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aem-GridColumn--default--12"},":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"],":items":{"flexible_column_cont":{"id":"flexible-column-container-7a7b17927e","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-e6728c67fe",":itemsOrder":["breadcrumb"],":items":{"breadcrumb":{"id":"breadcrumb-21d2d7af51","items":[{"id":"breadcrumb-21d2d7af51-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-21d2d7af51-item-c4004d2d70","link":{"valid":true,"url":"/fr/analytics/real-time-analytics/"},"active":false,"current":false,"title":"Analytique en temps réel","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"},{"id":"breadcrumb-21d2d7af51-item-798fb87da1","link":{"valid":true,"url":"/fr/analytics/real-time-analytics/streaming-analytics/"},"active":true,"current":true,"title":"Analytique en streaming","appliedCssClassNames":"summit-page",":type":"snowflake-site/components/structure/page"}],":type":"snowflake-site/components/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-8096c64acb","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-5e6f872db5",":itemsOrder":["title_v2","text","container"],":items":{"title_v2":{"id":"title-v2-23cc9cba8b","additionalClasses":"hub-hero__headline","type":"heading1","lines":["Analytique en streaming : analyser les données en mouvement, et savoir quand c'est utile"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-137e3b854f","additionalClasses":"hub-hero__subheadline","text":"\u003Cp\u003EToutes les charges de travail en temps réel n’ont pas besoin de l’analytique en streaming. Cet article explique ce que recouvre concrètement l'analytique en streaming (ingestion en streaming, traitement de flux et mise à disposition des données à faible latence) et comment déterminer si vos usages l'exigent.\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,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"content_chip_jennifer_wu":"aem-GridColumn aem-GridColumn--default--12","content_chip":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-e3109472b8",":itemsOrder":["content_chip","content_chip_jennifer_wu"],":items":{"content_chip":{"id":"content-chip-12f625b32a","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"800","src":"/adobe/dynamicmedia/deliver/dm-aid--a7e9fdff-6f08-4edc-9cd1-e213bb234aaa/laurie-macpherson.jpg?preferwebp=true&quality=85","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_jennifer_wu":{"id":"content-chip-4e48ab6544","cta":{"id":"cta","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire la biographie"},"image":{"id":"image","height":"230","src":"/adobe/dynamicmedia/deliver/dm-aid--f1d1fbd0-d5e7-4d32-8885-a4d7d1652678/jennifer-wu.png?preferwebp=true&quality=85","alt":"Jennifer Wu","lazyEnabled":true,"isLcpImage":false,"width":"212",":type":"snowflake-site/components/image"},"headline":{"id":"title","type":"heading5","lines":["Jennifer Wu","Analytics Expert, Snowflake"],":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/content-chip"}},"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":type":"snowflake-site/components/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-b12a2e912d",":itemsOrder":["youtube"],":items":{"youtube":{"id":"embed-f8c342c46c","youtubeVideoId":"VKnz7vMEBoY","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"}},":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-9d8ca9a6bb","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-62fdb0adda",":itemsOrder":["text_894059747","text"],":items":{"text_894059747":{"id":"text-47011bcf31","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EThèmes liés à l’analyse en temps réel :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-0c018898e4","additionalClasses":"related-topics ","text":"\u003Cul\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\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/ai-analytics/\"\u003EAnalytique par 