","title":"Qu’est-ce que l’analyse prédictive ? Modèles et cas d’usage | Snowflake","analyticsPageType":"content","analyticsCategory":"analytics","analyticsSubCategory":"ai-analytics","excludeFromAnalytics":false,":mappedPath":"/fr/analytics/ai-analytics/predictive-analytics/",":items":{"root":{"columnCount":12,"columnClassNames":{"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","experiencefragment":"aem-GridColumn aem-GridColumn--default--12","modal_container":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12",":items":{"experiencefragment-banner":{"id":"experiencefragment-c9d0510a0d","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/pushdown-banner/master/jcr:content","configured":true,":items":{"root":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"pushdown_banner_copy_1875701434":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-b7b41427a2",":items":{"pushdown_banner_copy_1875701434":{"id":"pushdown-banner-9b6138f6ca","contentHeadline":"Expedition. Gratuit, à la demande.","contentDescription":"Des sessions techniques pour les praticiens, des résultats concrets 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":"Regarder maintenant (EN)"},":type":"snowflake-site/components/pushdown-banner","appliedCssClassNames":"snowflake-pushdown-banner-text-white snowflake-pushdown-banner-background-black"}},":itemsOrder":["pushdown_banner_copy_1875701434"],":type":"snowflake-site/components/container"},"image":{":type":"nt:unstructured"},"cq:metadata":{":type":"nt:unstructured"}},":itemsOrder":["root","image","cq:metadata"],":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf"},"experiencefragment-header":{"id":"experiencefragment-f8a2372495","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/mega-nav-header/master/jcr:content","configured":true,":items":{"root":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"mega_header":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-a5191a17eb",":items":{"markup_editor":{"id":"markup-editor-f87067ff3d","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-3fc44e3920",":items":{"nav_mega":{"activeItem":"item_1719963657751_c_663444255","id":"tabs-b0a4143426",":type":"snowflake-site/components/nav/nav-mega",":items":{"item_1719963657751_c_663444255":{"id":"nav-dropdown-menu-c17a85e893","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-40e5568d24",":items":{"nav_column":{"additionalClasses":"nav-platform-sidebar","numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-7034716b85",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-71bb0ab9eb","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-a71054ec51","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-08b465c753","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-dd8f68cb1a","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-5877bc1840","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-c1391cab34","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-695b518e1a","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","navColumnTitle":"Fonctionnalités phares","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-8339005eb1",":items":{"nav_item_copy_1686013634":{"id":"nav-item-8510387eb9","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-2ac790f2bf","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-9b20c3facb","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-0a9d889793","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-6169f80693","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-c661f10056","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-f26c21d47e","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-d74468d1a5","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-5fe6465c04","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-01935c5be8","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_676020780":{"navColumnTitle":"     ","numberOfSubColumns":"one-column","maxWidth":"300","layout":"SIMPLE","id":"container-977ff096dd",":items":{"nav_item_511717659":{"id":"nav-item-6ee2326cf5","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-ea1a1e3a79","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-f7f41767e0","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-da87ee1637","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-7ab65659d4","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy_copy","nav_column_676020780"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Produit"},"nav_dropdown_menu_2":{"id":"nav-dropdown-menu-ce6245e91d","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-f48549fd1d",":items":{"nav_column":{"navColumnTitle":"Par secteur","numberOfSubColumns":"one-column","minWidth":"280","layout":"SIMPLE","id":"container-c90f980ee1",":items":{"nav_item_copy_361384_1796955541":{"id":"nav-item-213468ad73","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-ece209e445","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-3643a79db9","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-48079ecd69","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-228c6341a6","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-c357f75868","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-6e1d32e492","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-b328bcaa49","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-9352b00873","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-3d14bcaee7","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy":{"navColumnTitle":"Par département","numberOfSubColumns":"one-column","minWidth":"200","layout":"SIMPLE","id":"container-4141bef8b2",":items":{"nav_item":{"id":"nav-item-a91d1b32cb","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-cdf36042b6","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-168ce5cdec","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_833417450":{"navColumnTitle":"Solutions d’accompagnement","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-d2d196e774",":items":{"nav_item_copy_107772":{"id":"nav-item-7cd43553fb","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","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","alt":"Cloud icon","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-5f6c12fd14","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","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","alt":"Migrate icon","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"navColumnTitle":"Solutions partenaires","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-304df06ffd",":items":{"nav_item":{"id":"nav-item-1e9b15d61e","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","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","alt":"Partner Network icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-8b9733e004","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","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","alt":"Partner Finder icon","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-6cf4e276e2","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","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","alt":"Calendar icon","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy","nav_column_833417450","nav_column_copy_copy"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Solutions"},"item_1719963657751_c":{"id":"nav-dropdown-menu-ac4d05a1e2","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-e36b45e75f",":items":{"nav_column":{"numberOfSubColumns":"one-column","minWidth":"230","maxWidth":"350","layout":"SIMPLE","id":"container-072477ab4d",":items":{"nav_item_copy_copy_2_793631646":{"id":"nav-item-d92eb47bb0","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy":{"additionalClasses":"meganav-platform-features","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-2b187c588b",":items":{"nav_item":{"id":"nav-item-3670c79bf5","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","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","alt":"Partner Network icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_258535199":{"id":"nav-item-b1c3916901","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","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","alt":"Cloud icon","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-26fc7b8859","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","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","alt":"User with security lock icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-48bd578eb8","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","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","alt":"Cost Optimization icon","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-d37230ef0d","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","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","alt":"Launch","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column","nav_column_copy_copy"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Pourquoi Snowflake"},"item_1719961362824":{"id":"nav-dropdown-menu-3643bd4f02","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-f9b31d6ed6",":items":{"nav_column_copy":{"navColumnTitle":"Restez connecté","numberOfSubColumns":"one-column","minWidth":"124","layout":"SIMPLE","id":"container-5140b47313",":items":{"nav_item":{"id":"nav-item-44344eda23","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-64173962b3","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-5c637c72a3","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-67b945d1b4","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_44600420__826130542":{"navColumnTitle":"Découvrez","numberOfSubColumns":"two-columns","layout":"SIMPLE","id":"container-5074eec5fc",":items":{"nav_item_copy":{"id":"nav-item-bc66abe693","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","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","alt":"Notebooks icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item":{"id":"nav-item-9ac6b610e6","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","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","alt":"Training icon","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-04bed54714","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","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","alt":"Webinars icon","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-993df83263","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","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","alt":"Certification icon","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-837f374c63","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","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","alt":"Live Demo icon","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-e8237809b6","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","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","alt":"Education icon","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-f4f573f222","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","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","alt":"Hands-on Labs icon","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-25efc8bea7","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","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","alt":"Copy","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-c562a64f9e","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","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","alt":"Data Sheet","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column_copy","nav_column_44600420__826130542"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Ressources"},"item_1719963657751":{"id":"nav-dropdown-menu-b85722e63c","enableDropdown":true,"nav_column_container":{"layout":"SIMPLE","id":"container-2244c1cb3e",":items":{"nav_column_copy_copy":{"navColumnTitle":"Créez","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-ed0a50050f",":items":{"nav_item":{"id":"nav-item-cba070b992","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","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","alt":"Developers icon","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-f7ff490279","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","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","alt":"Solution Center icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-8109a546ce","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","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","alt":"Download