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14.0028L26.0428 18.6516H26.0654C26.4039 18.945 26.9117 18.9111 27.205 18.5726C27.4984 18.2341 27.4645 17.7264 27.126 17.433L21.9695 12.818L27.7466 6.49927Z\" fill=\"#249EDC\"/\u003E\r\n\u003C/svg\u003E\u003C/span\u003E\r\n\u003C/div\u003E","cssContent":".share-icon__svg *{pointer-events:none}.share-icon-group{display:flex;gap:24px;align-items:center}.share-icon{display:inline-block}.share-icon svg{height:32px}.share-icon__svg:hover path{fill:var(--ui-02);transition:300ms ease fill}.share-icon__svg path{transition:300ms ease fill}","jsContent":"const pageURL = window.location.href;\r\n\r\ndocument.addEventListener('click', (e) =\u003E {\r\n\r\nconst targetClassesArr = [...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-768a922f31","type":"heading2","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-9fe4650b2a","text":"\u003Cp\u003EAlors que les entreprises évoluent dans un monde toujours plus data-driven, elles ont plus que jamais besoin de solutions de stockage et d’analyse de données flexibles, évolutives et rentables. Les data lakes se sont imposés comme des pierres angulaires d’une infrastructure de données moderne, conçue pour gérer les données actuelles avec les volumes, la variété et la vitesse qui les caractérisent.\u003C/p\u003E\n\u003Cp\u003ECet article explique non seulement ce qu’est un data lake, mais détaille également l’architecture sous-jacente qui lui permet d’être efficace à grande échelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"data",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-de8baf2def","additionalClasses":"headline-decoration","type":"heading2","lines":["Qu’est-ce qu’un data lake ?"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-1c6095b9b0","text":"\u003Cp\u003EUn data lake est un référentiel centralisé qui permet aux entreprises de stocker tous types de données (structurées, semi-structurées et non structurées) dans leur format brut. Contrairement aux bases de données ou aux data warehouses traditionnels, les data lakes ne nécessitent pas de schémas ou de formatage prédéfinis avant le stockage. Ils sont donc particulièrement flexibles et agiles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-622754ac83","type":"heading4","lines":["Caractéristiques principales :"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-79f69b7e18","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ESchéma en lecture : \u003C/b\u003Eles données sont interprétées lorsqu’elles sont consultées, et non lorsqu’elles sont ingérées.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrise en charge de divers types de données :\u003C/b\u003E fichiers CSV, journaux \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/what-is-json/\"\u003EJSON\u003C/a\u003E, images, vidéos…\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrise en charge de plusieurs langages : \u003C/b\u003Enotamment SQL, Spark, etc.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EÉvolutivité et rentabilité : \u003C/b\u003Esolution conçue sur un stockage d’objets économique, souvent dans le cloud.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrise en charge d’un large éventail d’utilisateurs :\u003C/b\u003E data analysts, data scientists, data engineers…\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EPrise en charge de l’ensemble du cycle de vie des données : \u003C/b\u003Ede l’ingestion et du stockage, au traitement et à la transformation, et aux analyses avancées et à l’IA.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"is",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e531bc667b","additionalClasses":"headline-decoration","type":"heading2","lines":["Data lake vs. data warehouse"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-93e984acfc","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EUn \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Edata lake et un data warehouse\u003C/a\u003E sont tous deux utilisés pour stocker et analyser des données, mais ils diffèrent à des égards importants.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EStructure de données :\u003C/b\u003E un data lake stocke des données brutes non traitées sans structure prédéfinie. Il utilise une approche de « schéma en lecture », où les données ne sont organisées que lorsqu’elles sont accessibles pour analyse. Un \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Edata warehouse\u003C/a\u003E nécessite que les données soient nettoyées et transformées pour s'adapter à un schéma prédéfini avant stockage, un processus appelé « schéma en écriture ».\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETypes de données : \u003C/b\u003Eun data lake peut prendre en charge tous les types de données, y compris les données non structurées, semi-structurées et structurées. Les data warehouses traditionnels stockent principalement des données structurées prêtes à être analysées.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUtilisateurs et finalité :\u003C/b\u003E les data warehouses sont souvent optimisés pour la Business Intelligence (BI) et la création de rapports et sont utilisés par les business analysts. Les data lakes sont souvent utilisés par les data scientists et les data engineers pour des analyses avancées et le machine learning sur une plus grande variété de types de données.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EApproche hybride :\u003C/b\u003E de nombreuses entreprises combinent les deux systèmes dans une architecture de « lake house » pour bénéficier de la flexibilité d’un data lake et des performances d’un data warehouse.