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\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-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-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-571b043d9e","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",":itemsOrder":["callout__0","text__0","title_what-is-streaming-analytics","text_what-is-streaming-analytics_0","yt_what-is-streaming-analytics_0","title_streaming-analytics-vs-real-time-analytics","text_streaming-analytics-vs-real-time-analytics_0","card_v2_streaming-analytics-vs-real-time-analytics_0","title_why-stream-processing-results-require-context","text_why-stream-processing-results-require-context_0","callout_why-stream-processing-results-require-context_0","title_when-streaming-analytics-is-worth-the-cost","text_when-streaming-analytics-is-worth-the-cost_0","callout_when-streaming-analytics-is-worth-the-cost_0","title_running-streaming-analytics-on-snowflake","text_running-streaming-analytics-on-snowflake_0","title_treat-streaming-as-a-design-choice-not-a-default","text_treat-streaming-as-a-design-choice-not-a-default_0","callout_treat-streaming-as-a-design-choice-not-a-default_0"],":items":{"callout__0":{"id":"text-7a8cebfad3","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EDÉFINITION DE L’ANALYTIQUE EN STREAMING\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EL'analytique en streaming est une composante de l'analytique en temps réel, dans laquelle les données sont ingérées en continu et, si nécessaire, traitées au fil de l'eau avant d'atteindre la couche de restitution. Elle associe ingestion en streaming, traitement de flux et mise à disposition à faible latence, afin que les résultats se mettent à jour à mesure que de nouveaux événements arrivent. Ses exigences en matière de fraîcheur des données et de traitement sont plus strictes que celles des autres approches d'analytique en temps réel.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text__0":{"id":"text-0d3d5ff8e2","text":"\u003Cp\u003EUne demande d'« analyse en temps réel » commence souvent par un objectif de latence : cinq secondes, 30 secondes, voire une minute. Pourtant, savoir en combien de temps le résultat doit être disponible ne permet pas de déterminer si vous avez besoin d'analytique en streaming.\u003C/p\u003E\n\u003Cp\u003EUn tableau de bord qui semble à jour aux yeux de l'utilisateur peut s'appuyer sur un traitement de flux qui transforme les événements en continu, au fil de l'eau, ou sur des requêtes rapides exécutées sur des données récemment chargées. Si la seconde approche répond au besoin, rien ne justifie d'assumer la complexité de l'analytique en streaming, ni les exigences qu'elle impose à l'architecture.\u003C/p\u003E\n\u003Cp\u003EPour prendre la bonne décision, commencez plutôt par répondre à ces trois questions : Au bout de combien de temps une réponse perd-elle sa valeur ? Les données doivent-elles être transformées ou enrichies pour être exploitables ?  Et si oui, ce traitement doit-il s'effectuer en continu sur les événements en transit, ou peut-il intervenir une fois les données chargées ?\u003C/p\u003E\n\u003Cp\u003ELa première réponse définit votre objectif de fraîcheur. Les réponses aux deux questions suivantes indiquent quelles briques de l'analytique en streaming vous sont réellement nécessaires : de la seule ingestion en streaming jusqu'à l'association de l'ingestion et du traitement de flux.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_what-is-streaming-analytics":{"id":"title-v2-d637d47ff3","additionalClasses":"anchor-title anchor-title--what-is-streaming-analytics","type":"heading2","lines":["Qu’est-ce que l’analytique en streaming ?"],":type":"snowflake-site/components/title-v2"},"text_what-is-streaming-analytics_0":{"id":"text-f0cff42ef2","text":"\u003Cp\u003EL'analytique en streaming est une discipline de bout en bout qui consiste à ingérer des données en continu, et éventuellement à les traiter au fil de l'eau, afin qu'elles atteignent la couche de restitution le plus rapidement possible pour éclairer les décisions et déclencher des actions. Certaines charges de travail d'analytique en streaming exigent que le système conserve un état et mette à jour les résultats au fur et à mesure de l'intégration de nouveaux événements : c'est le traitement de flux. D’autres nécessitent seulement une ingestion rapide et fiable dans une couche de restitution capable d’interroger immédiatement les données.\u003C/p\u003E\n\u003Cp\u003EL’analytique en streaming s’inscrit dans une architecture événementielle plus large. Il est donc important de préciser le rôle de chaque composant.\u003C/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EL’ingestion en streaming\u003C/strong\u003E désigne la manière dont les événements entrent dans l'environnement analytique. Des plateformes comme \u003Ca href=\"https://kafka.apache.org/\" target=\"_blank\"\u003EKafka\u003C/a\u003E acheminent des événements horodatés entre producteurs et consommateurs, tandis que les API d'écriture directe et la capture des données modifiées (Change Data Capture, CDC) permettent d'extraire les données des systèmes opérationnels. L’objectif est d’acheminer les données rapidement et de manière fiable.