icon","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy_1367930678":{"navColumnTitle":"Découvrez","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-f63a6a481b",":items":{"nav_item":{"id":"nav-item-fe7780cfaf","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","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","alt":"Docs icon","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/nav/nav-item"},"nav_item_copy":{"id":"nav-item-519b3d4db8","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","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","alt":"Open Source icon","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-b00cb52625","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","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","alt":"Northstar logo","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"],":type":"snowflake-site/components/nav/nav-column"},"nav_column_copy_copy_1101894776":{"navColumnTitle":"Restez connecté","numberOfSubColumns":"one-column","layout":"SIMPLE","id":"container-bfb09fb5b3",":items":{"nav_item":{"id":"nav-item-169fff0e00","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","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","alt":"Developers icon","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-ca7e3994e9","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","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","alt":"Partner Network icon","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"],":type":"snowflake-site/components/nav/nav-column"}},":itemsOrder":["nav_column_copy_copy","nav_column_copy_copy_1367930678","nav_column_copy_copy_1101894776"],":type":"snowflake-site/components/nav/nav-column/nav-column-container"},":type":"snowflake-site/components/nav/nav-dropdown-menu","cq:panelTitle":"Développeurs"},"item_1718247180324":{"id":"nav-dropdown-menu-f333139426","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-c736d8e375","languageNavItems":[{"title":"English","path":"/en/analytics/ai-analytics/predictive-analytics/","locale":"en","active":false},{"title":"日本語","path":"/ja/","locale":"ja","active":false},{"title":"한국어","path":"/ko/analytics/ai-analytics/predictive-analytics/","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/analytics/ai-analytics/predictive-analytics/","locale":"de","active":false},{"title":"Français","path":"/fr/analytics/ai-analytics/predictive-analytics/","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-1af17f519f","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-27a3d7d185","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"],"appliedCssClassNames":"snowflake-header-container white",":type":"snowflake-site/components/mega-header"}},":itemsOrder":["markup_editor","mega_header"],":type":"snowflake-site/components/container"}},":itemsOrder":["root"],":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf"},"responsivegrid":{"columnCount":12,"columnClassNames":{"flexible_column_cont_939100716":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1158003461":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_663228916":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_912630531":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1398138236":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1786318617":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1467213961":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1377146023":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12",":items":{"flexible_column_cont":{"id":"flexible-column-container-b50aac5a61","propertiesId":"hub-hero-breadcrumbs","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-3ff87792d7",":items":{"breadcrumb":{"id":"breadcrumb-7d3191a39d","items":[{"id":"breadcrumb-7d3191a39d-item-25575695e0","link":{"valid":true,"url":"/fr/analytics/"},"active":false,"current":false,"title":"Analytique","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"},{"id":"breadcrumb-7d3191a39d-item-02e748e930","link":{"valid":true,"url":"/fr/analytics/ai-analytics/"},"active":false,"current":false,"title":"Analyse de données par l’IA","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"},{"id":"breadcrumb-7d3191a39d-item-ac1660d3ee","link":{"valid":true,"url":"/fr/analytics/ai-analytics/predictive-analytics/"},"active":true,"current":true,"title":"Analyse prédictive","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"}],":type":"snowflake-site/components/breadcrumb"}},":itemsOrder":["breadcrumb"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_939100716":{"id":"flexible-column-container-d3948a687c","propertiesId":"hub-hero","type":"2-column-even","alignColumns":"center","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-199461020b",":items":{"title_v2":{"id":"title-v2-b1d7cb5d80","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Analyse prédictive : fonctionnement, modèles et cas d'usage"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-253c1617f5","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EL’analyse prédictive utilise les schémas présents dans les données historiques pour estimer ce qui pourrait se produire ensuite. Découvrez comment fonctionnent les modèles prédictifs, comment les équipes les évaluent et les maintiennent, et comment l’IA prédictive permet d’appliquer ces techniques à grande échelle.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"container":{"additionalClasses":"hub-hero__authors","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"content_chip_1934028918":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-ebd0b17964",":items":{"content_chip":{"id":"content-chip-7358d536bb","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/Laurie-Macpherson/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"/adobe/dynamicmedia/deliver/dm-aid--d76c5f1c-c1a7-47c1-ae53-19ff8db7bef9/laurie-macpherson.jpg?quality=85&preferwebp=true","alt":"Laurie MacPherson","lazyEnabled":true,"isLcpImage":false,"width":"800",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Laurie MacPherson","Technical Writer, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"},"content_chip_1934028918":{"id":"content-chip-92313662ec","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/authors/tracy-kabuya/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"709","src":"/adobe/dynamicmedia/deliver/dm-aid--9ad7a3c0-e23e-4370-bc36-f59a5fe02eff/img-1817.jpg?quality=85&preferwebp=true","alt":"Tracy Kabuya","lazyEnabled":true,"isLcpImage":false,"width":"709",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Tracy Kabuya","Contributrice locale, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},":itemsOrder":["content_chip","content_chip_1934028918"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["title_v2","text","container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-hero__video-column","layout":"SIMPLE","id":"container-1851b120fe",":items":{"youtube":{"id":"embed-55fe9bc914","youtubeVideoId":"560rRpKij6U","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":true,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"}},":itemsOrder":["youtube"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1398138236":{"id":"flexible-column-container-a580d8f40d","propertiesId":"hub-hero-related-topics","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"related-topics-outer-container border-top","layout":"SIMPLE","id":"container-1080b2e388",":items":{"text_894059747":{"id":"text-a337055640","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes liés à l’analyse de données par l’IA :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-853c6d640c","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/ai-analytics/\"\u003EAnalyse de données par l'IA\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/data-analysis/\"\u003EAnalyse de données\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/real-time-analytics/\"\u003EAnalytique en temps réel\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/business-intelligence/\"\u003EBusiness Intelligence\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-5ac1220915","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":items":{"callout__0":{"id":"text-1255e465dd","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDÉFINITION DE L’ANALYSE PRÉDICTIVE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL’analyse prédictive consiste à utiliser des données existantes pour estimer des valeurs, des événements ou des probabilités futurs à l’aide de méthodes statistiques et de l’apprentissage automatique, également appelé machine learning (ML).\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-392f403f7d","text":"\u003Cp\u003EElle repose sur une hypothèse simple : les schémas observés dans les données historiques peuvent nous apprendre quelque chose sur ce qui pourrait se produire ensuite.\u003C/p\u003E\n\u003Cp\u003ECette hypothèse est souvent utile. Toutefois, le comportement de vos clients évolue, les conditions opérationnelles changent et les facteurs de risque se transforment. Le véritable test d’un modèle prédictif intervient après sa mise en production, lorsqu’il rencontre des situations différentes de celles observées dans ses données d’entraînement. Les schémas appris par le modèle se généralisent-ils suffisamment bien pour produire des estimations fiables sur de nouveaux cas ?\u003C/p\u003E\n\u003Cp\u003ECette tension est au cœur de l’analyse prédictive : les modèles apprennent du passé, mais leur performance se mesure dans l’avenir. Le principe s’applique aussi bien aux méthodes statistiques classiques qu’à l’IA prédictive. L’analyse prédictive est plus fiable lorsque les entreprises considèrent les prédictions comme des estimations à tester et à maintenir dans le temps, plutôt que comme des réponses définitives sur ce qui va se produire.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-predictive-analytics":{"id":"title-v2-62de09309c","additionalClasses":"anchor-title anchor-title--what-is-predictive-analytics","type":"heading2","lines":["Qu’est-ce que l’analyse prédictive ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-predictive-analytics_0":{"id":"text-482062239a","text":"\u003Cp\u003EConcrètement, un modèle prédictif applique des méthodes statistiques ou de \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E (ML) à vos données existantes pour produire une estimation. Selon le cas d'usage, cette estimation prend la forme d'une probabilité, d'une prévision ou d'un score de risque. Dans tous les cas, elle exprime ce qui est susceptible de se produire, et non ce qui se produira avec certitude.\u003C/p\u003E\r\n\u003Cp\u003ECette orientation vers l’avenir distingue l’analyse prédictive d’autres formes d’analyse :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003El’analyse descriptive explique ce qui s’est produit ; \u003C/li\u003E\r\n\u003Cli\u003El’analyse prédictive estime ce qui pourrait se produire ; \u003C/li\u003E\r\n\u003Cli\u003El’analyse diagnostique cherche à comprendre pourquoi cela s’est produit ;\u003C/li\u003E\r\n\u003Cli\u003El’analyse prescriptive utilise cette estimation pour recommander une action.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELes systèmes prédictifs imposent des exigences supplémentaires par rapport aux systèmes descriptifs et diagnostiques. Les données historiques doivent représenter suffisamment les conditions que le modèle rencontrera. Les prédictions doivent être validées sur des données que le modèle n’a pas utilisées pour son entraînement. Enfin, la performance doit être surveillée lorsque les conditions changent. Un modèle performant l’année dernière peut progressivement perdre en précision si le comportement de vos clients, les conditions du marché ou les processus opérationnels évoluent.\u003C/p\u003E\r\n\u003Cp\u003ELe ML a élargi le champ d’application des techniques prédictives dans les organisations. Les méthodes de prévision statistique et de classification existaient avant le machine learning moderne, mais le ML permet d’exécuter des modèles prédictifs sur de très grandes populations de clients, de produits, de transactions ou d’autres entités.\u003C/p\u003E\r\n\u003Cp\u003EL’analyse prédictive et l’IA prédictive sont étroitement liées, mais ces deux notions ne désignent pas exactement la même chose :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003El’analyse prédictive est une discipline analytique qui utilise des schémas historiques pour estimer des résultats futurs ; \u003C/li\u003E\r\n\u003Cli\u003El’IA prédictive désigne généralement l’utilisation de modèles de machine learning pour produire ces prédictions à grande échelle.