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Ho"}},{"authorImage":{"id":"image-13916325e0","height":"230","src":"/adobe/dynamicmedia/deliver/dm-aid--78ee8e11-b5cf-4d8a-926e-7c6b5c3a4393/jennifer-wu.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"212",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-a309008c4b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jennifer-wu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Jennifer Wu"}}],"image":{"id":"image-e94a07537d","height":"392","alt":"Graphic showing the ingestion, transformation, activation workflow for AI-ready data","src":"/adobe/dynamicmedia/deliver/dm-aid--db778a76-99df-478a-be58-bacb79463dcc/ai-ready-data-foundation-blog-header.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1151",":type":"snowflake-site/components/image"},"timeToRead":"12","publicationDate":"NOV 03, 2026","tag":{"tagText":"Analytics","tagColor":"#29B5E8"},"title":{"lines":["Analytique en temps réel sur Snowflake : bien frais et servis à point nommé, les flux coulent à flots"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"},"container":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{},"id":"container-f7d7c6618b",":type":"snowflake-site/components/container",":items":{},":itemsOrder":[]},"button":{"id":"button-ad5fbfd235","showOutboundIcon":false,"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"UNKNOWN"}},":itemsOrder":["blog_hero","container","button"]},"responsivegrid_content":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_component_1":"aem-GridColumn aem-GridColumn--default--12","youtube":"aem-GridColumn 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aem-GridColumn--default--12","blog_component_13":"aem-GridColumn aem-GridColumn--default--12","blog_component_12":"aem-GridColumn aem-GridColumn--default--12","blog_component_11":"aem-GridColumn aem-GridColumn--default--12","blog_component_10":"aem-GridColumn aem-GridColumn--default--12","blog_component_20":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12"},"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_component_0":{"id":"blog-text-2f97b52120","text":"\u003Cp\u003EChaque interaction client est une occasion de gagner leur confiance ou de perdre leur attention. Pour rester compétitives, les entreprises doivent agir avant même la fin de cette interaction. Une équipe des services financiers a besoin des dernières données de marché pour discuter avec ses clients des ajustements à apporter à leur portefeuille. Une équipe des opérations réseau doit savoir dès qu’un réseau commence à se dégrader afin d’aider les clients concernés pendant leur échange avec le support. Un retailer doit personnaliser l’expérience pendant que le client effectue encore ses achats et optimiser ses stocks en fonction de la demande en temps réel. Et un agent d’IA a besoin d’un contexte fiable, actuel et précis pour recommander la meilleure action à entreprendre, résoudre un problème ou éclairer une décision.\u003C/p\u003E\u003Cp\u003EDans chaque cas, la valeur des données dépend de la rapidité avec laquelle les équipes passent de l’observation à l’action. Lorsqu’une équipe détecte rapidement un problème, en comprend l’impact et agit sur-le-champ, elle augmente son chiffre d’affaires, améliore l’expérience client et prend de meilleures décisions. Cette urgence et cette rapidité d’accès aux informations peuvent différencier votre business de la concurrence. Pourtant, construire un stack en temps réel a souvent nécessité d’assembler manuellement des systèmes distincts pour l’ingestion, la transformation et la mise à disposition : connecter les producteurs à un broker de messages, déployer des tâches de transformation séparées et charger les résultats dans une nouvelle base de données. Chaque couche ajoute de l’infrastructure à exploiter, des spécialistes à recruter, des pipelines susceptibles de tomber en panne, des coûts à maîtriser et de nouvelles règles de sécurité à maintenir.