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aem-GridColumn--default--12","blog_component_12":"aem-GridColumn aem-GridColumn--default--12","blog_component_11":"aem-GridColumn aem-GridColumn--default--12","blog_component_22":"aem-GridColumn aem-GridColumn--default--12","blog_component_10":"aem-GridColumn aem-GridColumn--default--12","blog_component_21":"aem-GridColumn aem-GridColumn--default--12","blog_component_20":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_component_0":{"id":"blog-text-45c328f38a","text":"\u003Cp\u003EDans le cadre de mon travail avec les clients de Snowflake, l’une des évolutions les plus intéressantes que j’ai constatées concerne nos échanges sur l’observabilité.\u003C/p\u003E\u003Cp\u003EIl y a quelques années, les responsables ingénierie se concentraient sur l’instrumentation et la visibilité : Comment collecter davantage de télémétrie ? Comment réduire le temps moyen de résolution ? Comment gagner en visibilité sur des systèmes toujours plus complexes ?\u003C/p\u003E\u003Cp\u003EAujourd’hui, \u003Ca href=\"https://opentelemetry.io/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EOpenTelemetry\u003C/a\u003E et les plateformes d’observabilité modernes comme \u003Ca href=\"https://www.snowflake.com/en/product/observe/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003EObserve by Snowflake\u003C/a\u003E permettent de répondre plus facilement à bon nombre de ces questions. Les échanges sur l’observabilité peuvent désormais commencer par les \u003Cstrong\u003Eindicateurs et résultats métier\u003C/strong\u003E, plutôt que par les journaux, les métriques et les traces. L’objectif n’est plus seulement de comprendre ce qui se passe dans les systèmes distribués, mais de \u003Cstrong\u003Erelier les données techniques à\u003C/strong\u003E \u003Cstrong\u003Eleur signification pour\u003C/strong\u003E \u003Cstrong\u003El’entreprise\u003C/strong\u003E.\u003C/p\u003E\u003Cp\u003ECette évolution soulève plusieurs questions concrètes pour les équipes d’ingénierie :\u003C/p\u003E\u003Cul\u003E\u003Cli\u003ECombien de temps devons-nous conserver la télémétrie ?\u003C/li\u003E\u003Cli\u003EComment relier les données opérationnelles aux données clients et métier ?\u003C/li\u003E\u003Cli\u003EComment la télémétrie peut-elle servir l’analytique et l’IA ?\u003C/li\u003E\u003Cli\u003EComment garantir que les données d’observabilité restent ouvertes et sous notre contrôle ?\u003C/li\u003E\u003C/ul\u003E\u003Cp\u003ECes questions sont loin d’être théoriques. Elles se posent dès que l’impact technique d’un incident soulève des enjeux d’attrition client, de chiffre d’affaires et d’impact métier.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_1":{"id":"blog-title-827ea8a7d8","propertiesId":"lobservabilite-ne-sarrete-pas-avec-lincident","type":"heading2","lines":["L’observabilité ne s’arrête pas avec l’incident"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_2":{"id":"blog-text-6772fe807f","text":"\u003Cp\u003EPrenons l’exemple fictif d’une grande entreprise de services financiers comptant plusieurs millions de clients. Comme dans de nombreuses entreprises numériques, quelques minutes de baisse des performances peuvent suffire à dégrader l’expérience client, à multiplier les demandes d’assistance et à menacer le chiffre d’affaires.\u003C/p\u003E\u003Cp\u003ELors d’un incident en production, l’équipe d’ingénierie a constaté une latence élevée sur un service de paiement destiné aux clients. Au départ, l’origine du problème était difficile à déterminer : l’application, une dépendance en amont ou l’infrastructure sous-jacente. L’équipe ne pouvait pas déterminer immédiatement quels clients ou quelles transactions étaient affectés.\u003C/p\u003E\u003Cp\u003EEn s’appuyant sur l’Observability Context Graph d’Observe et sur le \u003Ca href=\"https://www.snowflake.com/fr/blog/ai-sre-unified-telemetry-context-graph-incident-investigation/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003ESite Reliability Engineering assisté par IA\u003C/a\u003E (SRE IA), l’équipe a unifié des Streams de télémétrie disparates pour isoler le composant défaillant, cartographier ses répercussions à travers des dépendances complexes et rétablir le bon fonctionnement de l’environnement.