\u003C/p\u003E\n\u003Cp\u003E\u003Cstrong\u003ELe traitement des flux\u003C/strong\u003E constitue la couche de calcul. Des moteurs comme \u003Ca href=\"https://flink.apache.org/\" target=\"_blank\"\u003EApache Flink\u003C/a\u003E ou \u003Ca href=\"https://spark.apache.org/streaming/\" target=\"_blank\"\u003ESpark Structured Streaming\u003C/a\u003E permettent d'effectuer, sur ces flux, des agrégations sur fenêtres temporelles, des jointures avec état, du filtrage et de l'enrichissement.\u003C/p\u003E\n\u003Cp\u003E\u003Cem\u003EDécouvrez Snowpipe Streaming, qui permet d'ingérer en continu de grands volumes de données pour obtenir des informations en quasi temps réel :\u003C/em\u003E\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_what-is-streaming-analytics_0":{"id":"embed-1982ca718b","youtubeVideoId":"6iq-ijH1BGo","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_streaming-analytics-vs-real-time-analytics":{"id":"title-v2-aaeec8f964","additionalClasses":"anchor-title anchor-title--streaming-analytics-vs-real-time-analytics","type":"heading2","lines":["Analytique en streaming ou analytique en temps réel"],":type":"snowflake-site/components/title-v2"},"text_streaming-analytics-vs-real-time-analytics_0":{"id":"text-e6ad56cbdc","text":"\u003Cp\u003EL’analytique en streaming et l’analytique en temps réel sont souvent évoquées ensemble, mais elles ne désignent pas la même chose.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EL’analytique en temps réel est le terme générique.\u003C/b\u003E Les données et les résultats doivent être suffisamment récents, et les requêtes suffisamment rapides, pour permettre une décision ou une action dans un délai défini. Cette fraîcheur peut provenir de l'ingestion en streaming, du chargement par micro-lots, de la réplication CDC ou d'une combinaison de ces approches. Les requêtes croisent généralement les données récentes avec l'historique.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/real-time-analytics/\"\u003EEn savoir plus sur l’analyse en temps réel \u003E\u003C/a\u003E\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EL’analytique en streaming est une catégorie de l’analyse en temps réel.\u003C/b\u003E L'analytique en streaming impose, elle, des exigences plus strictes quant à la manière dont les données sont traitées avant d'atteindre la couche de restitution. Elle introduit un modèle de traitement plus spécifique : les événements entrants sont traités dès leur arrivée, souvent en conservant un état d'un événement, d'une fenêtre ou d'une séquence à l'autre. Les exigences de fraîcheur et de transformation des données sont plus strictes, car les calculs portent sur des données en mouvement et non au repos.\u003C/p\u003E\n\u003Cp\u003EImaginons une requête interactive qui renvoie un résultat en moins d'une seconde, sur des données chargées il y a cinq secondes. Si le métier a besoin d'une réponse datant de dix secondes au plus, cette architecture peut tout à fait répondre à l'exigence de temps réel. Aucune requête en streaming permanente n’est nécessaire.\u003C/p\u003E\n\u003Cp\u003EInversons maintenant l’exemple. Un pipeline actualise continuellement un agrégat, mais le tableau de bord qui l'affiche n'est consulté qu'une fois par heure. Le calcul s’effectue en streaming, mais cette fraîcheur supplémentaire apporte très peu de valeur à l’organisation.\u003C/p\u003E\n\u003Cp\u003EC'est aussi pourquoi l'analytique en streaming fait partie de la catégorie plus large de l'analytique en temps réel, au lieu de constituer une catégorie distincte. Le calcul en streaming est l'un des moyens d'atteindre la fraîcheur des données qu'exige l'analytique en temps réel. Ce n'est toutefois pas le seul, et il entraîne une complexité supplémentaire en matière de gestion des états, d'ordonnancement des événements et de garanties de livraison.\u003C/p\u003E\n\u003Cp\u003ECette distinction détermine ce que vous devez créer. Une exigence de fraîcheur impose des contraintes sur la vitesse de disponibilité des données et le temps de réponse des requêtes. Une exigence de streaming signifie que le calcul doit lui-même suivre un flux continu d’événements, notamment les états, l’ordonnancement ou les règles temporelles dont dépend le résultat.