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1697459514":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"air",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-e90ade523e","additionalClasses":"headline-decoration","type":"heading2","lines":["Avantages d’un data lake"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-12cf115092","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EUn data lake offre de nombreux avantages aux entreprises qui cherchent à gérer et à analyser des données modernes à grande échelle.\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EStockage rentable : \u003C/b\u003Eles data lakes sont construits sur un stockage d’objets économique, souvent dans le cloud, ce qui se traduit par des coûts de stockage inférieurs à ceux des systèmes traditionnels. Il devient alors rentable pour stocker de grandes quantités de données sans structure prédéfinie.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EÉvolutivité pour le Big Data : \u003C/b\u003Eles data lakes sont conçus pour gérer le volume et la variété des données actuelles et peuvent prendre facilement en charge des pétaoctets de données. Ils sont hautement évolutifs et peuvent évoluer en fonction des besoins de votre entreprise.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFlexibilité pour plusieurs types de données : \u003C/b\u003Eun data lake peut ingérer n’importe quel type de données, de n’importe quelle source et à n’importe quelle vitesse, qu’elles soient \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Estructurées, semi-structurées ou non structurées\u003C/a\u003E. Cette approche « schéma en lecture » offre une flexibilité maximale pour l’exploration des données.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnalyse avancée et préparation à l’IA :\u003C/b\u003E un data lake sert de base flexible pour un large éventail de workloads analytiques. Il prend en charge les analyses avancées, le machine learning et les analyses en temps réel. La capacité à fournir des données brutes en grand volume est essentielle pour entraîner des modèles d’IA et de machine learning.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_1767623498":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"what",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-1ec2aff45d","additionalClasses":"headline-decoration","type":"heading2","lines":["Architecture d’un data lake"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-de82103f10","text":"\u003Cp\u003ESi le terme de data lake désigne le concept, l’architecture de data lake fait référence à la structure et aux composants sous-jacents qui permettent à un data lake de fonctionner efficacement. Il s’agit d’un système en couches conçu pour gérer l’ingestion, le stockage, le traitement, la découverte, la gouvernance et la consommation de jeux de données à grande échelle.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-36db2dbda1","type":"heading4","lines":["Principaux composants de l’architecture de data lake :"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-c1f3a4b97e","text":"\u003Ctable\u003E\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EFonctionnalité\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche d’ingestion\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EExtrait des données de diverses sources (streaming, batch, IoT, API).\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche de stockage\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EStocke des données brutes dans un stockage d’objets évolutif.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche de métadonnées et de catalogage\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EIndexe, étiquette et organise les données pour faciliter leur découverte.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche de traitement\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EGère \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Ela transformation des données\u003C/a\u003E via des outils de traitement par batch/en temps réel.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche d’accès\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EPermet les requêtes, l’exploration et l’analyse.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ECouche de gouvernance et de sécurité\u003C/b\u003E\u003C/p\u003E\n\u003C/td\u003E\n\u003Ctd\u003E\u003Cp\u003EGère la confidentialité des données, les contrôles d’accès, l’audit et la conformité.\u003C/p\u003E\n\u003C/td\u003E\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","richText":true,":type":"snowflake-site/components/text"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"columnClassNames":{"container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"rel",":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1520074195":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1202161416":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1835740287":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__746839706":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1923870193":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1477902138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"type",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7d0e45499d","type":"heading2","lines":["Types de données pris en charge"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-3de9ec6d37","text":"\u003Cp\u003EUn data lake est conçu pour stocker de grandes quantités de données dans leur format natif brut. Il prend donc en charge un large éventail de types de données, regroupés en trois grandes catégories :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-278258d786","type":"heading4","lines":["Données