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_3":{"id":"image-4fd5396ec4","height":"954","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--5132b307-08f8-42ce-990c-250b2bdf36fd/interactive-network-graph-visualization-showing-connected.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Graphe interactif montrant des nœuds bleus et violets reliés par des liens de traçabilité dans un catalogue de données.","isLcpImage":true,"width":"1690","title":"Figure 1 : l’Observability Context Graph cartographie les relations et les dépendances entre les ressources et les événements. Il aide ainsi les équipes à comprendre pourquoi un incident s’est produit, et pas seulement à déterminer ce qui s’est passé.",":type":"snowflake-site/components/image"},"blog_component_4":{"id":"blog-text-5a0dffd3e5","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003ESur le plan opérationnel, l’incident était clos. Mais sur le plan stratégique, il ne faisait que commencer.\u003C/p\u003E\n\u003Cp\u003EL’équipe d’ingénierie avait répondu à la question « Qu’est-ce qui est tombé en panne ? » L’équipe de direction a toutefois immédiatement posé d’autres questions :\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EQuel a été l’impact sur les clients ?\u003C/li\u003E\n\u003Cli\u003EQuel montant de chiffre d’affaires était menacé ?\u003C/li\u003E\n\u003Cli\u003ECertains prestataires de paiement ont-ils été plus touchés que d’autres ?\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ES’agit-il d’un incident isolé ou du début d’une tendance plus générale en matière de fiabilité ?\u003C/p\u003E\n\u003Cp\u003EPour obtenir ces réponses, il fallait combiner la télémétrie opérationnelle avec les comptes clients, les transactions de paiement, l’historique des déploiements et l’utilisation des produits. À ce stade, l’observabilité n’était plus seulement un enjeu opérationnel : elle devenait un enjeu de données.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_5":{"id":"blog-title-81e849d5ad","propertiesId":"de-la-cause-premiere-a-limpact-metier","type":"heading2","lines":["De la cause première à l’impact métier"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_6":{"id":"blog-text-8ccc981e6e","text":"\u003Cp\u003EEn enrichissant la télémétrie opérationnelle avec des données contextuelles sur les clients, les produits et les finances, les responsables métier et de l’ingénierie de l’organisation financière ont obtenu une vision commune de l’impact de l’incident. Au-delà de l’analyse des causes premières, l’équipe pouvait ainsi quantifier l’impact sur les clients, estimer le chiffre d’affaires menacé et hiérarchiser les mesures correctives selon les résultats métier.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_7":{"id":"image-a90e02f566","height":"662","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--730f6076-260e-4bb0-9003-b8c2bf127786/dashboard-with-six-panels-showing-payment-transaction.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Tableau de bord à six volets affichant les taux d’échec des paiements, l’évolution des pertes de revenus et la répartition des transactions par statut.","isLcpImage":false,"width":"1999","title":"Figure 2 : en ajoutant le contexte métier à la télémétrie opérationnelle, les équipes peuvent dépasser la simple identification de la cause profonde pour mesurer l’impact sur les clients, les transactions et le chiffre d’affaires.",":type":"snowflake-site/components/image"},"blog_component_8":{"id":"blog-text-b7fce6d71f","text":"\u003Cp\u003E \u003C/p\u003E\n\u003Cp\u003EVoilà toute la portée de l’observabilité : Les données qui aident les équipes d’ingénierie à rétablir la production permettent également aux responsables de comprendre l’impact sur les clients, de dégager des tendances de fiabilité à long terme, d’améliorer les décisions d’ingénierie et d’alimenter l’IA.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003EAu lieu de simplement chercher la cause de la panne, les organisations peuvent aussi en mesurer l’impact.