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E\u003Ci\u003EDans cet épisode d’« AI Builders », Dan Darnell, VP of Marketing chez Sway AI, explique comment créer un environnement no-code pour permettre aux entreprises de toutes tailles d’exploiter le ML et l’analyse prédictive :\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-predictive-analytics_0":{"id":"embed-bc524a46c7","youtubeVideoId":"fRXW-NaAjO4","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","isLcpImage":false,"type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"title_how-predictive-analytics-works":{"id":"title-v2-0730b6ee93","additionalClasses":"anchor-title anchor-title--how-predictive-analytics-works","type":"heading2","lines":["Comment fonctionne l’analyse prédictive ?"],":type":"snowflake-site/components/title-v2"},"text_how-predictive-analytics-works_0":{"id":"text-44800f9c40","text":"\u003Cp\u003EL’analyse prédictive suit un cycle de vie composé de plusieurs étapes étroitement liées. Les décisions prises en amont façonnent toute la suite du processus. Le cycle de vie type se présente ainsi :\u003C/p\u003E\r\n\u003Col\u003E\r\n\u003Cli\u003E\u003Cb\u003ECadrer la décision à prendre :\u003C/b\u003E définissez le résultat attendu, l’horizon de prédiction et l’action associée au résultat.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EConstituer une vue historique des données :\u003C/b\u003E joignez les sources pertinentes et évaluez leur exhaustivité et leur homogénéité.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPréparer les variables utiles au modèle :\u003C/b\u003E transformez les observations brutes en signaux utiles pour l’entraînement et l’inférence.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESélectionner et entraîner un modèle :\u003C/b\u003E choisissez une approche adaptée à la prédiction et aux données disponibles.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EValider sa performance :\u003C/b\u003E testez le modèle sur des données mises de côté, à l’aide de métriques qui reflètent l’objectif métier.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDéployer le processus de prédiction :\u003C/b\u003E alimentez le modèle avec les données actuelles et transmettez ses résultats aux personnes ou aux systèmes qui les utilisent.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESurveiller les changements et réentraîner le modèle :\u003C/b\u003E suivez la performance et actualisez le modèle à mesure que les schémas sous-jacents évoluent.\u003C/li\u003E\r\n\u003C/ol\u003E\r\n\u003Ch3\u003ECommencer par la prédiction à produire\u003C/h3\u003E\r\n\u003Cp\u003ELa première étape consiste à définir suffisamment précisément le résultat que le modèle doit estimer. Cette définition doit préciser :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Ela cible de la prédiction ; \u003C/li\u003E\r\n\u003Cli\u003El’horizon temporel ; \u003C/li\u003E\r\n\u003Cli\u003Ele contexte dans lequel le résultat sera utilisé ; \u003C/li\u003E\r\n\u003Cli\u003El’action associée à la prédiction. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ECe cadrage permet aussi de rattacher le projet à une véritable question métier. Par exemple, un modèle de prédiction du désabonnement ne doit pas se limiter à identifier les clients susceptibles de partir. Il faut également définir la période concernée :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Ele client risque-t-il de partir dans les 30 prochains jours ? \u003C/li\u003E\r\n\u003Cli\u003Edans les 90 prochains jours ? \u003C/li\u003E\r\n\u003Cli\u003Eavant le prochain renouvellement de contrat ?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EConstruire la vue historique\u003C/h3\u003E\r\n\u003Cp\u003EUne fois la cible de prédiction clarifiée, les équipes rassemblent les données historiques pertinentes. Cette étape consiste généralement à :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Erapprocher les données issues de plusieurs sources ;\u003C/li\u003E\r\n\u003Cli\u003Ecorriger les incohérences ;\u003C/li\u003E\r\n\u003Cli\u003Ecréer des variables qui représentent les relations que le modèle doit apprendre.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ECette phase peut prendre du temps lorsque les variables importantes sont réparties entre plusieurs systèmes, lorsque les définitions ont évolué ou lorsque des données sont manquantes ou incohérentes.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ca target=\"_blank\" href=\"https://www.sciencedirect.com/science/article/pii/S2095809924003734\" rel=\"noopener noreferrer\"\u003EDes recherches récentes sur le machine learning industriel\u003C/a\u003E identifient la qualité, la rareté, le déséquilibre et l’hétérogénéité des données comme des contraintes majeures en production. Un modèle ne peut exploiter que les signaux présents dans ses données d’entraînement.\u003C/p\u003E\r\n\u003Cp\u003ELe \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/feature-engineering/\"\u003Efeature engineering\u003C/a\u003E consiste à transformer les observations brutes en signaux plus utiles pour la prédiction. Par exemple, une série de transactions peut être transformée en variables décrivant :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Ela récence ;\u003C/li\u003E\r\n\u003Cli\u003Ela fréquence ;\u003C/li\u003E\r\n\u003Cli\u003El’évolution dans le temps ;\u003C/li\u003E\r\n\u003Cli\u003Eles valeurs décalées dans une série temporelle.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EEntraîner et valider le modèle\u003C/h3\u003E\r\n\u003Cp\u003EUne fois les données d’entraînement préparées, les équipes sélectionnent une famille de modèles adaptée et l’entraînent sur une partie des données historiques. Après l’\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/model-training/\"\u003Eentraînement du modèle\u003C/a\u003E, l’étape suivante consiste à le valider sur des données mises de côté, que le modèle n’a encore jamais vues. Cette séparation permet de mieux mesurer sa capacité à généraliser à de nouveaux cas, plutôt que sa capacité à reproduire l’échantillon historique.\u003C/p\u003E\r\n\u003Cp\u003ELes métriques d’évaluation doivent correspondre au type de prédiction et à l’objectif métier. L’exactitude globale (\u003Ci\u003Eaccuracy\u003C/i\u003E) peut être trompeuse lorsqu’un événement est rare. En détection des fraudes, un modèle qui classe presque toutes les transactions comme légitimes peut obtenir un score élevé tout en manquant les fraudes. La précision (\u003Ci\u003Eprecision\u003C/i\u003E) et le rappel (\u003Ci\u003Erecall\u003C/i\u003E) permettent alors de mieux évaluer sa capacité à détecter les cas importants. \u003C/p\u003E\r\n\u003Ch3\u003EMettre le modèle en production\u003C/h3\u003E\r\n\u003Cp\u003EUn modèle validé a besoin d’un workflow fiable pour transformer de nouvelles données en prédictions utilisables. Les variables doivent être générées de manière cohérente, actualisées selon le calendrier prévu et transmises au modèle sous la même forme que lors de l’entraînement. Dans le cas contraire, un \u003Ca target=\"_blank\" href=\"https://nhimg.org/glossary/train-serving-skew/\" rel=\"noopener noreferrer\"\u003Edécalage entre entraînement et inférence (training-serving skew)\u003C/a\u003E peut dégrader la performance.\u003C/p\u003E\r\n\u003Cp\u003EL’\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/inference/\"\u003Einférence\u003C/a\u003E doit également avoir une fréquence et une destination clairement définies. Une prévision de la demande peut être exécutée chaque nuit, tandis qu’un modèle de détection des fraudes peut attribuer un score à une transaction presque en temps réel. Ces exigences influencent l’architecture de mise en production.\u003C/p\u003E\r\n\u003Cp\u003ELes workflows de production doivent être reproductibles et traçables. Les équipes doivent pouvoir déterminer :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Equelle version du modèle a produit une prédiction ;\u003C/li\u003E\r\n\u003Cli\u003Equelles données et quelle logique de variables l’ont alimentée ;\u003C/li\u003E\r\n\u003Cli\u003Equand le modèle a été réentraîné pour la dernière fois.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003ESurveiller les changements\u003C/h3\u003E\r\n\u003Cp\u003ELe déploiement d’un modèle ne marque pas la fin du cycle de vie. La performance prédictive dépend du maintien des relations apprises à partir des données historiques.\u003C/p\u003E\r\n\u003Cp\u003EAu fil du temps, les données entrantes peuvent changer, la relation entre une variable et un résultat peut s’affaiblir ou de nouveaux schémas peuvent apparaître. La \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/observability/model-monitoring/\"\u003Esurveillance\u003C/a\u003E permet de détecter ces évolutions avant qu’elles ne se traduisent par une baisse visible de la qualité des prédictions.\u003C/p\u003E\r\n\u003Cp\u003ELe réentraînement permet au modèle d’intégrer de nouvelles observations. Selon le cas d’usage, il peut être déclenché :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eselon un calendrier défini ; \u003C/li\u003E\r\n\u003Cli\u003Elorsque la dérive des données est détectée ; \u003C/li\u003E\r\n\u003Cli\u003Elorsque la performance descend sous un seuil donné ; \u003C/li\u003E\r\n\u003Cli\u003Eaprès un changement important du processus métier.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EUne \u003Ca target=\"_blank\" href=\"https://www.sciencedirect.com/science/article/abs/pii/S1532046425001315\" rel=\"noopener noreferrer\"\u003Eétude de 2025 sur les systèmes de ML prédictifs\u003C/a\u003E a montré que le décalage temporel et la dérive conceptuelle sont des sources courantes de changement après le déploiement. Les schémas appris pendant l’entraînement doivent donc être réévalués lorsque l’environnement change.\u003C/p\u003E\r\n\u003Ch3\u003EChoisir le niveau de granularité du modèle\u003C/h3\u003E\r\n\u003Cp\u003ELe passage à l’échelle soulève une autre question : combien de modèles faut-il maintenir ? Un modèle par client, produit ou localisation peut capturer davantage de variations locales. Toutefois, chaque modèle supplémentaire implique ses propres besoins :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eentraînement ; \u003C/li\u003E\r\n\u003Cli\u003Esurveillance ; \u003C/li\u003E\r\n\u003Cli\u003Egestion des versions ; \u003C/li\u003E\r\n\u003Cli\u003Eréentraînement ; \u003C/li\u003E\r\n\u003Cli\u003Evalidation.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EÀ grande échelle, cette approche peut créer des centaines ou des milliers de workflows parallèles à administrer.\u003C/p\u003E\r\n\u003Cp\u003EUn modèle unique entraîné sur plusieurs entités peut parfois réduire la charge opérationnelle tout en capturant les variations nécessaires. Le bon niveau de granularité dépend donc à la fois de la performance prédictive attendue et du coût de maintenance du parc de modèles.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2":{"id":"card-v2-05df7529a1","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/customers/all-customers/case-study/igs-energy/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire l’étude de cas complète"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["TÉMOIGNAGE CLIENT : IGS Energy"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"234","src":"/adobe/dynamicmedia/deliver/dm-aid--0ed8d846-9c35-47d1-ace5-c3bd1b3aef79/fireblocks.png?quality=85&preferwebp=true","alt":"Fireblocks logo","lazyEnabled":true,"isLcpImage":false,"width":"416",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EPour prévoir la consommation énergétique de ses clients, IGS Energy est passé d’un modèle par client à un modèle unifié dans Snowflake. Selon son témoignage, cette approche a réduit les coûts d’entraînement de 75 %, tout en maintenant la précision des prévisions. Elle illustre un enjeu central du passage à l’échelle : choisir une granularité de modélisation adaptée sans multiplier inutilement les modèles à maintenir.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_types-of-predictive-analytics-models":{"id":"title-v2-2c212ef199","additionalClasses":"anchor-title anchor-title--types-of-predictive-analytics-models","type":"heading2","lines":["Types de modèles d’analyse prédictive"],":type":"snowflake-site/components/title-v2"},"text_types-of-predictive-analytics-models_0":{"id":"text-1e9f7fa489","text":"\u003Cp\u003ELe choix d’un modèle devient plus simple lorsque la question prédictive est clairement définie. La plupart des problèmes d’analyse prédictive appartiennent à quelques grandes familles :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EClassification :\u003C/b\u003E à quelle catégorie cette observation appartient-elle ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ERégression :\u003C/b\u003E quelle valeur numérique peut-on attendre ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EClustering :\u003C/b\u003E quels groupes naturels existent dans les données ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESéries temporelles :\u003C/b\u003E que va-t-il probablement se produire ensuite ?