\u003C/p\u003E\u003Cp\u003EC’est pourquoi Snowflake lance \u003Cstrong\u003Eune\u003C/strong\u003E \u003Cstrong\u003Eexpérience\u003C/strong\u003E \u003Cstrong\u003Econnectée\u003C/strong\u003E \u003Cstrong\u003Equi rend\u003C/strong\u003E \u003Cstrong\u003El’analytique en temps réel\u003C/strong\u003E plus simple à créer, plus économique à faire évoluer et gouvernée à chaque étape. Elle réunit la connectivité, la transformation et la mise à disposition dans les tables natives Snowflake et les tables Apache Iceberg™, afin que les équipes transforment les données récentes en décisions sans gérer de stack fragmenté ni renoncer au libre choix de leurs moteurs et outils.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"blog-title-c39a78117e","propertiesId":"decouvrez-snowflake-pour-lanalytique-en-temps-reel","type":"heading2","lines":["Découvrez Snowflake pour l’analytique en temps réel"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_2":{"id":"blog-text-e0b9e4d79f","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/fr/analytics/real-time-analytics/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EL’analytique en temps réel\u003C/a\u003E permet de transformer des données actualisées en informations fiables et en actions concrètes en quelques secondes. L’analytique en temps réel traite et analyse les données à mesure que les événements se produisent. Elle offre aux utilisateurs et aux systèmes une vision actuelle de la situation, avec une latence adaptée à chaque charge de travail.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"image-2c7ed8a532","height":"708","alt":"Schéma « Before » d'une architecture streaming temps réel fragmentée. Les sources (applications et services, appareils IoT, topics Kafka, agents IA) alimentent quatre blocs successifs. Le premier est le bus de messages, un cluster Kafka avec ses brokers. Le deuxième est le moteur de traitement, avec des jobs Flink ou Spark. Le troisième est une base de service à faible latence. Le dernier regroupe les applications et tableaux de bord pour analystes, utilisateurs et IA. Chaque étape ajoute une copie des données, un runtime ou un modèle de sécurité supplémentaire. Un connecteur alimente aussi un entrepôt analytique, synchronisé avec la base de service par des jobs de synchronisation : encore une copie, encore une synchronisation. Des pictogrammes d'alerte et de coût ($$) soulignent le résultat : stockage en silos, moteurs multiples, gouvernance fragmentée.","src":"/adobe/dynamicmedia/deliver/dm-aid--48543ea8-718e-49d3-b050-6f74307f7b2a/architecture-diagram-showing-data-flow-from-message-bus.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":true,"width":"1786","title":"Figure 1 : Architecture traditionnelle d’analytique en temps réel.",":type":"snowflake-site/components/image"},"blog_component_4":{"id":"blog-text-bcf8591fff","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ESnowflake réunit désormais la connectivité, la transformation et la mise à disposition pour assurer une analytique en temps réel de bout en bout. Cet article présente ces fonctionnalités : connecter les données, les transformer à la volée et les servir rapidement. Snowflake répond aux besoins suivants sans obliger les équipes à reconstruire leur architecture pour chacun d’eux.\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003ESimplicité pour faire évoluer vos projets :\u003C/b\u003E créer des pipelines en temps réel sans devoir maîtriser chacun des systèmes sous-jacents. Snowflake gère l’infrastructure, son évolution et le pipeline sur l’ensemble de la connectivité, de la transformation et de la mise à disposition. Les fonctionnalités de \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/fr/product/snowflake-coco/\"\u003ESnowflake CoCo\u003C/a\u003E transforment la configuration du pipeline en une conversation guidée. Décrivez votre besoin : CoCo configure, crée et implémente la logique.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDes performances élevées pour un coût total réduit :\u003C/b\u003E obtenez des informations récentes sans financer un stack en temps réel entièrement distinct. Votre équipe peut se passer de bases de données de mise à disposition séparées et de stockage dupliqué, ainsi que des efforts d’ingénierie nécessaires pour les exploiter, les surveiller et les dépanner. Lors de nos tests internes, nous avons observé une \u003Cb\u003Elatence de bout en bout P50 de trois secondes\u003C/b\u003E\u003Csup data-footnote-ref=\"true\" id=\"footnote-ref-1\"\u003E\u003Ca href=\"#footnote-1\"\u003E[1]\u003C/a\u003E\u003C/sup\u003E avec Snowpipe Streaming, les transformations en streaming et Snowflake Interactive Analytics.