\u003C/p\u003E\n\u003Cp\u003EPour certaines charges de travail, il suffit d’exécuter périodiquement le calcul sur les données récemment chargées. D’autres dépendent de séquences d’événements, de fenêtres glissantes ou d’un état cumulé qui doit être conservé à mesure que le flux progresse.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_streaming-analytics-vs-real-time-analytics_0":{"id":"card-v2-4aac8328aa","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"button":{"id":"button","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Lire le témoignage client"},":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["PSA BDP"],":type":"snowflake-site/components/title-v2"},"image":{"id":"image","height":"351","src":"/adobe/dynamicmedia/deliver/dm-aid--2ad0db1f-65fa-408d-b295-b0090ec8248a/psa-bdp.png?preferwebp=true&quality=85","alt":"PSA-BDP","lazyEnabled":true,"isLcpImage":false,"width":"642",":type":"snowflake-site/components/image"},"type":"content-card","text":{"id":"text","text":"\u003Cp\u003EActeur mondial de la logistique, PSA BDP utilise Snowflake pour centraliser d'importants volumes de données logistiques à haute vélocité, notamment la localisation des navires et les perturbations, afin de les analyser et de les partager dans toute l'entreprise. La migration de l'ingestion en temps réel et du partage de données vers Snowflake lui a permis de réduire d'environ 40 % les coûts associés, par rapport à ses anciennes solutions.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal"},"title_why-stream-processing-results-require-context":{"id":"title-v2-26a4bb84bf","additionalClasses":"anchor-title anchor-title--why-stream-processing-results-require-context","type":"heading2","lines":["Pourquoi les résultats du traitement des flux nécessitent du contexte"],":type":"snowflake-site/components/title-v2"},"text_why-stream-processing-results-require-context_0":{"id":"text-b39e0b48bc","text":"\u003Cp\u003EL'arrivée du traitement de flux dans l'architecture entraîne l'un des changements conceptuels les plus importants : il n'existe aucun moment naturel où les données entrantes peuvent simplement être considérées comme « terminées ».\u003C/p\u003E\r\n\u003Cp\u003ELorsque vous exécutez une requête sur la table des ventes de la semaine précédente, les limites sont évidentes. Vous savez quels enregistrements doivent être inclus dans le calcul. En revanche, avec un flux continu, c'est à vous de fixer ces limites : quels événements intègrent un calcul, combien de temps vous acceptez d'attendre les données tardives, et que faire lorsque des événements sont renvoyés ou livrés plusieurs fois.\u003C/p\u003E\r\n\u003Ch3\u003ELes fenêtres définissent ce que vous mesurez\u003C/h3\u003E\r\n\u003Cp\u003ELa plupart des calculs en streaming nécessitent de convertir un flux continu en unités analysables. C’est là qu’interviennent les fenêtres.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EUne fenêtre fixe\u003C/b\u003E découpe le flux en périodes de durée fixe, qui ne se chevauchent pas. Vous pouvez, par exemple, calculer le volume de transactions de 10 h 00 à 10 h 05, puis ouvrir une nouvelle fenêtre de 10 h 05 à 10 h 10.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUne fenêtre glissante,\u003C/b\u003E à l'inverse, se chevauche avec les précédentes.. Si vous souhaitez recalculer chaque minute « les transactions des dix dernières minutes », chaque nouvelle fenêtre contient une partie des événements de la précédente.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EUne fenêtre de session\u003C/b\u003E fonctionne différemment : elle regroupe les événements selon les périodes d'activité, et non selon des intervalles horaires fixes.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EAutrement dit, un indicateur issu d'un flux, comme « 1 240 transactions », n'a pas de sens à lui seul. Encore faut-il savoir à quel intervalle ou à quelle session ces transactions se rapportent.\u003C/p\u003E\r\n\u003Ch3\u003EL’heure de l’événement et l’heure de traitement coïncident rarement\u003C/h3\u003E\r\n\u003Cp\u003EL'heure de l'événement (event time) correspond au moment où l'événement s'est réellement produit. L'heure de traitement (processing time) correspond au moment où le système analytique l'a reçu ou traité. Ces horodatages diffèrent souvent.