\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EMéthodes d’ensemble :\u003C/b\u003E quel résultat obtient-on en combinant plusieurs modèles ?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ENotez que les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/\"\u003Efamilles de modèles\u003C/a\u003E et les techniques de modélisation sont deux choses différentes. La classification décrit un objectif, tandis que plusieurs techniques peuvent être utilisées pour l’atteindre.\u003C/p\u003E\r\n\u003Ch3\u003EModèles de classification\u003C/h3\u003E\r\n\u003Cp\u003ELes modèles de classification attribuent des observations à des catégories ou estiment la probabilité qu’une observation appartienne à une classe donnée. Un modèle de désabonnement peut par exemple estimer la probabilité qu’un client cesse d’utiliser un service au cours des 90 prochains jours. Un modèle de risque peut également répartir des candidats ou des comptes entre plusieurs catégories prédéfinies.\u003C/p\u003E\r\n\u003Cp\u003EDes techniques comme la régression logistique, les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/decision-tree/\"\u003Earbres de décision\u003C/a\u003E, les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/random-forest/\"\u003Eforêts aléatoires\u003C/a\u003E, le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/what-is-gradient-boosting/\"\u003Egradient boosting\u003C/a\u003E et les \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/neural-network/\"\u003Eréseaux de neurones\u003C/a\u003E permettent toutes de traiter des problèmes de classification. Le bon choix dépend en partie des exigences de performance, et en partie du niveau d’explication qu’exige la décision.\u003C/p\u003E\r\n\u003Ch3\u003EModèles de régression\u003C/h3\u003E\r\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/regression/\"\u003Emodèles de régression\u003C/a\u003E estiment des valeurs numériques continues. Les projections de chiffre d’affaires, la valeur vie client et les estimations de prix sont des exemples courants. La \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/models/linear-regression/\"\u003Erégression linéaire\u003C/a\u003E constitue un point de départ relativement transparent. Les modèles fondés sur des arbres, les méthodes d’ensemble et les réseaux de neurones peuvent prendre en charge des relations plus complexes.\u003C/p\u003E\r\n\u003Cp\u003EPour utiliser un modèle de régression, l’équipe doit savoir :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Equel résultat numérique l’entreprise cherche à estimer ; \u003C/li\u003E\r\n\u003Cli\u003Equel niveau de précision est nécessaire ; \u003C/li\u003E\r\n\u003Cli\u003Equelle décision sera prise à partir de cette estimation.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EModèles de clustering\u003C/h3\u003E\r\n\u003Cp\u003ELes algorithmes de clustering recherchent des groupes naturellement présents dans les données, sans partir d’une étiquette cible prédéfinie. Le \u003Ca href=\"https://stanford.edu/~cpiech/cs221/handouts/kmeans.html\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eclustering K-means\u003C/a\u003E peut par exemple identifier des clients dont les habitudes d’achat sont similaires, même si l’entreprise n’a pas défini ces segments à l’avance. Ces groupes peuvent ensuite servir à :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Esegmenter une clientèle ; \u003C/li\u003E\r\n\u003Cli\u003Epersonnaliser une campagne ; \u003C/li\u003E\r\n\u003Cli\u003Ecibler une offre ; \u003C/li\u003E\r\n\u003Cli\u003Eapprofondir une analyse.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELe clustering découvre une structure plutôt qu’il ne prédit une cible connue. Il est donc utilisé différemment de la classification et de la régression, notamment au début d’une démarche analytique.\u003C/p\u003E\r\n\u003Ch3\u003EModèles de séries temporelles\u003C/h3\u003E\r\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/time-series-analysis/\"\u003Emodèles de séries temporelles\u003C/a\u003E utilisent des observations ordonnées dans le temps pour estimer des valeurs futures. La demande, les ventes, la charge énergétique, la performance financière et les niveaux de stock entrent tous naturellement dans cette catégorie. La saisonnalité, la tendance et l’horizon de prévision déterminent la conception du modèle. Les techniques vont, quant à elles, d’approches statistiques comme \u003Ca href=\"https://www.datacamp.com/tutorial/arima\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EARIMA\u003C/a\u003E à des méthodes de ML entraînées sur plusieurs séries liées.\u003C/p\u003E\r\n\u003Cp\u003ECette famille de modèles est très présente dans l’analyse prédictive d’entreprise, car de nombreuses questions métier comportent une dimension temporelle : De quelle quantité de stock aurons-nous besoin le mois prochain ? Quel sera le chiffre d’affaires du prochain trimestre ? Quelle sera la demande énergétique demain ?\u003C/p\u003E\r\n\u003Ch3\u003EMéthodes d’ensemble\u003C/h3\u003E\r\n\u003Cp\u003ELes \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/ensemble-learning/\"\u003Eméthodes d’ensemble\u003C/a\u003E combinent plusieurs modèles pour améliorer la stabilité ou la performance prédictive. Les forêts aléatoires et le gradient boosting en sont des exemples bien connus. D’autres méthodes agrègent les résultats de modèles entraînés séparément, ce qui réduit la dépendance à l’égard des forces et faiblesses d’un modèle unique.\u003C/p\u003E\r\n\u003Cp\u003ECette amélioration peut toutefois s’accompagner d’un compromis. Une méthode d’ensemble complexe ou un réseau neuronal est généralement moins facile à expliquer. Dans les environnements réglementés, où un auditeur ou un client peut avoir besoin de comprendre comment une décision a été prise, l’interprétabilité est non négociable.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_generative-ai-in-predictive-analytics":{"id":"title-v2-b381884ea7","additionalClasses":"anchor-title anchor-title--generative-ai-in-predictive-analytics","type":"heading2","lines":["IA générative dans l’analyse prédictive"],":type":"snowflake-site/components/title-v2"},"text_generative-ai-in-predictive-analytics_0":{"id":"text-119eda61df","text":"\u003Cp\u003ELes modèles prédictifs estiment un résultat futur défini, comme la demande, la probabilité de désabonnement ou le risque de panne, à partir de données historiques. L’\u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/generative-ai/\"\u003EIA générative\u003C/a\u003E produit de nouveaux contenus (texte, images ou code) à partir de schémas appris pendant l’entraînement.\u003C/p\u003E\n\u003Cp\u003ECes deux approches sont de plus en plus complémentaires. Une plateforme d’analyse prédictive alimentée par l’IA peut par exemple utiliser un modèle génératif pour expliquer ou résumer le résultat d’un modèle prédictif. Le modèle prédictif continue quant à lui à produire la prévision ou la probabilité.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_predictive-analytics-tools-and-platforms":{"id":"title-v2-1cf000c73a","additionalClasses":"anchor-title anchor-title--predictive-analytics-tools-and-platforms","type":"heading2","lines":["Outils et plateformes d’analyse prédictive"],":type":"snowflake-site/components/title-v2"},"text_predictive-analytics-tools-and-platforms_0":{"id":"text-477c01493f","text":"\u003Cp\u003EUn outil ou une plateforme d’analyse prédictive doit prendre en charge l’ensemble du workflow :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003El’endroit où le modèle est entraîné ; \u003C/li\u003E\r\n\u003Cli\u003El’endroit où les données sont stockées ; \u003C/li\u003E\r\n\u003Cli\u003Eles personnes qui peuvent créer ou exécuter les modèles ; \u003C/li\u003E\r\n\u003Cli\u003Ela destination des prédictions ; \u003C/li\u003E\r\n\u003Cli\u003Ela surveillance et le réentraînement. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ECes choix d’architecture ont souvent plus d’impact opérationnel que la différence entre deux bibliothèques de modélisation.\u003C/p\u003E\r\n\u003Cp\u003ELors de l’évaluation d’un outil d’analyse prédictive, posez-vous les questions suivantes :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003EOù le modèle est-il entraîné par rapport aux données sources gouvernées ?\u003C/li\u003E\r\n\u003Cli\u003ELes données doivent-elles être copiées dans un autre environnement ?\u003C/li\u003E\r\n\u003Cli\u003EComment la prédiction atteint-elle une application, un tableau de bord ou un processus métier ?\u003C/li\u003E\r\n\u003Cli\u003EQuels utilisateurs peuvent créer ou exécuter des charges de travail prédictives ?\u003C/li\u003E\r\n\u003Cli\u003EComment les versions des modèles, le lignage et le réentraînement sont-ils suivis ?\u003C/li\u003E\r\n\u003Cli\u003EQuelle infrastructure est nécessaire pour l’inférence ?\u003C/li\u003E\r\n\u003Cli\u003EComment les coûts évoluent-ils avec le nombre de modèles ou de prédictions ?\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELe déplacement des données mérite une attention particulière. Copier des données gouvernées dans un environnement de ML distinct crée :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eune nouvelle version des données à synchroniser ; \u003C/li\u003E\r\n\u003Cli\u003Eun pipeline supplémentaire à maintenir ; \u003C/li\u003E\r\n\u003Cli\u003Eune nouvelle frontière de gouvernance ; \u003C/li\u003E\r\n\u003Cli\u003Eun risque de décalage entre les données sources et les données utilisées par le modèle. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESi la copie n’est plus à jour ou si un pipeline en amont échoue, le modèle ne fonctionne plus sur l’état actuel de l’activité.\u003C/p\u003E\r\n\u003Cp\u003ELe profil des utilisateurs évolue, lui aussi. Les fonctions prédictives natives en SQL permettent aux analystes et aux analytics engineers d’exécuter des charges de travail courants, comme la prévision et la détection des anomalies, sans faire appel à une équipe de data science pour chaque demande. Les modèles personnalisés exigent toujours des environnements orientés code lorsque les équipes ont besoin de feature engineering, d’algorithmes ou d’évaluations spécialisés. Ces deux approches peuvent coexister, sans imposer une stack différente pour chaque type de charge de travail prédictif.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_predictive-analytics-use-cases-and-business-impact":{"id":"title-v2-0151c98dc8","additionalClasses":"anchor-title anchor-title--predictive-analytics-use-cases-and-business-impact","type":"heading2","lines":["Cas d’usage de l’analyse prédictive et impact métier"],":type":"snowflake-site/components/title-v2"},"text_predictive-analytics-use-cases-and-business-impact_0":{"id":"text-dcf23f2806","text":"\u003Cp\u003ELes listes de secteurs peuvent donner l’impression que l’analyse prédictive recouvre une multitude de problèmes différents. En réalité, la plupart des cas d’usage reposent sur quelques questions récurrentes :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eprévoir la demande future ; \u003C/li\u003E\r\n\u003Cli\u003Eestimer la probabilité d’un événement ; \u003C/li\u003E\r\n\u003Cli\u003Eidentifier un comportement anormal ; \u003C/li\u003E\r\n\u003Cli\u003Eanticiper une panne ; \u003C/li\u003E\r\n\u003Cli\u003Eattribuer un niveau de risque.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EPar exemple, un fournisseur d’énergie qui réalise la prévision de sa charge énergétique et un détaillant qui prévoit la demande de produits résolvent des problèmes analytiques très proches, même si les processus métier associés n’ont rien à voir.\u003C/p\u003E\r\n\u003Ch3\u003EPrévision de la demande et du chiffre d’affaires\u003C/h3\u003E\r\n\u003Cp\u003ELa prévision utilise les schémas historiques, la saisonnalité et d’autres signaux pour estimer l’activité future. La valeur ne vient pas uniquement de la prévision. Elle apparaît ensuite dans les décisions prises à partir de celle-ci. Une estimation plus rapide ou plus précise peut modifier les volumes de stock commandés, l’allocation des ressources ou les scénarios auxquels se prépare une équipe financière.