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EUne gouvernance à chaque étape :\u003C/b\u003E découvrir et sécuriser les données et les ressources d’IA, puis créer avec elles dans un environnement intégré. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/fr/product/features/horizon/\"\u003EHorizon Catalog\u003C/a\u003E centralise dans Snowflake les contrôles d’accès, la traçabilité, la qualité des données et le contexte stratégique. Créer un climat de confiance dans toute votre organisation grâce à la surveillance de la qualité des données et à la classification des données et des ressources d’IA.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"image-d248bf8a28","height":"776","alt":"Schéma « After » d'une architecture streaming unifiée dans Snowflake. Les sources (applications et services, appareils IoT, topics Kafka, agents IA) alimentent trois étapes. Connect ingère les données en continu via Snowpipe Streaming (API directe, GA) et Datastream (compatible Kafka, préversion publique bientôt). Transform les traite à la fraîcheur requise grâce à Streaming Transformations, des transformations à la volée en préversion privée. Serve les met à disposition des personnes, applications et IA : analytique interactive avec service à faible latence (GA), et tableaux de bord, applications et IA qui partagent les mêmes données. En socle, le catalogue Horizon assure une gouvernance unifiée.","src":"/adobe/dynamicmedia/deliver/dm-aid--24c263a1-d917-4c58-9470-abd2edcdc19c/snowflake-data-platform-architecture-diagram-showing.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1646","title":"Figure 2 : Snowflake réunit la connexion, la transformation et la mise à disposition des données en temps réel au sein d’un même environnement.",":type":"snowflake-site/components/image"},"blog_component_6":{"id":"blog-title-d56b91df8a","propertiesId":"connecter-les-donnees-en-temps-reel-sans-ajouter-dinfrastructure","type":"heading2","lines":["Connecter les données en temps réel sans ajouter d’infrastructure"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_7":{"id":"blog-text-9dfb6ba94c","text":"\u003Cp\u003EL’analytique en temps réel commence par une ingestion rapide et simple des données. \u003Cstrong\u003ESnowpipe Streaming\u003C/strong\u003E achemine les données des applications, appareils et services directement vers les tables natives Snowflake ou les Iceberg Tables. Les équipes peuvent choisir librement le format de table adapté à leur architecture. Les tables natives offrent une solution simple et entièrement gérée pour l’analytique opérationnelle et les applications. Les Iceberg Tables conservent les données dans un format ouvert accessible à d’autres outils. Les données sont interrogeables en quelques secondes, avec un débit de \u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/fr/product/features/snowpipe-streaming/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ejusqu’à 20 Go/s par table\u003C/a\u003E\u003C/strong\u003E et une tarification fixe basée sur l’ingestion.\u003C/p\u003E\u003Cp\u003EL’ingestion est souvent le poste budgétaire qui détermine si un projet en temps réel verra le jour. Lorsque les coûts augmentent avec chaque nouvelle source, les équipes conservent moins de données actualisées qu’elles ne le souhaiteraient. À charge de travail comparable, Snowpipe Streaming est \u003Cstrong\u003E5 fois moins cher\u003C/strong\u003E\u003Csup id=\"footnote-ref-2\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-2\"\u003E[2]\u003C/a\u003E\u003C/sup\u003E que Zerobus et \u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/en/blog/engineering/efficient-snowflake-ingestion-query-ready/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E80 % moins cher\u003C/a\u003E\u003C/strong\u003E que ClickPipes\u003Csup id=\"footnote-ref-3\" data-footnote-ref=\"true\"\u003E\u003Ca href=\"#footnote-3\"\u003E[3]\u003C/a\u003E\u003C/sup\u003E. Vous payez un tarif fixe basé sur le volume de données ingéré, ce qui garantit des coûts prévisibles à mesure que vous ajoutez des sources.