\u003C/p\u003E\r\n\u003Cp\u003EImaginons qu’une transaction ait eu lieu à 10:04:58, mais n’ait atteint le pipeline analytique qu’à 10:06. Il peut s'agir d'une connexion réseau momentanément bloquée, ou d'un système en amont qui a retransmis l'événement. Du point de vue de l'heure de l'événement, la transaction appartient à la fenêtre 10 h 00 – 10 h 05. Du point de vue de l'heure de traitement, en revanche, cette fenêtre est déjà close.\u003C/p\u003E\r\n\u003Cp\u003EC'est pourquoi les filigranes (watermarks) et les règles de gestion des données tardives sont indispensables. Un watermark représente l'estimation, par le système, de l'avancement de l'heure de l'événement. Il lui permet de déterminer à quel moment une fenêtre fondée sur l'heure de l'événement est prête à produire un résultat. Une politique de gestion des retards détermine ce qui se passe lorsque des événements arrivent après cette échéance. Attendre plus longtemps permet d'intégrer davantage d'événements retardataires avant que le résultat ne soit figé. À l'inverse, fermer la fenêtre plus tôt produit un résultat plus frais, mais augmente le risque que des données tardives nécessitent une mise à jour ou un traitement à part.\u003C/p\u003E\r\n\u003Cp\u003EWill Xu, Principal Product Manager for Snowflake Interactive Analytics, recommande d’adapter cette tolérance à la décision elle-même : « Les équipes doivent déterminer le délai maximal de retard des données qu'une charge de travail peut tolérer. En règle générale, cette fenêtre est de quatre heures. Plus elle est étendue, plus le coût d’infrastructure augmente. C’est une décision stratégique à prendre dès le début de la planification. »\u003C/p\u003E\r\n\u003Ch3\u003ELes garanties de livraison influencent aussi le résultat\u003C/h3\u003E\r\n\u003Cp\u003ELes événements tardifs ne sont qu'une source d'incertitude parmi d'autres. Les nouvelles tentatives en sont une autre. Par nature, les systèmes distribués perdent des connexions, redémarrent des processus et renvoient des données. Une question en apparence banale finit alors par avoir d’importantes conséquences analytiques : Ai-je déjà traité cet événement ?\u003C/p\u003E\r\n\u003Cp\u003EAvec une livraison « au moins une fois » (at-least-once), le système privilégie l'absence de perte d'événements. Un consommateur peut donc recevoir plusieurs fois le même événement. Les traitements en aval doivent donc tolérer les doublons, généralement grâce à des \u003Ca href=\"https://nhimg.org/glossary/idempotent-operation/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Eopérations idempotentes\u003C/a\u003E qui produisent le même résultat effectif lorsqu'elles sont répétées.\u003C/p\u003E\r\n\u003Cp\u003ELa livraison « exactement une fois » (exactly-once) vise à ce que chaque événement n'ait qu'un seul effet sur le résultat, même en cas de nouvelles tentatives d'envoi. Cela nécessite généralement de coordonner les systèmes qui produisent, traitent et stockent les événements.\u003C/p\u003E\r\n\u003Cp\u003EPour la personne qui consulte un résultat analytique, toute cette infrastructure se résume à un chiffre affiché à l’écran. Mais la sémantique ne doit pas se perdre dans le pipeline. Si un indicateur calculé en streaming doit orienter une décision, les équipes doivent connaître sa fenêtre de calcul, la manière dont les données tardives sont prises en compte et les garanties de livraison sur lesquelles il s'appuie.\u003C/p\u003E\r\n\u003Cp\u003ELes équipes doivent également définir une règle de rapprochement des données. Le chiffre obtenu en streaming à 10 h 05 ne sera pas forcément identique au résultat du traitement par lots effectué ensuite, lorsque tous les enregistrements en retard auront été pris en compte. Déterminez à l’avance quel chiffre fera foi et comment résoudre les écarts.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_why-stream-processing-results-require-context_0":{"id":"text-1b18bffe79","additionalClasses":"callout callout--tip","text":"\u003Cp\u003E\u003Cstrong\u003EASTUCE\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EDocumentez la fenêtre de calcul, la règle de gestion des données tardives et les garanties de livraison avec chaque indicateur. Un chiffre issu d'un flux inspire bien plus confiance lorsque ses utilisateurs savent quelles données il intègre et ce qui peut encore évoluer.