\u003C/p\u003E\r\n\u003Ch3\u003EPrédiction du désabonnement et de la fidélisation\u003C/h3\u003E\r\n\u003Cp\u003ELes modèles de désabonnement estiment la probabilité qu’un client cesse d’utiliser un produit ou un service. Le moment où la prédiction est produite est essentiel. Une information reçue après le départ du client a peu de valeur opérationnelle. Les équipes doivent disposer du signal suffisamment tôt pour déclencher :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eune prise de contact ; \u003C/li\u003E\r\n\u003Cli\u003Eune intervention du service client ; \u003C/li\u003E\r\n\u003Cli\u003Eune offre de fidélisation ; \u003C/li\u003E\r\n\u003Cli\u003Eune action commerciale. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELe modèle et le processus métier doivent donc être conçus ensemble.\u003C/p\u003E\r\n\u003Ch3\u003EDétection des fraudes et des anomalies\u003C/h3\u003E\r\n\u003Cp\u003ELa détection des fraudes recherche des schémas associés à un comportement suspect. La détection des anomalies identifie les observations qui s’écartent d’une référence attendue.\u003C/p\u003E\r\n\u003Cp\u003ELes établissements financiers peuvent utiliser ces approches pour signaler des transactions ou des comptes potentiellement frauduleux. Les mêmes techniques peuvent s’appliquer :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eaux métriques opérationnelles ; \u003C/li\u003E\r\n\u003Cli\u003Eà la performance des équipements ; \u003C/li\u003E\r\n\u003Cli\u003Eaux événements de sécurité ; \u003C/li\u003E\r\n\u003Cli\u003Eaux processus métier ; \u003C/li\u003E\r\n\u003Cli\u003Eaux environnements IoT.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELa valeur prédictive vient souvent de la capacité à concentrer l’attention des équipes. Au lieu d’examiner tous les événements de la même manière, elles peuvent se concentrer sur ceux qui nécessitent une action.\u003C/p\u003E\r\n\u003Ch3\u003EMaintenance prédictive\u003C/h3\u003E\r\n\u003Cp\u003ELes entreprises industrielles et les organisations qui exploitent des actifs critiques utilisent les données des équipements, des capteurs et de la maintenance pour estimer la probabilité d’une panne. Une alerte précoce permet aux opérateurs de planifier la maintenance avant qu’une panne n’interrompe la production. Le bénéfice apparaît généralement sous la forme :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Ed’une réduction des arrêts non planifiés ; \u003C/li\u003E\r\n\u003Cli\u003Ed’une diminution des réparations d’urgence ; \u003C/li\u003E\r\n\u003Cli\u003Ed’une meilleure utilisation des équipes de maintenance ; \u003C/li\u003E\r\n\u003Cli\u003Ed’une prolongation de la durée de vie des équipements.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EScoring des risques\u003C/h3\u003E\r\n\u003Cp\u003ELes modèles prédictifs peuvent également estimer la probabilité d’un défaut de paiement, d’un sinistre, d’un départ client ou d’un autre événement associé à un risque métier. Un score permet à l’entreprise de hiérarchiser les situations et d’appliquer différents workflows selon le niveau de risque. Comme pour les autres cas d’usage, la valeur dépend de ce qui se passe une fois le score produit.\u003C/p\u003E\r\n\u003Cp\u003EDans tous ces scénarios, l’analyse prédictive réduit le délai entre l’apparition d’un signal important dans les données et la réponse de l’organisation.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_getting-predictive-models-into-production":{"id":"title-v2-66ac444cf7","additionalClasses":"anchor-title anchor-title--getting-predictive-models-into-production","type":"heading2","lines":["Mettre les modèles prédictifs en production"],":type":"snowflake-site/components/title-v2"},"text_getting-predictive-models-into-production_0":{"id":"text-eb8f7bd29f","text":"\u003Cp\u003EUn modèle entraîné ne constitue qu’une partie d’un workflow prédictif en production. Les variables doivent parvenir au modèle selon le calendrier prévu. L’inférence doit s’exécuter de manière fiable et les prédictions doivent être transmises aux systèmes ou aux personnes qui les utilisent. Les équipes doivent également disposer d’un niveau de lignage suffisant pour relier une prédiction :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eà la version du modèle ; \u003C/li\u003E\r\n\u003Cli\u003Eaux données utilisées ; \u003C/li\u003E\r\n\u003Cli\u003Eaux variables produites ; \u003C/li\u003E\r\n\u003Cli\u003Eà la logique de transformation.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Ch3\u003EConcevoir des workflows reproductibles\u003C/h3\u003E\r\n\u003Cp\u003ELa mise en production se dégrade rapidement lorsqu’un modèle dépend :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Ed’un jeu de données assemblé manuellement ; \u003C/li\u003E\r\n\u003Cli\u003Ede transformations non documentées ; \u003C/li\u003E\r\n\u003Cli\u003Ed’un notebook que seule la personne qui l’a créé sait exécuter. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELa préparation des données et la logique de création des variables utilisées pendant l’entraînement doivent pouvoir être reproduites pendant l’inférence. Des processus versionnés et reproductibles réduisent également l’écart entre les données d’entraînement et les données de production.\u003C/p\u003E\r\n\u003Ch3\u003EConcevoir des modèles à grande échelle\u003C/h3\u003E\r\n\u003Cp\u003ELe nombre de modèles a un effet direct sur les coûts d’exploitation. Un modèle distinct pour chaque client, référence produit ou localisation multiplie :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eles tâches d’entraînement ; \u003C/li\u003E\r\n\u003Cli\u003Eles versions à gérer ; \u003C/li\u003E\r\n\u003Cli\u003Eles workflows de surveillance ; \u003C/li\u003E\r\n\u003Cli\u003Eles calendriers de réentraînement. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EDans certains cas, un modèle unifié entraîné sur de nombreuses entités peut capturer la variabilité nécessaire avec beaucoup moins de composants à exploiter.\u003C/p\u003E\r\n\u003Ch3\u003ERelier la gouvernance au cycle de vie du modèle\u003C/h3\u003E\r\n\u003Cp\u003ELes données d’entraînement, les définitions des variables et les versions des modèles doivent être associées à un lignage lorsque les prédictions sont utilisées en production. Si les données sous-jacentes sont déjà gouvernées là où elles résident, les équipes peuvent préserver ces contrôles et ces relations tout au long du processus prédictif. Copier les données dans un autre environnement impose de recréer et de maintenir les politiques, le lignage et les définitions à un nouvel endroit. Commencer par un cas d’usage clairement défini et mesurable permet d’établir un parcours de production complet avant de le déployer sur davantage de modèles et de charges de travail.\u003C/p\u003E\r\n\u003Ch3\u003ELes modèles prédictifs ne sont pas statiques\u003C/h3\u003E\r\n\u003Cp\u003EUn modèle entraîné une seule fois et laissé sans maintenance finit par décrire une version ancienne de son environnement. Le réentraînement planifié permet au modèle d’accéder à des observations plus récentes. La surveillance des modèles vérifie si la performance évolue. La détection de la dérive des données et des concepts aide, quant à elle, à identifier les changements dans les données d’entrée ou dans la relation entre ces entrées et le résultat. Les tests champion-challenger offrent une autre approche : conserver le modèle de production actuel tout en évaluant une alternative par rapport à celui-ci.\u003C/p\u003E\r\n\u003Cp\u003ELes charges de travail de séries temporelles nécessitent une attention particulière lors de la validation. Une répartition aléatoire des données d’entraînement et de test peut exposer le modèle à des informations futures qui n’auraient pas été disponibles au moment de la prédiction. La validation croisée des séries temporelles préserve l’ordre chronologique. Les variables décalées permettent au modèle d’utiliser les observations passées sans introduire d’informations futures dans l’entraînement. Ces pratiques maintiennent les modèles prédictifs en phase avec les conditions qu’ils sont censés prédire.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_why-run-predictive-analytics-on-snowflake":{"id":"title-v2-50bd509aad","additionalClasses":"anchor-title anchor-title--why-run-predictive-analytics-on-snowflake","type":"heading2","lines":["Pourquoi exécuter l’analyse prédictive sur Snowflake ?"],":type":"snowflake-site/components/title-v2"},"text_why-run-predictive-analytics-on-snowflake_0":{"id":"text-ad33980f19","text":"\u003Cp\u003EL’analyse prédictive est plus efficace lorsque les modèles restent étroitement connectés aux données qu’ils utilisent. Exécuter les charges de travail prédictives dans le même environnement gouverné que les données d’entreprise réduit le besoin de créer :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Edes copies séparées ; \u003C/li\u003E\r\n\u003Cli\u003Edes pipelines de synchronisation ; \u003C/li\u003E\r\n\u003Cli\u003Edes contrôles de gouvernance supplémentaires ; \u003C/li\u003E\r\n\u003Cli\u003Edes environnements ML isolés des données sources. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EL'AI Data Cloud de Snowflake applique ce même principe : les workflows de machine learning, les applications d’IA et les agents peuvent fonctionner dans l’environnement gouverné où résident les données sous-jacentes.\u003C/p\u003E\r\n\u003Cp\u003ESnowflake prend en charge deux approches pour les travaux prédictifs. Pour les cas d’usage courants, les \u003Ca href=\"https://docs.snowflake.com/en/guides-overview-ml-functions\"\u003Efonctions ML\u003C/a\u003E proposent des fonctionnalités intégrées de :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003Eprévision ; \u003C/li\u003E\r\n\u003Cli\u003Edétection des anomalies ; \u003C/li\u003E\r\n\u003Cli\u003Eclassification. \u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ELes équipes peuvent entraîner et utiliser ces modèles en SQL, notamment pour évaluer la performance des modèles et les appliquer à de nouvelles données.\u003C/p\u003E\r\n\u003Cp\u003EPour les modèles personnalisés, \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-ml/overview\"\u003ESnowflake ML\u003C/a\u003E prend en charge un cycle de vie plus complet du machine learning, notamment :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003El’ingénierie des variables (\u003Ci\u003Efeature engineering\u003C/i\u003E) ; \u003C/li\u003E\r\n\u003Cli\u003El’entraînement ; \u003C/li\u003E\r\n\u003Cli\u003Ela gestion des modèles ; \u003C/li\u003E\r\n\u003Cli\u003Ele \u003Ca href=\"https://www.snowflake.com/fr/artificial-intelligence/machine-learning/mlops/\"\u003EMLOps.\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003ESnowpark permet aux équipes de développer des workflows de machine learning en Python sur les données dans Snowflake. Snowflake Notebooks fournit, quant à lui, un environnement intégré pour développer, expérimenter et analyser les résultats.\u003C/p\u003E\r\n\u003Cp\u003ECes approches répondent à différents niveaux de personnalisation, mais elles partagent le même avantage fondamental : les charges de travail prédictives restent connectées à des données gouvernées et à jour sur une plus grande partie du cycle de vie du modèle.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_trustworthiness-depends-on-the-surrounding-lifecycle":{"id":"title-v2-c3d9d96e02","additionalClasses":"anchor-title anchor-title--trustworthiness-depends-on-the-surrounding-lifecycle","type":"heading2","lines":["La fiabilité dépend de tout le cycle de vie"],":type":"snowflake-site/components/title-v2"},"text_trustworthiness-depends-on-the-surrounding-lifecycle_0":{"id":"text-dac914dca1","text":"\u003Cp\u003EL’analyse prédictive est, au fond, un exercice de généralisation. Les modèles apprennent à partir de données historiques, puis rencontrent des clients, des transactions, des équipements et des conditions opérationnelles qu’ils n’ont jamais vus auparavant.\u003C/p\u003E\n\u003Cp\u003EUn système prédictif fiable nécessite donc davantage qu’un bon résultat lors de l’entraînement. Les données doivent rester représentatives. L’évaluation doit refléter la tâche de prédiction réelle. La surveillance doit détecter le moment où les schémas historiques cessent de s’appliquer. Lorsque les conditions changent, le réentraînement et la gestion des modèles permettent de maintenir la cohérence entre la prédiction et l’environnement qu’elle cherche à décrire.