\u003C/p\u003E\u003Cp\u003EPour les équipes des services financiers, chaque seconde compte. Rendre les activités de trading, les historiques de transactions et les événements opérationnels disponibles pour analyse en quelques secondes permet aux équipes d’agir à partir d’informations à jour. \u003Ca href=\"https://youtu.be/sHi8ZMS2JJw?si=7xzxX-KjG_UUeR9C&t=1002\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ECboe\u003C/a\u003E \u003Ca href=\"https://youtu.be/sHi8ZMS2JJw?si=7xzxX-KjG_UUeR9C&t=1002\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EGlobal Markets\u003C/a\u003E illustre l’ampleur de cette approche : l’entreprise utilise Snowpipe Streaming pour charger \u003Cstrong\u003E190 milliards de\u003C/strong\u003E \u003Cstrong\u003Elignes par jour en moins de 30 secondes\u003C/strong\u003E, puis fournit directement ces données aux analystes via des entrepôts interactifs.\u003C/p\u003E\u003Cp\u003EÀ mesure que le volume d’événements augmente, les équipes ne devraient pas consacrer davantage de temps à gérer les mécanismes d’ingestion. Avec les \u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/fr/blog/snowpipe-streaming-elastic-channels-ga/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EElastic Channels\u003C/a\u003E\u003C/strong\u003E de Snowpipe Streaming, disponibles pour tous nos clients, des centaines de producteurs, notamment des applications et des appareils Internet des objets (IoT), peuvent écrire vers une même destination par l’intermédiaire d’un canal élastique unique. Vous pouvez connecter davantage de sources sans créer ni gérer de canaux supplémentaires. Snowflake adapte automatiquement le chemin d’ingestion à l’augmentation des volumes. Les événements sont livrés dans l’ordre, une seule fois. Les équipes peuvent ainsi se consacrer à l’analyse plutôt qu’à l’écriture d’une logique de déduplication en aval. Lors de nos tests internes, nous avons encore augmenté le débit pour atteindre \u003Cstrong\u003E\u003Ca href=\"https://www.snowflake.com/fr/blog/snowpipe-streaming-elastic-channels-benchmark/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003E41 Go/s dans une seule table\u003C/a\u003E\u003C/strong\u003E.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_8":{"id":"image-5ec41c3a3e","height":"736","alt":"Schéma comparant deux chemins d'ingestion de données en streaming vers Snowflake. En haut, un parcours multi-étapes illustratif, dont les composants varient selon la charge de travail. Les producteurs de données (appareils, applications, services) passent par un bus de messages (brokers, partitions), puis par des connecteurs (routage, gestion des schémas), puis par un stockage objet (staging, copies en double), avant d'arriver dans les tables Snowflake. En bas, Snowpipe Streaming Elastic Channels simplifie ce flux : des milliers d'écrivains indépendants envoient leurs données via des canaux élastiques, avec mise à l'échelle automatique et accusé de réception durable, par SDK ou REST. Les données arrivent directement dans des tables Snowflake ou Iceberg, prêtes à être traitées, interrogées et analysées.","src":"/adobe/dynamicmedia/deliver/dm-aid--46e74095-8ed5-4b59-b58e-1e3f5e6c373f/multi-hop-data-pipeline-architecture-with-snowpipe.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 3 : Elastic Channels permet d’ingérer des données en streaming directement dans Snowflake, sans ajouter de système intermédiaire dédié à l’ingestion.",":type":"snowflake-site/components/image"},"blog_component_9":{"id":"blog-text-3bd60bbce0","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EDe nombreuses équipes utilisent déjà des producteurs et consommateurs Apache Kafka®, mais l’infrastructure de streaming est souvent conçue pour les ingénieurs infrastructure. \u003Cb\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/fr/product/features/datastream/\"\u003EDatastream\u003C/a\u003E\u003C/b\u003E, bientôt disponible en public preview et actuellement en private preview, adopte une approche centrée sur les données. Ce service natif Snowflake compatible avec Kafka permet aux équipes data d’utiliser leurs producteurs et consommateurs Kafka existants avec un endpoint géré, sans exploiter de brokers ni maintenir une couche de connecteurs distincte pour transférer les données vers les tables. Le streaming centré sur les données recentre les équipes sur les données gouvernées : elles gèrent les topics, la fraîcheur et les pipelines plutôt que les partitions, la réplication et la capacité du cluster. Des clients comme Minted et Zywave adoptent cette approche pour réduire la multiplication des systèmes d’authentification, des rôles et des outils de surveillance entre Kafka et Snowflake.