\u003C/b\u003E Une fois la télémétrie reliée au contexte métier, la réponse aux incidents devient le point de départ de l’analyse, et non son aboutissement. Il reste alors à déterminer comment conserver, gouverner et réutiliser les données d’observabilité dans les workflows analytiques, d’IA et opérationnels.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_9":{"id":"blog-title-bea9948f5b","propertiesId":"pourquoi-lobservabilite-ouverte-est-essentielle","type":"heading2","lines":["Pourquoi l’observabilité ouverte est essentielle"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_10":{"id":"blog-text-7810066f98","text":"\u003Cp\u003EÀ mesure que leurs pratiques d’observabilité gagnent en maturité, les organisations d’ingénierie découvrent que la valeur de la télémétrie dépasse largement la durée d’un incident. Journaux, métriques et traces deviennent des actifs d’entreprise durables : des données à conserver, gouverner et enrichir aux côtés des données clients, produit et métier.\u003C/p\u003E\n\u003Cp\u003EC’est là que l’observabilité ouverte prend tout son sens. Le standard OpenTelemetry offre une méthode cohérente et indépendante des fournisseurs pour instrumenter les applications. Les technologies ouvertes comme Apache Iceberg™ transforment la manière dont les organisations stockent et gèrent leurs données d’entreprise, en dissociant le stockage du calcul et en favorisant l’interopérabilité entre plusieurs moteurs. \u003Ca rel=\"nofollow noopener noreferrer\" href=\"https://www.snowflake.com/fr/blog/observe-apache-iceberg-open-observability/\" target=\"_blank\"\u003EEn savoir plus sur Observe sur Apache Iceberg\u003C/a\u003E (actuellement en private preview).\u003C/p\u003E\n\u003Cp\u003EEnsemble, ces technologies créent un socle qui évite de confiner la télémétrie à une plateforme opérationnelle. Les organisations peuvent la conserver sous la forme d’un jeu de données ouvert qu’elles contrôlent, gouverner ces données de manière cohérente, les analyser avec leurs autres données d’entreprise et les exploiter dans leurs workflows analytiques, d’IA et opérationnels.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_11":{"id":"blog-title-227cdf0360","propertiesId":"comment-snowflake-et-observe-se-completent","type":"heading2","lines":["Comment Snowflake et Observe se complètent"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_12":{"id":"blog-text-e62daef5ed","text":"\u003Cp\u003ELa question que les responsables ingénierie me posent le plus souvent est la suivante : comment leur plateforme d’observabilité et leur plateforme de données doivent-elles fonctionner ensemble ? La réponse devient évidente lorsque l’on examine de près le cycle de vie d’un incident.\u003C/p\u003E\u003Cp\u003EUne idée reçue consiste à penser que l’intégration de la télémétrie dans Snowflake remplace une plateforme d’observabilité. Sur le terrain, je constate au contraire que les clients considèrent généralement ces plateformes comme complémentaires, et non mutuellement exclusives.\u003C/p\u003E\u003Cp\u003EPour répondre à un incident, les équipes d’ingénierie ont besoin de workflows opérationnels qui réunissent les données et leur contexte afin de comprendre et de résoudre rapidement les problèmes. Snowflake fournit le socle de données, tandis qu’Observe y ajoute des workflows conçus pour l’observabilité, le SRE IA et l’Observability Context Graph afin de relier journaux, métriques, traces et dépendances d’infrastructure. Les équipes peuvent ainsi transformer les données en contexte exploitable et rétablir plus rapidement le service.\u003C/p\u003E\u003Cp\u003EL’analyse post-incident relève d’une autre problématique. Les équipes doivent relier la télémétrie aux données clients et financières pour dégager des tendances à long terme. Une fois intégrées à l’AI Data Cloud de Snowflake, les données de télémétrie peuvent être enrichies et analysées comme un actif stratégique de l’entreprise.