\u003C/p\u003E\n\u003Cp\u003ECette logique s’applique aux méthodes statistiques classiques, à l’IA prédictive et aux systèmes qui combinent prédiction et IA générative. Le modèle produit une estimation de ce qui pourrait se produire. Le cycle de vie qui l’entoure détermine si cette estimation reste fiable dans le temps.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_trustworthiness-depends-on-the-surrounding-lifecycle_0":{"id":"text-b5f8672787","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EÀ RETENIR\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003ELes modèles prédictifs apprennent à partir de schémas historiques, mais leur véritable test intervient lorsqu’ils rencontrent de nouvelles données. Une analyse prédictive fiable dépend de données représentatives, d’une évaluation adaptée, d’une surveillance continue et d’un cycle de vie capable de prendre en compte l’évolution des conditions.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},":itemsOrder":["callout__0","text__0","title_what-is-predictive-analytics","text_what-is-predictive-analytics_0","yt_what-is-predictive-analytics_0","title_how-predictive-analytics-works","text_how-predictive-analytics-works_0","card_v2","title_types-of-predictive-analytics-models","text_types-of-predictive-analytics-models_0","title_generative-ai-in-predictive-analytics","text_generative-ai-in-predictive-analytics_0","title_predictive-analytics-tools-and-platforms","text_predictive-analytics-tools-and-platforms_0","title_predictive-analytics-use-cases-and-business-impact","text_predictive-analytics-use-cases-and-business-impact_0","title_getting-predictive-models-into-production","text_getting-predictive-models-into-production_0","title_why-run-predictive-analytics-on-snowflake","text_why-run-predictive-analytics-on-snowflake_0","title_trustworthiness-depends-on-the-surrounding-lifecycle","text_trustworthiness-depends-on-the-surrounding-lifecycle_0","callout_trustworthiness-depends-on-the-surrounding-lifecycle_0"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-e51abb844a",":items":{"text_943981956_copy_":{"id":"text-c54f215449","additionalClasses":"eyebrow-text","text":"\u003Cp\u003EDans ce guide\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular"},"text_copy":{"id":"text-f6ea6df62a","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-predictive-analytics\"\u003EQu’est-ce que l’analyse prédictive ?\u003C/li\u003E\u003Cli data-anchor=\"how-predictive-analytics-works\"\u003EComment fonctionne l’analyse prédictive ?\u003C/li\u003E\u003Cli data-anchor=\"types-of-predictive-analytics-models\"\u003ETypes de modèles d’analyse prédictive\u003C/li\u003E\u003Cli data-anchor=\"generative-ai-in-predictive-analytics\"\u003EIA générative dans l’analyse prédictive\u003C/li\u003E\u003Cli data-anchor=\"predictive-analytics-tools-and-platforms\"\u003EOutils et plateformes d’analyse prédictive\u003C/li\u003E\u003Cli data-anchor=\"predictive-analytics-use-cases-and-business-impact\"\u003ECas d’usage de l’analyse prédictive et impact métier\u003C/li\u003E\u003Cli data-anchor=\"getting-predictive-models-into-production\"\u003EMettre les modèles prédictifs en production\u003C/li\u003E\u003Cli data-anchor=\"why-run-predictive-analytics-on-snowflake\"\u003EPourquoi exécuter l’analyse prédictive sur Snowflake ?\u003C/li\u003E\u003Cli data-anchor=\"trustworthiness-depends-on-the-surrounding-lifecycle\"\u003ELa fiabilité dépend de tout le cycle de vie\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},":itemsOrder":["text_943981956_copy_","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1786318617":{"id":"flexible-column-container-8f992aab64","propertiesId":"hub-faq","type":"2-column-40-60","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-faq-intro",":items":{"title_v2_copy":{"id":"title-v2-07bc7d55a7","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Foire aux questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-409d19c8a0","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes experts Snowflake répondent à vos questions les plus fréquentes sur l’analyse prédictive.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"hub-faq-accordions",":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-9f60cc698c","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"L’analyse prédictive est-elle la même chose que le machine learning ?","richText":"\u003Cp\u003ENon. Le machine learning regroupe des techniques qui permettent d’apprendre des schémas à partir de données. L’analyse prédictive décrit un objectif analytique : estimer une valeur, une probabilité ou un résultat futur. Le machine learning est souvent utilisé pour l’analyse prédictive, mais les méthodes statistiques classiques peuvent également produire des modèles prédictifs.\u003C/p\u003E\r\n"},{"title":"Quelle est la différence entre l’IA et l’analyse prédictive ?","richText":"\u003Cp\u003EL’intelligence artificielle est un domaine plus large qui englobe le machine learning et d’autres approches. L’analyse prédictive poursuit un objectif plus précis : utiliser des données historiques pour estimer des résultats futurs. Les systèmes prédictifs modernes peuvent également intégrer une surveillance continue et un réentraînement planifié. Ils ne sont donc pas nécessairement limités à des prévisions ponctuelles et statiques.\u003C/p\u003E\r\n"},{"title":"De quelle quantité de données historiques avez-vous besoin pour l’analyse prédictive ?","richText":"\u003Cp\u003ECela dépend du schéma que le modèle doit apprendre. Pour une prévision saisonnière, il faut disposer de suffisamment d’historique pour observer plusieurs cycles saisonniers. Deux cycles complets constituent souvent un minimum utile. Pour la classification, le nombre et l’équilibre des exemples dans chaque classe sont généralement plus importants qu’un nombre universel de lignes. Il n’existe donc pas de recommandation unique applicable à tous les cas d’usage.\u003C/p\u003E\r\n"},{"title":"Quel niveau d’exactitude suffit pour un modèle prédictif ?","richText":"\u003Cp\u003ELe seuil de performance doit dépendre de la décision prise. Un modèle doit améliorer suffisamment le processus qu’il remplace pour justifier son coût et les conséquences éventuelles de ses erreurs. L'exactitude globale peut également masquer une mauvaise performance sur les événements rares. Dans ce cas, des mesures comme la précision (precision) et le rappel (recall) peuvent être plus informatives.\u003C/p\u003E\r\n"},{"title":"Faut-il des data scientists pour faire de l’analyse prédictive ?","richText":"\u003Cp\u003EPas pour tous les cas d’usage. Les fonctions prédictives intégrées permettent aux utilisateurs SQL d’exécuter des workloads tels que la prévision, la classification et la détection des anomalies sans développer un modèle personnalisé. Les modèles sur mesure, le feature engineering spécialisé et les workflows d’évaluation complexes nécessitent généralement des compétences en data science ou en machine learning.\u003C/p\u003E\r\n"}],":type":"snowflake-site/components/simple-snowflake-accordion"}},":itemsOrder":["simple_snowflake_acc"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1467213961":{"id":"flexible-column-container-e97ce90427","propertiesId":"hub-explore-resources-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-f9cca74889",":items":{"title_v2":{"id":"title-v2-b69ce57f9f","additionalClasses":"hub-explore-resources-header__headline","type":"heading2","lines":["Découvrir les ressources en analytique"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"}},":itemsOrder":["title_v2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_912630531":{"id":"flexible-column-container-cc91100151","propertiesId":"hub-explore-resources-grid","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"hub-explore-resources-grid-inner",":items":{"resource_chip_0":{"id":"content-chip-2b48007420","tagText":"BLOG","tagColor":"#29B5E8","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/en/blog/retail-like-for-like-growth-ai-data-trends-2026/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Analyse prédictive dans le retail pour une croissance à périmètre constant (EN)"],":type":"snowflake-site/components/title-v2"},"appliedCssClassNames":"snowflake-content-chip-white-bg",":type":"snowflake-site/components/content-chip"},"resource_chip_1":{"id":"content-chip-5dff27b491","tagText":"EBOOK","tagColor":"#71D3DC","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/resources/ebook/essential-guide-data-analytics-ai/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Le guide indispensable de l’analyse de données"],":type":"snowflake-site/components/title-v2"},"appliedCssClassNames":"snowflake-content-chip-white-bg",":type":"snowflake-site/components/content-chip"},"resource_chip_2":{"id":"content-chip-1b23afa015","tagText":"BLOG","tagColor":"#71D3DC","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.snowflake.com/fr/blog/ml-powered-functions-improve-speed-quality/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Regarder maintenant"},"headline":{"id":"title","type":"heading5","lines":["Avec le ML, Snowflake accélère et affine la prise de décisions"],":type":"snowflake-site/components/title-v2"},"appliedCssClassNames":"snowflake-content-chip-white-bg",":type":"snowflake-site/components/content-chip"},"resource_chip_3":{"id":"content-chip-95983cb6aa","tagText":"BLOG","tagColor":"#29B5E8","cta":{"id":"cta","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/blog/scalable-model-development-production-snowflake-ml/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la suite"},"headline":{"id":"title","type":"heading5","lines":["Éliminez les silos de données : créez, déployez et servez des modèles à grande échelle avec Snowflake ML"],":type":"snowflake-site/components/title-v2"},"appliedCssClassNames":"snowflake-content-chip-white-bg",":type":"snowflake-site/components/content-chip"}},":itemsOrder":["resource_chip_0","resource_chip_1","resource_chip_2","resource_chip_3"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_1158003461":{"id":"flexible-column-container-0b4ad46724","propertiesId":"hub-explore-topics-header","type":"1-column","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"large","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-9d710a0f65",":items":{"title_v2_copy_copy":{"id":"title-v2-a0c53d168f","additionalClasses":"hub-explore-topics-header__headline","type":"heading2","lines":["Explorer les thèmes liés à l’analytique"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_copy":{"id":"text-3555a40d87","additionalClasses":"hub-explore-topics-header__subheadline","text":"\u003Cp\u003EDes analyses approfondies de chaque aspect de l’analytique\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2_copy_copy","text_copy_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-white-bg"},"flexible_column_cont_1377146023":{"id":"flexible-column-container-c6715e2a0d","propertiesId":"hub-explore-topics-grid","type":"3-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"small","bottomPadding":"large","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-342f129c30",":items":{"topic_card_0":{"id":"card-v2-be388745b0","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/analytics/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"En savoir plus"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Analytique"],":type":"snowflake-site/components/title-v2"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EL’utilisation systématique des données pour surveiller les performances, expliquer les changements, estimer les résultats futurs et guider les décisions métier.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["topic_card_0"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-58c2d1983d",":items":{"topic_card_1":{"id":"card-v2-ce5a67b7f0","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/analytics/business-intelligence/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"En savoir plus"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Business Intelligence"],":type":"snowflake-site/components/title-v2"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003ELa collecte, l’intégration et l’analyse des données d’entreprise pour fournir des informations exploitables, alimenter le reporting et éclairer les décisions opérationnelles.