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_10":{"id":"image-1a0b77f728","height":"666","alt":"Tableau comparatif en deux colonnes sur le streaming de données. À gauche, en orange, l'approche centrée infrastructure telle qu'elle fonctionne aujourd'hui : on part d'un cluster, on raisonne en partitions, réplication et rééquilibrage, il faut configurer un connecteur pour charger les données dans une table, et on monte en charge en redimensionnant le cluster. À droite, en bleu, l'approche centrée données de Datastream : on part d'un topic, traité comme un actif du catalogue, on raisonne en données, fraîcheur et pipelines, le chargement topic-vers-table est intégré, et on monte en charge en allouant un groupe de brokers, comme un entrepôt (warehouse).","src":"/adobe/dynamicmedia/deliver/dm-aid--e19df8a7-297b-42f5-988f-ad703d92ef26/comparison-diagram-showing-infra-centric-versus-data.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 4 : Le streaming centré sur les données transforme le point de départ, la manière d’aborder les projets et les tâches auxquelles les équipes consacrent leur temps.",":type":"snowflake-site/components/image"},"blog_component_11":{"id":"blog-title-760595d761","propertiesId":"transformer-les-donnees-avec-le-niveau-de-fraicheur-dont-votre-business-a-besoin","type":"heading2","lines":["Transformer les données avec le niveau de fraîcheur dont votre business a besoin"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_12":{"id":"blog-text-125ec1cb1e","text":"\u003Cp\u003ELes données récentes n’ont de valeur qu’une fois adaptées aux besoins du business. En filtrant, enrichissant, restructurant et agrégeant les données entrantes pendant leur ingestion avec Snowpipe Streaming, les \u003Cstrong\u003Etransformations en streaming\u003C/strong\u003E, en private preview, les rendent exploitables pour l’analytique en temps réel. Les équipes définissent la logique une seule fois dans une définition de pipe SQL, puis les enregistrements sont transformés avant d’être chargés dans une table.\u003C/p\u003E\u003Cp\u003ELes équipes peuvent éliminer les événements non pertinents, enrichir les enregistrements entrants avec des données de référence, appliquer une logique personnalisée et développer des tableaux imbriqués en lignes individuelles. Intégrer la transformation au chemin d’ingestion simplifie le stack de streaming et accélère l’exploitation des données, comme le constatent déjà plusieurs clients Snowflake. Un fabricant d’équipements industriels standardise les identifiants de ses clients et de ses actifs afin que ses workflows analytiques reçoivent des enregistrements nommés de manière cohérente. Parallèlement, une entreprise de cybersécurité utilise les transformations en streaming pour traiter les événements de sécurité en temps réel et réduire les coûts de traitement ainsi que la latence de la surveillance et du reporting.\u003C/p\u003E\u003Cp\u003EPour les autres charges de travail prises en charge, les transformations en streaming peuvent supprimer le besoin d’une couche externe de traitement des flux, ainsi que celui de créer, planifier et maintenir des transformations en aval après le chargement des données. Résultat : moins d’infrastructure à exploiter et un parcours plus court entre les événements bruts et les informations exploitables. Les transformations en streaming prennent en charge les tables Snowflake standard et interactives, ainsi que les Iceberg Tables gérées par Snowflake. Les objectifs actuels de fraîcheur entre l’ingestion et la mise à disposition sont inférieurs à trois secondes pour les destinations interactives et à 10 secondes pour les destinations standard et Iceberg.