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_when-streaming-analytics-is-worth-the-cost":{"id":"title-v2-32b01d65c4","additionalClasses":"anchor-title anchor-title--when-streaming-analytics-is-worth-the-cost","type":"heading2","lines":["Quand le coût de l’analytique en streaming se justifie"],":type":"snowflake-site/components/title-v2"},"text_when-streaming-analytics-is-worth-the-cost_0":{"id":"text-cf6acc45a2","text":"\u003Cp\u003ECertaines charges de travail doivent réellement prendre des décisions dans des délais extrêmement courts. L'autorisation des paiements avec détection de fraude, les enchères publicitaires en temps réel et certains systèmes de contrôle industriel peuvent devoir réagir en quelques millisecondes. Le traitement s'effectue alors généralement directement dans l'application ou dans le pipeline de traitement de flux, afin que la décision puisse être prise au cours même de la transaction.\u003C/p\u003E\r\n\u003Cp\u003EMais la plupart des cas d’usage d’\u003Ca href=\"https://www.snowflake.com/fr/analytics/\"\u003Eanalytics\u003C/a\u003E en entreprise ne sont pas soumis à ces contraintes. Pour nombre d’entre eux, l’analytique en temps quasi réel suffit, sans calcul continu des données en transit. Une équipe des opérations peut avoir besoin de détecter en quelques minutes l’accumulation d’un retard afin de pouvoir intervenir. Ou encore, un processus logistique peut avoir besoin qu'une anomalie d'expédition soit signalée tant qu'il est encore possible de réacheminer la marchandise. Un processus automatisé peut avoir besoin de données de télémétrie récentes avant de décider de l’action suivante. Dans ces situations, disposer de données actualisées en quelques secondes ou minutes offre une réelle valeur opérationnelle.\u003C/p\u003E\r\n\u003Ch3\u003EQuand un traitement périodique ou par micro-batch suffit\u003C/h3\u003E\r\n\u003Cp\u003ELe traitement par batch perdure pour de bonnes raisons. Chaque exécution porte sur un ensemble de données délimité, à intervalle défini. Vous disposez ainsi d'une unité relativement claire à tester, rejouer et examiner en cas de problème.\u003C/p\u003E\r\n\u003Cp\u003ELe traitement par micro-lots (micro-batching) raccourcit cet intervalle : de petits groupes d'enregistrements sont traités toutes les quelques secondes ou minutes. Selon la charge de travail, cette méthode peut souvent produire des données suffisamment récentes.\u003C/p\u003E\r\n\u003Cp\u003EPar exemple, si un tableau de bord logistique reste utile avec une actualisation toutes les cinq minutes, conserver un état continu entre chaque événement entrant n'améliorera sans doute pas la décision. L'analytique en quasi temps réel est alors probablement plus adaptée. En revanche, si l'équipe doit détecter une séquence au moment même où elle se produit, ou si attendre cinq minutes ferait passer le moment d'intervenir, le traitement de flux se justifie davantage.\u003C/p\u003E\r\n\u003Cp\u003EDe nombreuses équipes combinent traitement par lots et streaming, plutôt que d'imposer le même modèle de traitement à chaque étape du pipeline.\u003C/p\u003E\r\n\u003Ch3\u003ERéduire la latence augmente généralement les coûts\u003C/h3\u003E\r\n\u003Cp\u003ECette approche permet également d’établir un modèle de coûts plus pertinent. Réduire la latence suppose généralement des traitements plus fréquents et des marges opérationnelles plus serrées. Les coûts de calcul peuvent donc augmenter, tout comme l'effort de supervision, de dépannage et de rapprochement qu'exige la charge de travail. Mettez ce coût permanent en regard des décisions dont les résultats s'améliorent réellement lorsque les données leur parviennent plus tôt.\u003C/p\u003E\r\n\u003Cp\u003ECes exemples illustrent la diversité des exigences :\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EAutorisation des transactions en cas de fraude :\u003C/b\u003E souvent quelques millisecondes, avec une logique de décision intégrée au chemin transactionnel ou au pipeline de traitement de flux.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDétection des anomalies opérationnelles :\u003C/b\u003E de quelques secondes à quelques minutes, selon la rapidité de réaction attendue d'une personne ou d'un processus automatisé.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESuivi des stocks et de l’exécution des commandes :\u003C/b\u003E généralement de l'ordre de quelques minutes : un traitement incrémental ou à intervalles courts peut suffire.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EReporting de direction :\u003C/b\u003E souvent toutes les heures ou tous les jours, sauf si un autre workflow en aval consomme les données plus tôt.