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["topic_card_1"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_3":{"layout":"SIMPLE","id":"container-d7dc80e949",":items":{"topic_card_2":{"id":"card-v2-2fb5767ad0","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/analytics/ai-analytics/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"En savoir plus"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Analyse de données par l’IA"],":type":"snowflake-site/components/title-v2"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EL’application de l’apprentissage automatique (ML), de l’IA générative et des agents IA à l’ensemble du cycle analytique, pour simplifier la préparation des données, permettre l’interrogation en langage naturel et accélérer la prise de décision.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"vertical"}},":itemsOrder":["topic_card_2"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false,"appliedCssClassNames":"snowflake-flexible-column-container-white-bg"}},":itemsOrder":["flexible_column_cont","flexible_column_cont_939100716","flexible_column_cont_1398138236","flexible_column_cont_663228916","flexible_column_cont_1786318617","flexible_column_cont_1467213961","flexible_column_cont_912630531","flexible_column_cont_1158003461","flexible_column_cont_1377146023"],":type":"wcm/foundation/components/responsivegrid"},"modal_container":{"layout":"SIMPLE","id":"container-f7fc73bd5c",":items":{},":itemsOrder":[],":type":"snowflake-site/components/modal/modal-container"},"markup_editor":{"id":"markup-editor-9c3d2c96b6","title":" ","cssContent":".snowflake-flexible-column-container-gray-10-bg \u003E .snowflake-flexible-column-container{background-color:var(--ui-background-05) !important}.text-color-text-05 .snowflake-text h2,.text-color-text-05.cq-Editable-dom h2,.text-color-text-05 .snowflake-text h3,.text-color-text-05.cq-Editable-dom h3,.text-color-text-05 .snowflake-text h4,.text-color-text-05.cq-Editable-dom h4,.text-color-text-05 .snowflake-text h5,.text-color-text-05.cq-Editable-dom h5,.text-color-text-05 .snowflake-text h6,.text-color-text-05.cq-Editable-dom h6{color:#000 !important}.logo-container{max-width:180px}div.snowflake-breadcrumb a.snowflake-breadcrumb-item,.snowflake-breadcrumb div.snowflake-breadcrumb-item{text-transform:none;font-weight:500}.snowflake-breadcrumb svg{display:none !important}.snowflake-breadcrumb a:has(svg)::after{content:\"/\";margin:0 12px;color:#666}.text-size-regular:has(.seo-hub-hero__related-topic-label){display:flex;align-items:center}.hub-hero__headline span{text-transform:none !important}.hub-hero__subheadline p{max-width:70ch;margin-top:8px}.hub-hero__authors \u003E .container \u003E .cmp-container \u003E .aem-container{display:flex;flex-direction:row}.hub-hero__authors \u003E .container \u003E .cmp-container \u003E .aem-container \u003E div{width:auto !important;margin:24px 48px 0 0 !important}.hub-hero__authors .heading-5-v2{gap:var(--spacing-00)}.hub-hero__authors .snowflake-content-chip-button{display:none !important}.hub-hero__authors .snowflake-person-chip-content .body-2,.hub-hero__authors .snowflake-content-chip-content .snowflake-title-v2-line{font-size:16px !important;line-height:20px !important;font-family:\"Lato\",sans-serif !important;color:#000 !important;font-weight:600 !important}.hub-hero__authors .snowflake-person-chip-content .body-3,.hub-hero__authors .snowflake-content-chip-content .snowflake-title-v2-line:not(:first-child){font-weight:400 !important;color:var(--text-05) !important;font-size:16px !important}.hub-hero__authors .snowflake-image-container img{aspect-ratio:1 !important;border-radius:100%;overflow:hidden}.hub-hero__authors .snowflake-person-chip-avatar{width:56px;height:56px}.hub-hero__authors .snowflake-content-chip{align-items:center;display:inline-flex}.hub-hero__authors .snowflake-content-chip-image{max-width:56px;line-height:0;margin-right:var(--spacing-03)}.hub-hero__authors .snowflake-person-chip-inner-horizontal{gap:var(--spacing-03)}@media screen and (min-width:1367px){.hub-hero__headline .heading-1-v2{font-size:48px;line-height:44px}}#hub-explore-resources-grid-inner \u003E .container \u003E .cmp-container \u003E .aem-container{display:flex;flex-direction:row;flex-wrap:wrap;gap:24px}#hub-explore-resources-grid-inner \u003E .container \u003E .cmp-container \u003E .aem-container::before,#hub-explore-resources-grid-inner \u003E .container \u003E .cmp-container \u003E .aem-container::after{display:none}#hub-explore-resources-grid-inner \u003E .container \u003E .cmp-container \u003E .aem-container \u003E div{width:calc(25% - 18px)}.sf-copy-markdown{margin-top:40px !important}.sf-copy-markdown [data-copy-md]{display:inline-flex;align-items:center;gap:6px;padding:8px 16px;font-family:\"Texta\",sans-serif;text-transform:uppercase;font-weight:800 !important;font-size:14px;color:#11567f;background:#f0faff;border:1px solid #b8e6f9;border-radius:24px;cursor:pointer;transition:background .2s ease,border-color .2s ease,color .2s ease}.sf-copy-markdown [data-copy-md]:hover{background:#ddf3fc;border-color:#29b5e8}.sf-copy-markdown [data-copy-md][data-copied=\"1\"]{color:#0f7b3e;background:#ecfdf5;border-color:#6ee7a0;pointer-events:none}.sf-copy-markdown [data-copy-md] svg{flex-shrink:0}.seo-hub__top-bar \u003E .container \u003E .cmp-container \u003E .aem-container{display:flex;justify-content:space-between}.seo-hub__top-bar \u003E .container \u003E .cmp-container \u003E .aem-container \u003E div:first-child{flex-grow:1}.hub-sidebar{padding:0 40px}.sticky-sidebar{max-width:340px;margin-left:auto}.page-toc ul{list-style-type:none;padding:0;margin-top:16px !important}.page-toc li{padding:8px 16px;border-left:4px solid var(--ui-01);cursor:pointer;transition:300ms ease all}.page-toc li:hover{color:var(--ui-01);border-color:#7fd3f1}.page-toc ul li:first-child{padding-top:0 !important}.page-toc ul li:last-child{padding-bottom:0 !important}.sticky-sidebar .page-toc li.is-active{font-weight:600;color:var(--snow-blue,#29b5e8)}.sticky-sidebar .page-toc li[data-anchor]{cursor:pointer}.seo-hub-hero__subheadline p{max-width:80ch}#hero:has(.snowflake-youtube-lite) .seo-hub-hero__subheadline p{max-width:50ch}@media screen and (min-width:1367px){.seo-hub-hero__headline span.snowflake-title-v2-line{font-size:56px !important}}.callout,.customer-card{background-color:#eef9fd;border-left:4px solid var(--ui-01);padding:24px 24px 24px 32px;border-radius:4px}.callout.snowflake-text p:not(:first-child){margin-top:var(--spacing-01)}.callout \u003E span \u003E p:first-child \u003E strong,.callout \u003E span \u003E p:first-child \u003E b{text-transform:uppercase;font-family:\"Texta\",sans-serif;font-size:16px !important;color:var(--ui-01) !important}.seo-customer.snowflake-card-v2-advanced-horizontal .snowflake-card-v2-advanced-image-container{max-width:200px}.seo-customer.snowflake-card-v2-advanced-horizontal .snowflake-card-v2-advanced-image-container img{object-fit:contain}.tag-group ul{list-style-type:none;padding:0;margin:0;display:flex;flex-direction:row;row-gap:12px;column-gap:8px;align-items:center;flex-wrap:wrap}.tag-group ul li:first-child{width:100%;flex-shrink:0}.tag-group ul li a{display:inline-block;padding:2px 12px;border-radius:48px;background-color:#ededed;color:#666;font-size:14px !important}.related-topics ul{list-style-type:none;padding:0;margin:0;display:flex;gap:8px;flex-wrap:wrap}.related-topics li{display:inline-block;border:1px solid #ccc;padding:4px 12px;border-radius:24px}.related-topics-outer-container \u003E .container \u003E .cmp-container \u003E .aem-container{display:flex;flex-direction:row}.related-topics-outer-container \u003E .container \u003E .cmp-container \u003E .aem-container \u003E div{width:auto !important;margin:0 !important}.related-topics-outer-container \u003E .container \u003E .cmp-container \u003E .aem-container \u003E div:first-child{margin-right:16px !important;flex-shrink:0}.longform-content li{margin-top:1rem !important}div.longform-content p{max-width:80ch}.bolder .snowflake-title-v2-line{font-weight:900 !important}.border-top \u003E div{border-top:1px solid #ccc;padding-top:48px}.longform-content ol{margin-top:0 !important}.longform-content ol li{margin-bottom:1rem !important}.longform-content ul{list-style-type:none}.longform-content ul li{margin:0;padding:0 0 0 32px;position:relative}.longform-content ul 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}div.longform-content .snowflake-text h2,div.longform-content .snowflake-text .heading-2-v2,div.longform-content .snowflake-title-v2 .heading-2-v2,div.longform-content .snowflake-text h3,div.longform-content .snowflake-text .heading-3-v2,div.longform-content .snowflake-title-v2 .heading-3-v2,div.longform-content .snowflake-text h4,div.longform-content .snowflake-text .heading-4-v2,div.longform-content .snowflake-title-v2 .heading-4-v2,div.longform-content .snowflake-text h5,div.longform-content .snowflake-text .heading-5-v2,div.longform-content .snowflake-title-v2 .heading-5-v2,div.longform-content .snowflake-text h6,div.longform-content .snowflake-title-v2 .heading-6-v2,div.longform-content .snowflake-text .heading-6-v2{text-transform:none !important}div.longform-content .snowflake-text h2,div.longform-content .snowflake-text .heading-2-v2,div.longform-content .snowflake-text h3,div.longform-content .snowflake-text .heading-3-v2,div.longform-content .snowflake-text h4,div.longform-content .snowflake-text .heading-4-v2,div.longform-content .snowflake-text h5,div.longform-content .snowflake-text .heading-5-v2,div.longform-content .snowflake-text h6,div.longform-content .snowflake-text .heading-6-v2{margin-top:1.5rem !important;line-height:1.1 !important}div.longform-content .snowflake-text h3,div.longform-content .snowflake-text .heading-3-v2,div.longform-content .snowflake-title-v2 .heading-3-v2,div.longform-content .snowflake-text h4,div.longform-content .snowflake-text .heading-4-v2,div.longform-content .snowflake-title-v2 .heading-4-v2,div.longform-content .snowflake-text h5,div.longform-content .snowflake-text .heading-5-v2,div.longform-content .snowflake-title-v2 .heading-5-v2,div.longform-content .snowflake-text h6,div.longform-content .snowflake-text .heading-6-v2,div.longform-content .snowflake-title-v2 .heading-6-v2{font-family:Lato,sans-serif !important;font-weight:800 !important}div.longform-content .snowflake-text h2,div.longform-content .snowflake-text .heading-2-v2,div.longform-content .snowflake-title-v2 .heading-2-v2{font-size:28px !important}div.longform-content .snowflake-text h3,div.longform-content .snowflake-text .heading-3-v2,div.longform-content .snowflake-title-v2 .heading-3-v2{font-size:22px !important}div.longform-content .snowflake-text h4,div.longform-content .snowflake-text .heading-4-v2,div.longform-content .snowflake-title-v2 .heading-4-v2{font-size:18px !important}div.longform-content .snowflake-text h5,div.longform-content .snowflake-text .heading-5-v2,div.longform-content .snowflake-title-v2 .heading-5-v2{font-size:16px !important}div.longform-content .snowflake-text h6,div.longform-content .snowflake-text .heading-6-v2,div.longform-content .snowflake-title-v2 .heading-6-v2{font-size:14px !important}@media screen and (min-width:992px){div.longform-content .snowflake-text h2,div.longform-content .snowflake-text .heading-2-v2,div.longform-content .snowflake-title-v2 .heading-2-v2{font-size:38px !important}div.longform-content .snowflake-text h3,div.longform-content .snowflake-text .heading-3-v2,div.longform-content .snowflake-title-v2 .heading-3-v2{font-size:26px !important}div.longform-content .snowflake-text h4,div.longform-content .snowflake-text .heading-4-v2,div.longform-content .snowflake-title-v2 .heading-4-v2{font-size:22px !important}div.longform-content .snowflake-text h5,div.longform-content .snowflake-text .heading-5-v2,div.longform-content .snowflake-title-v2 .heading-5-v2{font-size:18px !important}div.longform-content .snowflake-text h6,div.longform-content .snowflake-text .heading-6-v2,div.longform-content .snowflake-title-v2 .heading-6-v2{font-size:16px !important}}.longform-content .black-blue-text-color .snowflake-title-v2-line:not(:first-child){font-size:14px !important;font-weight:400 !important;color:rgba(0,0,0,.6) !important;margin-top:8px !important}.longform-content table{margin-top:24px;margin-bottom:24px;width:100%;background-color:var(--ui-background-01);border-collapse:collapse;border:2px solid var(--ui-background-09);font-family:\"Lato\",sans-serif;color:var(--ui-background-09)}.longform-content table thead{background-color:var(--ui-01)}.longform-content th,.longform-content td{min-width:120px;border:2px solid var(--ui-background-09);padding:var(--spacing-01)}.snowflake-responsive-container-inner-padding-medium:has(\u003E .longform-content){container:dg-text / inline-size}.longform-content .snowflake-text:has(table){--dg-line:rgba(204,204,204,.5);--dg-head:#ecf1f5;--dg-hover:#f6f9fa;--dg-cell:#2d2c2c;--dg-ring:#29b5e8;--dg-radius:12px;--dg-ease:cubic-bezier(.22,1,.36,1)}.longform-content .snowflake-text table:has(th){width:100%;margin:32px 0;border:1px solid var(--dg-line);border-collapse:separate;border-spacing:0;border-radius:var(--dg-radius);overflow:hidden;background:#fff;font-family:Lato,sans-serif;color:var(--dg-cell)}.longform-content .snowflake-text table:has(th) thead{background:var(--dg-head)}.longform-content .snowflake-text table:has(th) th,.longform-content .snowflake-text table:has(th) td{min-width:0;padding:16px;border:0;border-bottom:1px solid var(--dg-line);text-align:left;vertical-align:top;font-size:16px;line-height:24px;word-break:normal;overflow-wrap:break-word}.longform-content .snowflake-text table:has(th) th{background:var(--dg-head);font-weight:600;font-size:16px;line-height:20px;text-transform:none;letter-spacing:normal;color:#000;vertical-align:bottom}.longform-content .snowflake-text table:has(th) td:first-child{width:28%;font-weight:700;color:#000}.longform-content .snowflake-text table:has(th) tbody tr:last-child td{border-bottom:0}.longform-content .snowflake-text table:has(th):is(th,td) p{margin:0 !important;font-size:inherit !important;line-height:inherit !important}@media (hover:hover){.longform-content .snowflake-text table:has(th) tbody td{transition:background-color 200ms var(--dg-ease)}.longform-content .snowflake-text table:has(th) tbody tr:hover td{background:var(--dg-hover)}}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)){display:block;margin:32px 0}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) tbody{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:16px}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) tr{display:contents}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) td{display:flex;flex-direction:column;gap:8px}.longform-content .snowflake-text table:not(:has(th)) td{position:relative;width:auto !important;min-width:0;padding:24px !important;border:1px solid var(--dg-line) !important;border-radius:var(--dg-radius);background:#fff;transition:box-shadow 200ms var(--dg-ease)}.longform-content .snowflake-text table:not(:has(th)) td h3{margin:0 !important;line-height:20px !important}.longform-content .snowflake-text table:not(:has(th)) td h3 a{outline:2px solid transparent}.longform-content .snowflake-text table:not(:has(th)) td p{margin:0 !important;font-size:14px !important;line-height:20px !important;color:#666}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) td h3 a::after{content:\"\";position:absolute;inset:0;border-radius:inherit}.longform-content .snowflake-text table:not(:has(th)) td:has(a:focus-visible){outline:2px solid var(--dg-ring);outline-offset:2px}@media (hover:hover){.longform-content .snowflake-text table:not(:has(th)) td:hover{box-shadow:0 10px 20px rgba(152,162,179,.1)}}@container dg-text (min-width:640px){html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) tbody{grid-template-columns:repeat(4,minmax(0,1fr))}}@container dg-text (max-width:559px){.longform-content .snowflake-text table:has(th) th,.longform-content .snowflake-text table:has(th) td{padding:12px;font-size:14px;line-height:20px}.longform-content .snowflake-text table:has(th) th{font-size:14px;line-height:18px}.longform-content .snowflake-text table:has(th) td:first-child{width:auto}.longform-content .snowflake-text table:not(:has(th)) td{padding:16px !important}html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) tbody{gap:12px}}@container dg-text (max-width:359px){.longform-content .snowflake-text table:has(th) th,.longform-content .snowflake-text table:has(th) td{padding:12px 8px}}@container dg-text (max-width:299px){html:not(.aem-AuthorLayer-Edit) .longform-content .snowflake-text table:not(:has(th)) tbody{grid-template-columns:minmax(0,1fr)}}@media (prefers-reduced-motion:reduce){.longform-content .snowflake-text table td{transition:none}}@media print{.longform-content .snowflake-text table:has(th) tr{break-inside:avoid}}","jsContent":"(function(){var OFFSET=100;if(window.gsap&&window.ScrollTrigger){gsap.registerPlugin(ScrollTrigger);var sidebar=document.querySelector('.sticky-sidebar');var body=document.querySelector('.longform-content');if(sidebar&&body){ScrollTrigger.create({trigger:sidebar,start:'top 100px',endTrigger:body,end:'bottom bottom',pin:sidebar,pinSpacing:false\n});}\n}\ndocument.addEventListener('click',function(e){var li=e.target.closest('li[data-anchor]');if(!li)return;var slug=li.getAttribute('data-anchor');var heading=document.querySelector('.anchor-title--'+CSS.escape(slug));if(!heading)return;e.preventDefault();var top=heading.getBoundingClientRect().top+window.pageYOffset-OFFSET;window.scrollTo({top:top,behavior:'smooth'});history.replaceState(null,'','#'+slug);},false);var headings=document.querySelectorAll('[class*=\"anchor-title--\"]');if(headings.length&&'IntersectionObserver'in window){var io=new IntersectionObserver(function(entries){entries.forEach(function(entry){if(!entry.isIntersecting)return;var cls=Array.from(entry.target.classList)\n.find(function(c){return c.indexOf('anchor-title--')===0;});if(!cls)return;var slug=cls.replace('anchor-title--','');document.querySelectorAll('li[data-anchor]').forEach(function(li){li.classList.toggle('is-active',li.getAttribute('data-anchor')===slug);});});},{rootMargin:'-20% 0px -70% 0px',threshold:0});headings.forEach(function(h){io.observe(h);});}\n})();",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":true},"experiencefragment-footer":{"id":"experiencefragment-984d4c777f","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/footer/master/jcr:content","configured":true,":items":{"root":{"additionalClasses":"sf-footer","layout":"SIMPLE","id":"container-6c1cfc89e0",":items":{"container_copy_1510208642":{"additionalClasses":"sf-footer__inner","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-0ddacb7879",":items":{"flexible_column_cont":{"id":"flexible-column-container-d45a937486","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-b6407e5858",":items":{"container":{"additionalClasses":"sf-footer-grid__inner","layout":"RESPONSIVE_GRID","columnCount":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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-49f0a00fd5",":items":{"container_1622723482":{"additionalClasses":"sf-footer__column","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-435a94f25f",":items":{"container":{"additionalClasses":"sf-footer__newsletter-group","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12","marketo_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-599e9033f1",":items":{"text":{"id":"text-73b262af3b","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-v2-442ef9c2fa","marketoForm":{"hidden":null,"formId":"45871","edit":false,"successUrl":null,"script":null,"values":null},"partnerCampaignPage":false,"serverInstance":"252-RFO-227.mktoweb.com","munchkinId":"252-RFO-227","marketoConfigured":true,"formConfigured":true,":type":"snowflake-site/components/form/marketo-v2"}},":itemsOrder":["text","marketo_v2"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-59c9fce043",":items":{"text":{"id":"text-2fc839ee65","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-eb7582eb6a","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/container"},"container_copy_copy":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-04b395c80b",":items":{"text":{"id":"text-a91a4c6569","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_copy":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-b5b745bcc2",":items":{"text":{"id":"text-2f92734b6b","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_copy_copy_":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-42ca8d1855",":items":{"text":{"id":"text-3a0766286b","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container_1622723482","container","container_copy_copy","container_copy","container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"},"container_573483281_":{"additionalClasses":"sf-footer__bottom","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container_112062425":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-cce1b20f7e",":items":{"container_112062425":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-7f7a3bf6ec",":items":{"flexible_column_cont":{"id":"flexible-column-container-c7d3004b99","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-dea73f90a1",":items":{"container":{"additionalClasses":"sf-footer__legal-container","layout":"RESPONSIVE_GRID","columnCount":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"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-feb0c0e303",":items":{"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-e459f7f3c6",":items":{"image":{"id":"image-951b6cf36f","additionalClasses":"sf-footer__logo","height":"64","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","imageLink":{"valid":true,"url":"/en/"},"alt":"Snowflake logo","lazyEnabled":true,"isLcpImage":false,"width":"64",":type":"snowflake-site/components/image"}},":itemsOrder":["image"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":type":"snowflake-site/components/container"},"text_copy_copy_16360_26716402":{"id":"text-e2a6ec1494","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-60f2173a56","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"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container_112062425"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-none",":type":"snowflake-site/components/container"},"markup_editor_copy":{"id":"markup-editor-6166f0f82d","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"],"appliedCssClassNames":"ui-background-02",":type":"snowflake-site/components/container"}},":itemsOrder":["root"],":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf"},"experiencefragment":{"id":"experiencefragment-1b3e2437f5","localizedFragmentVariationPath":"/content/experience-fragments/snowflake-site/language-masters/fr/site/footer-legal-disclaimers/master/jcr:content","configured":true,":items":{"root":{"additionalClasses":"sf-footer__disclaimers","layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"container":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-dc2fa2c633",":items":{"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","id":"container-0a530455a2",":items":{"flexible_column_cont":{"id":"flexible-column-container-bf65ad4b97","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":{"additionalClasses":"sf-footer__disclaimers-inner","layout":"SIMPLE","id":"container-1a3a561e85",":items":{"simple_stat_referenc_1538552089":{"configurationStatus":{"configured":true,"message":""},":type":"snowflake-site/components/simple-stat","statDisclaimer":{"id":"cf-disclaimer","text":"\u003Cp\u003E* Private preview, † Public preview, ‡ Bientôt disponible\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text"},"statNumber":null,"statDescription":null}},":itemsOrder":["simple_stat_referenc_1538552089"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"],"appliedCssClassNames":"snowflake-container snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/container"}},":itemsOrder":["container"],":type":"snowflake-site/components/container"}},":itemsOrder":["root"],":type":"snowflake-site/components/experiencefragment","classNames":"aem-xf"}},":itemsOrder":["experiencefragment-banner","experiencefragment-header","responsivegrid","modal_container","markup_editor","experiencefragment-footer","experiencefragment"],":type":"wcm/foundation/components/responsivegrid"}},":itemsOrder":["root"],":type":"snowflake-site/components/structure/page","coveoConfig":{"organizationId":"snowflakecomputingproduction8neljofn","apiKey":"xx335921a6-2a0a-40f2-a167-e390b4766c3d","pipeline":"snowflake.com","searchHub":"snowflake.com"},"analyticsDebugMode":false,"analyticsData":{"excludeFromAnalytics":false,"subCategory":"ai-analytics","pageType":"content","templateName":"base-page-template54","siteName":"snowflake","pageUrl":"/fr/analytics/ai-analytics/predictive-analytics/","language":"fr","category":"analytics","pageName":"Analyse prédictive : comment les modèles de ML généralisent des données passées à de nouveaux cas","contentTags":["snowflake-site:taxonomy/content-type/fundamentals"]},"publicationThemeClass":"",":path":"/content/snowflake-site/global/fr/analytics/ai-analytics/predictive-analytics",":hierarchyType":"page","isPasswordProtected":false,"analyticsContentTags":["snowflake-site:taxonomy/content-type/fundamentals"],"analyticsEnabled":true,"locale":"fr"}