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_13":{"id":"image-f684d2116f","height":"725","alt":"Visuel sur l'enrichissement des données en streaming dans Snowflake. À gauche, trois encadrés présentent les fonctionnalités. In-Stream Join Processing combine en continu les flux temps réel avec des jeux de données de référence existants. Reference Dimension Tables exécute des LEFT OUTER JOIN sur des tables de dimension, comme les profils utilisateurs ou les localisations. Rows Land Enriched charge les enregistrements enrichis directement dans les tables cibles Snowflake, en une seule étape. À droite, le fichier SQL enriched_events_pipe.sql crée un pipe (CREATE PIPE … COPY INTO enriched_events). Il lit une source de données de type 'STREAMING', convertit les champs event_id, event_time, amount et user_id, et effectue des jointures sur les tables dim_users et dim_cities pour ajouter user_name et city.","src":"/adobe/dynamicmedia/deliver/dm-aid--2bf0f9a5-5597-4fc5-86f2-b865563037fd/stream-processing-features-alongside-sql-code-screenshot.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"1999","title":"Figure 5 : Une jointure externe gauche associe le flux entrant aux tables de dimensions de référence afin que les lignes enrichies soient directement chargées dans la table cible.",":type":"snowflake-site/components/image"},"blog_component_14":{"id":"blog-title-449017d114","propertiesId":"fournir-des-reponses-en-moins-dune-seconde-a-grande-echelle-avec-interactive-analytics","type":"heading2","lines":["Fournir des réponses en moins d’une seconde à grande échelle avec Interactive Analytics"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_15":{"id":"blog-text-e275ddfa0c","text":"\u003Cp\u003EC’est souvent dans la dernière ligne droite que les projets en temps réel échouent. Les tableaux de bord, les applications destinées aux clients et les agents d’IA interrogent simultanément les mêmes données. Pour tenir la cadence, les équipes se tournent souvent vers une base de données de mise à disposition distincte. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/analytics/interactive-analytics/\"\u003EInteractive Analytics\u003C/a\u003E prend directement en charge ces charges de travail dans Snowflake.\u003C/p\u003E\n\u003Cp\u003ELors de tests internes, \u003Cb\u003Eles entrepôts interactifs ont exécuté\u003C/b\u003E des requêtes en moins d’une seconde, avec \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/introducing-interactive-analytics/\"\u003Eune concurrence neuf fois supérieure et une latence trois fois inférieure\u003C/a\u003E à celles des entrepôts standard. Avec \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/zero-copy-interactive-analytics/\"\u003EZero-Copy Interactive\u003C/a\u003E, disponible pour tous nos clients, ils s’exécutent directement sur les tables standard, dynamiques et Iceberg Tables existantes. Aucune copie spécialisée n’est nécessaire pour servir les données avec une faible latence. Pour l’ingestion en streaming, nous prenons en charge jusqu’à 41 Go/s par table, et les données deviennent interrogeables en quelques secondes. \u003C/p\u003E\n\u003Cp\u003EDans deux benchmarks menés par \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/\"\u003EMcKnight Consulting Group\u003C/a\u003E, à coût de calcul horaire comparable, Snowflake a exécuté \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/when-everyone-asks-at-once-designing-snowflake-for-interactive-analytics/\"\u003E100 % des requêtes\u003C/a\u003E à chaque niveau de concurrence testé, jusqu’à 32 utilisateurs simultanés. Sur les requêtes de tableaux de bord, Snowflake a également fourni un \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/counting-distincts-and-high-cardinality-aggregations-designing-snowflake-for-interactive-analytics/\"\u003Edébit plus de cinq fois supérieur\u003C/a\u003E à celui d’une base de données d’analytique en temps réel leader, tandis que son concurrent a échoué sur 73 % des agrégations à forte cardinalité. Lors d’un \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://mcknightcg.com/auto-scaling-for-spiky-workloads-designing-snowflake-for-interactive-analytics/\"\u003Etroisième test\u003C/a\u003E simulant un pic soudain de trafic multiplié par cinq, Snowflake a automatiquement augmenté sa capacité et maintenu 99,997 % des requêtes dans les limites du SLA, avec une latence p99 inférieure de 40 % à celle du concurrent dimensionné pour les pics de charge. Pour égaler les résultats de Snowflake, le concurrent devrait exploiter en permanence une capacité dimensionnée pour les pics, pour un coût annuel presque deux fois supérieur, soit 31 273 USD contre 15 959 USD.