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_when-streaming-analytics-is-worth-the-cost_0":{"id":"text-84ac9b1ea4","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EPIÈGE COURANT\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EUne erreur fréquente consiste à croire que toute charge de travail d'analytique en streaming nécessite un traitement de flux. De nombreuses charges de travail n'ont besoin que d'une ingestion en streaming et d'une couche de requêtes rapide, sans fenêtres, sans gestion des états ni watermarks.  Ajoutez un traitement de flux lorsque les données doivent être transformées, enrichies ou dédoublonnées en transit. Dispensez-vous-en lorsque les données brutes ingérées sont déjà prêtes à être interrogées.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_running-streaming-analytics-on-snowflake":{"id":"title-v2-a0173e6b9a","additionalClasses":"anchor-title anchor-title--running-streaming-analytics-on-snowflake","type":"heading2","lines":["Exécuter l’analytique en streaming sur Snowflake"],":type":"snowflake-site/components/title-v2"},"text_running-streaming-analytics-on-snowflake_0":{"id":"text-2ab5255df9","text":"\u003Cp\u003ESnowflake prend en charge l'analytique en streaming de bout en bout (ingestion, traitement de flux et restitution à faible latence) au sein d'une plateforme unique et gouvernée. Vos équipes n'ont donc pas à gérer une infrastructure de streaming distincte.\u003C/p\u003E\n\u003Cp\u003ELe parcours de l’analytique en streaming comprend trois étapes, chacune faisant l’objet d’une présentation détaillée :\u003C/p\u003E\n\u003Cp\u003EL’\u003Cb\u003Eingestion en streaming\u003C/b\u003E transmet les données d’événements à Snowflake dès leur production, sans passer par des fichiers intermédiaires ni par un stockage objet temporaire. Cela comprend l'ingestion directe par API via \u003Ca href=\"https://www.snowflake.com/en/product/features/snowpipe-streaming/\"\u003ESnowpipe Streaming\u003C/a\u003E, \u003Ca href=\"https://docs.snowflake.com/en/user-guide/kafka-connector\"\u003ESnowflake Kafka Connector\u003C/a\u003E et la capture des données modifiées (CDC) depuis les bases de données opérationnelles.\u003C/p\u003E\n\u003Cp\u003ELe \u003Cb\u003Etraitement de flux\u003C/b\u003E transforme, enrichit et structure les données en transit avant qu’elles n’atteignent la couche de mise à disposition. Cela comprend le dédoublonnage, les jointures avec des tables de dimensions, les agrégations sur fenêtres temporelles et le traitement des flux CDC, des opérations qui nécessitaient traditionnellement un moteur de traitement de flux distinct, comme Apache Flink.\u003C/p\u003E\n\u003Cp\u003ELa \u003Cb\u003Emise à disposition à faible latence\u003C/b\u003E permet d’interroger les données obtenues à la vitesse requise par la charge de travail. L’analytique interactive permet d'exécuter des requêtes en moins d'une seconde, même avec un grand nombre d'utilisateurs simultanés.\u003C/p\u003E\n\u003Cp\u003EComme ces trois étapes s’exécutent nativement dans Snowflake, chaque ligne bénéficie de la gouvernance, des contrôles d’accès et de la traçabilité que vous avez déjà configurés. Aucune stack de streaming distincte n’est à sécuriser ni à rapprocher.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_treat-streaming-as-a-design-choice-not-a-default":{"id":"title-v2-a14fbe7e35","additionalClasses":"anchor-title anchor-title--treat-streaming-as-a-design-choice-not-a-default","type":"heading2","lines":["Faire du streaming un choix de conception, pas un réflexe"],":type":"snowflake-site/components/title-v2"},"text_treat-streaming-as-a-design-choice-not-a-default_0":{"id":"text-9846f00fa8","text":"\u003Cp\u003EDès qu'il est question d'analytique en temps réel, le premier réflexe est souvent de passer directement à l'analytique en streaming : ingestion continue, traitement en transit, toute la pile technologique. Pourtant, l'analytique en temps réel est la notion générale, et l'analytique en streaming n'est qu'une des façons d'y parvenir. De nombreuses charges de travail en temps réel sont très bien servies par des requêtes rapides sur des données fréquemment actualisées, sans la moindre architecture de données en mouvement.\u003C/p\u003E\n\u003Cp\u003EPartez des besoins réels de la charge de travail : le degré de fraîcheur nécessaire, la nécessité ou non de transformer les données pour les rendre exploitables, et l'obligation ou non d'effectuer cette transformation en transit. Ces réponses détermineront quelles briques de l'analytique en streaming intégrer à votre architecture. Vous aurez ainsi moins de composants à exploiter et à rapprocher, tout en bénéficiant des performances du streaming là où elles sont réellement utiles.