\u003C/p\u003E\u003Cp\u003EÀ mesure que l’IA s’impose au cœur des workflows d’ingénierie, le rapprochement de la télémétrie et des données d’entreprise permet aux équipes de détecter des schémas et des anomalies difficiles à repérer dans des systèmes cloisonnés.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_13":{"id":"blog-title-bf1890070d","propertiesId":"pendant-un-incident-une-observabilite-concue-pour-laction-avec-observe","type":"heading3","lines":["Pendant un incident : une observabilité conçue pour l’action avec Observe"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_14":{"id":"blog-text-7b569377ad","text":"\u003Cp\u003ELorsque la production est dégradée, les équipes d’ingénierie ont besoin de réponses immédiates. Les plateformes spécialisées dans l’observabilité, comme Observe, sont conçues pour répondre à ce workflow opérationnel. Le SRE IA aide les équipes à analyser les incidents, à détecter les anomalies et à synthétiser les enseignements, tandis que le graphe contextuel relie les applications, l’infrastructure, les journaux, les métriques et les traces dans une vue unifiée du système.\u003C/p\u003E\u003Cp\u003EEnsemble, ces fonctionnalités apportent aux équipes d’ingénierie le contexte nécessaire pour comprendre ce qui s’est passé et rétablir le service aussi rapidement que possible.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_15":{"id":"image-53c3a04f78","height":"1213","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c9359661-35b3-4a36-83f4-01131ede8252/snowflake-ai-sre-dashboard-showing-outage-root-cause.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Tableau de bord Snowflake AI SRE affichant la cause profonde d’une panne, les éléments de preuve et les erreurs de connexion à Redis.","isLcpImage":false,"width":"1999","title":"Figure 3 : grâce au langage naturel, le SRE IA d’Observe permet aux équipes d’analyser les incidents, de diagnostiquer les services lents ou défaillants et de dégager une synthèse des journaux, métriques et traces.",":type":"snowflake-site/components/image"},"blog_component_16":{"id":"blog-title-fa27daee64","propertiesId":"apres-lincident-le-contexte-dentreprise-grace-a-snowflake","type":"heading3","lines":["Après l’incident : le contexte d’entreprise grâce à Snowflake"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_17":{"id":"blog-text-d62a4e53f1","text":"\u003Cp\u003EUne fois le service rétabli, cette même télémétrie peut être reliée aux données clients, transactionnelles, financières, produit et de déploiement. Les équipes peuvent exploiter ce contexte élargi pour quantifier l’impact métier, identifier les schémas récurrents et hiérarchiser les investissements consacrés à la fiabilité.\u003C/p\u003E\u003Cp\u003EC’est ainsi que Snowflake prolonge la valeur de l’observabilité. La télémétrie peut être conservée et analysée avec les autres données de l’entreprise afin d’alimenter une analytique et une IA gouvernées, sans dissocier les données techniques du contexte métier nécessaire à leur interprétation.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_component_18":{"id":"image-70d1b38dd2","height":"1148","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--c97efd26-ff9e-4ed1-ae65-b7dac9dac0f8/incident-post-mortem-documenting-why-a-deployment-rollout.png?quality=85&preferwebp=true","lazyEnabled":true,"alt":"Rapport post-incident présentant les causes de l’échec d’un déploiement, ses conséquences métier et les mesures correctives.","isLcpImage":false,"width":"1999","title":"Figure 4 : le SRE IA relie les variations de performances aux conditions du déploiement, à leur cause profonde et à leur impact métier, afin d’aider les équipes à en mesurer l’importance.",":type":"snowflake-site/components/image"},"blog_component_19":{"id":"blog-title-a385565cb7","propertiesId":"snowflake-et-observe-complementaires-par-nature","type":"heading2","lines":["Snowflake et Observe, complémentaires par nature"],":type":"snowflake-site/components/blog/blog-title"},"blog_component_20":{"id":"blog-text-a439642a0f","text":"\u003Cp\u003EObserve fournit l’intelligence opérationnelle nécessaire pour résoudre les incidents en temps réel. 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