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_16":{"id":"image-3862d992fb","height":"235","alt":"Graphique à barres horizontales comparant les coûts de calcul sur un an : 31 278 dollars pour ClickHouse, contre 15 959 dollars pour Snowflake Interactive XS.","src":"/adobe/dynamicmedia/deliver/dm-aid--d84ab79f-09e1-4ed1-8917-92b70e55e948/horizontal-bar-chart-comparing-one-year-compute-costs.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"765","title":"Figure 6. Modélisation du coût de calcul sur un an, avec 52 pics hebdomadaires, sur la base de 3 dollars par crédit Snowflake et de 1,19 dollar par heure-réplique ClickHouse. Seul le calcul est pris en compte ; le stockage est exclu dans les deux cas. Source : https://mcknightcg.com/auto-scaling-for-spiky-workloads-designing-snowflake-for-interactive-analytics/",":type":"snowflake-site/components/image"},"blog_component_17":{"id":"blog-text-e27dcd4f56","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ELa mise à disposition s’effectue sur les mêmes données gouvernées que celles utilisées par votre business. Il n’y a donc aucune tâche de synchronisation à maintenir ni de deuxième copie à sécuriser. Les équipes stockent les données une seule fois et les servent à partir de cette copie. Les coûts restent ainsi prévisibles à mesure que le volume et la concurrence augmentent.\u003C/p\u003E\n\u003Cp\u003EPar exemple, plutôt que de déployer une plateforme distincte de détection des menaces, \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/customers/all-customers/snapshot/canva/\"\u003ECanva a rapproché la détection de ses données\u003C/a\u003E. Interactive Analytics exécute chaque minute des milliers de requêtes de détection sur les journaux d’audit en direct et les données de référence fiables que Canva conserve déjà dans Snowflake, sans deuxième copie à maintenir. Lors d’une évaluation représentative d’un environnement de production, l’entrepôt interactif a pris en charge plus de dix fois la charge simultanée d’un entrepôt standard équivalent, pour un coût modélisé nettement inférieur.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_20":{"id":"quote-item-661b853f94","alignment":"left","layout":"card","quoteSourceName":"Calvin Wang","quoteSourceTitle":"Staff Analytics Engineer, Security Group, Canva","showQuoteIcon":true,"quote":"Nous ne voulions pas créer une plateforme supplémentaire ni constituer une équipe pour l’exploiter. Nos données les plus fiables sur notre produit et notre communauté se trouvaient déjà dans Snowflake. Nous avons donc rapproché la détection des données, ce qui a amélioré la fiabilité de nos alertes. »","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_component_18":{"id":"blog-title-b7485395ec","propertiesId":"commencez-des-aujourdhui","type":"heading2","lines":["Commencez dès aujourd’hui"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_19":{"id":"blog-text-a51307fd54","text":"\u003Cp\u003ELes entreprises ont besoin d’un moyen plus simple de transformer les données récentes en décisions. Snowflake fournit un environnement connecté pour l’analytique en temps réel : simple à exploiter, rapide pour servir les données, économique à faire évoluer, ouvert aux tables natives comme aux Iceberg Tables et gouverné du premier événement à la décision finale. Et Snowflake CoCo facilite la création à chaque étape. Décrivez simplement votre besoin en langage naturel : CoCo peut vous aider à configurer la connectivité, à créer les transformations et à paramétrer Interactive Analytics.