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_treat-streaming-as-a-design-choice-not-a-default_0":{"id":"text-d5fc2e447f","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EÀ RETENIR\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EPartez de la charge de travail, pas de la technologie. Définissez le niveau de fraîcheur des données requis, la nécessité ou non d'une transformation en transit et la rapidité de requête attendue par l'application ou l'utilisateur final. Ne mettez ensuite en place que les briques de l'analytique en streaming que ces réponses exigent.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"}},"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",":itemsOrder":["container"],":items":{"container":{"additionalClasses":"sticky-sidebar","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_943981956_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-51f3133bf4",":itemsOrder":["text_943981956_copy_","text_copy"],":items":{"text_943981956_copy_":{"id":"text-d1067c1f64","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-ece2b965e3","additionalClasses":"page-toc","text":"\u003Cul\u003E\u003Cli data-anchor=\"what-is-streaming-analytics\"\u003EQu’est-ce que l’analytique en streaming ?\u003C/li\u003E\u003Cli data-anchor=\"streaming-analytics-vs-real-time-analytics\"\u003EAnalytique en streaming ou analytique en temps réel\u003C/li\u003E\u003Cli data-anchor=\"why-stream-processing-results-require-context\"\u003EPourquoi les résultats du traitement des flux nécessitent du contexte\u003C/li\u003E\u003Cli data-anchor=\"when-streaming-analytics-is-worth-the-cost\"\u003EQuand le coût de l’analytique en streaming se justifie\u003C/li\u003E\u003Cli data-anchor=\"running-streaming-analytics-on-snowflake\"\u003EExécuter l’analytique en streaming sur Snowflake\u003C/li\u003E\u003Cli data-anchor=\"treat-streaming-as-a-design-choice-not-a-default\"\u003EFaire du streaming un choix de conception, pas un réflexe\u003C/li\u003E\u003C/ul\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-05"}},"appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium",":type":"snowflake-site/components/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-846361e784","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",":itemsOrder":["title_v2_copy","text_copy"],":items":{"title_v2_copy":{"id":"title-v2-18d6487403","additionalClasses":"hub-faq__headline","type":"heading2","lines":["Foire aux questions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-4b546b7f76","additionalClasses":"hub-faq__subheadline","text":"\u003Cp\u003ELes experts Snowflake répondent à vos questions les plus fréquentes sur l’analytique en streaming.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},"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",":itemsOrder":["simple_snowflake_acc"],":items":{"simple_snowflake_acc":{"id":"simple-snowflake-accordion-991403874b","additionalClasses":"seo-hub__faqs","showDivider":false,"accordionItemsList":[{"title":"L’analytique en streaming est-elle identique à l’analytique en temps réel ?","richText":"\u003Cp\u003ENon. L’analytique en streaming est une composante de l’analytique en temps réel. L’analytique en temps réel désigne toute charge de travail dans laquelle les données et les résultats doivent être suffisamment récents pour permettre de prendre rapidement une décision ou d’agir. Cette fraîcheur peut provenir d’une ingestion en streaming, d’un chargement par micro-batch ou d’une combinaison des deux. L'analytique en streaming implique précisément l'ingestion continue des données et, si nécessaire, leur traitement en transit, avant leur arrivée dans la couche de restitution, où elles peuvent être interrogées avec une faible latence.\u003C/p\u003E\r\n"},{"title":"Quand privilégier l’analytique en streaming au traitement par batch ?","richText":"\u003Cp\u003EL'analytique en streaming est utile lorsque les données doivent être disponibles pour l'analyse en quelques secondes, et qu'un traitement en transit (traitement de flux) est nécessaire pour transformer, enrichir ou dédoublonner les événements avant qu'ils n'atteignent la couche de restitution. Si des mises à jour périodiques permettent toujours d’obtenir la réponse à temps, l’analytique en temps quasi réel est souvent plus simple et moins coûteuse à 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