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"youtube":{"id":"embed-d8d81178ec","youtubeVideoId":"wdmXRp8Cq2w","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"},"blog_text":{"id":"blog-text-b0e756917e","text":"\u003Cp\u003EDécouvrez comment tous ces éléments s’articulent dans notre webinaire à la demande « \u003Ca href=\"https://www.snowflake.com/en/webinars/thought-leadership/realtime-data-the-foundation-of-agentic-ai-2026-09-30/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EDonnées en temps réel : les fondations de l’IA agentique\u003C/a\u003E », dans lequel nous présentons les bonnes pratiques et une architecture de référence pour créer une analytique en temps réel au service des tableaux de bord, des applications et des agents d’IA.\u003C/p\u003E\n\u003Chr\u003E\n\n\u003Col data-footnotes=\"true\"\u003E\n\u003Cli id=\"footnote-1\"\u003E La latence médiane (P50) de mise à disposition des données, soit 3 secondes, a été mesurée lors de tests internes menés par Snowflake le 20 octobre 2026. Ces tests couvraient l’ensemble du parcours dans Snowpipe Streaming, les transformations en streaming et Interactive Analytics. \u003Ca href=\"#footnote-ref-1\" aria-label=\"Retour à la note [1]\"\u003E↩\u003C/a\u003E\u003C/li\u003E\n\u003Cli id=\"footnote-2\"\u003E Comparaison des coûts basée sur Snowpipe Streaming à 0,0037 crédit/Go × 2,35 USD/crédit, soit le prix moyen d’un crédit Snowflake, pour un total d’environ 0,0087 USD/Go. Comparé au prix catalogue de Databricks Zerobus, soit 0,05 USD/Go, ce tarif est environ 5,75 fois inférieur, chiffre arrondi à environ 5 fois en moyenne. \u003Ca href=\"#footnote-ref-2\" aria-label=\"\"\u003E\u003C/li\u003E\n\u003Cli id=\"footnote-3\"\u003E Comparé au tarif publié de ClickPipes pour l’ingestion en streaming, soit 0,04 USD/Go, ce coût est inférieur d’environ 78 %, chiffre arrondi à 80 %. ClickPipes facture également le calcul séparément. Ces frais sont exclus de la comparaison. \u003Ca href=\"#footnote-ref-3\" aria-label=\"\"\u003E\u003C/li\u003E\n\u003C/ol\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_component_0","blog_component_1","blog_component_2","blog_component_3","blog_component_4","blog_component_5","blog_component_6","blog_component_7","blog_component_8","blog_component_9","blog_component_10","blog_component_11","blog_component_12","blog_component_13","blog_component_14","blog_component_15","blog_component_16","blog_component_17","blog_component_20","blog_component_18","blog_component_19","youtube","blog_text"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 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Manager"},{"authorImage":{"id":"image-13916325e0","height":"230","src":"/adobe/dynamicmedia/deliver/dm-aid--78ee8e11-b5cf-4d8a-926e-7c6b5c3a4393/jennifer-wu.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"212",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-a309008c4b","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/jennifer-wu/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Jennifer Wu"},"authorTitle":"Product Marketing Manager at Snowflake"}],":type":"snowflake-site/components/blog/author-chip"}},":itemsOrder":["author_chip"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-medium"},"container_share_article":{"layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"share_article":"aem-GridColumn 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Analytics"],"type":"heading4",":type":"snowflake-site/components/title-v2"},"button":{"id":"button-765d584586","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/fr/blog/introducing-interactive-analytics/"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":" Introducing Snowflake Interactive Analytics for Modern Data Analytics"},"publicationDate":"DEC 15, 2025","authors":[]}],":type":"snowflake-site/components/blog/related-content","isBlogPage":true}},":itemsOrder":["flexible_column_container","related_content"],"appliedCssClassNames":"snowflake-container"}},":itemsOrder":["container_breadcrumb","container_main_content"],":type":"wcm/foundation/components/responsivegrid"},"container_47873732":{"additionalClasses":"section--blog-newsletter","layout":"RESPONSIVE_GRID","columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn 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