structurées"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1835740287":{"id":"text-b7a464d4c9","text":"\u003Cp\u003ELes données de ce type sont très organisées et rentrent parfaitement dans des lignes et des colonnes, comme une base de données relationnelle ou une feuille de calcul. Ce type de données a un schéma prédéfini, ce qui facilite la recherche, l’analyse et la gestion. Voici quelques exemples de types de données structurées :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-4d6cbdc7eb","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ETables de bases de données relationnelles : \u003C/b\u003Edonnées organisées avec des colonnes et des lignes fixes\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EFeuilles de calcul (par ex., fichiers CSV) :\u003C/b\u003E données tabulaires avec colonnes définies\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDonnées numériques :\u003C/b\u003E nombres entiers, nombres à virgule flottante, nombres décimaux\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDonnées catégorielles :\u003C/b\u003E étiquettes ou catégories avec un nombre limité de valeurs\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDonnées de dates et heures :\u003C/b\u003E horodatages, dates et heures\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1923870193":{"id":"title-v2-c84b689122","type":"heading4","lines":["Données semi-structurées"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1477902138":{"id":"text-2945cbc1a0","text":"\u003Cp\u003ECes données ne suivent pas une structure tabulaire rigide, mais possèdent certaines propriétés organisationnelles, ce qui les rend plus faciles à analyser que des données non structurées. Bien souvent, ces données contiennent des balises ou des marqueurs qui séparent les éléments sémantiques et imposent des hiérarchies. Voici quelques exemples :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy":{"id":"text-ea51144c29","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EJSON (JavaScript Object Notation) : \u003C/b\u003Eformat léger utilisant des paires clé-valeur et des objets imbriqués.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EXML (eXtensible Markup Language) : \u003C/b\u003Elangage de balisage qui définit un ensemble de règles pour coder des documents dans un format lisible par les humains et les machines.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ECSV (Comma Separated Values) avec structures complexes : \u003C/b\u003Esi les données CSV de base sont structurées, elles peuvent être considérées comme semi-structurées lorsqu’elles comportent des nombres variables de colonnes ou des données imbriquées dans certains champs.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers journaux : \u003C/b\u003Eils contiennent souvent des horodatages, des types d’événements et des messages, qui peuvent être analysés.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EBases de données NoSQL :\u003C/b\u003E documents ou magasins de paires clé-valeur où le schéma peut varier d’une entrée à l’autre.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EHTML (Hypertext Markup Language) :\u003C/b\u003E bien que principalement destinées aux pages web, ces données contiennent des éléments structurés.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EYAML (YAML Ain’t Markup Language™) :\u003C/b\u003E norme de sérialisation des données accessible à l’esprit humain et adaptée à tous les langages de programmation.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1520074195":{"id":"title-v2-0c27375a0d","type":"heading4","lines":["Données non structurées"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__746839706":{"id":"text-d2ee86c97f","text":"\u003Cp\u003ECes données n’ont pas de format ou d’organisation prédéfinis, ce qui les rend difficiles à analyser avec des méthodes traditionnelles. Pour en extraire des informations, il est généralement nécessaire de recourir à des outils et des techniques spécialisés comme le traitement du langage naturel ou le machine learning. Voici quelques exemples de types de données non structurées :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy_":{"id":"text-7d34078782","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers de texte :\u003C/b\u003E documents (.txt, .doc, .pdf), e-mails, publications sur les réseaux sociaux\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers d’image :\u003C/b\u003E JPEG, PNG, GIF\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers audio : \u003C/b\u003EMP3, WAV\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers vidéo :\u003C/b\u003E MP4, AVI, MOV\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDonnées de capteurs :\u003C/b\u003E flux de données provenant d’appareils IoT, parfois sans structure homogène\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFichiers binaires :\u003C/b\u003E fichiers exécutables, formats de données propriétaires\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1202161416":{"id":"text-404b380108","text":"\u003Cp\u003EUn data lake a pour caractéristique et avantage clé d’être capable de stocker et traiter tous ces types de données dans leur format natif. Cette approche de « schéma en lecture » offre de la flexibilité et permet aux data scientists et aux analystes d’explorer et d’analyser des données de diverses manières, sans subir les contraintes d’une structure prédéfinie.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","title_v2_copy_copy_c","text_copy_860073138__1835740287","text_copy_copy","title_v2_copy_copy_c_1923870193","text_copy_860073138__1477902138","text_copy_copy_copy","title_v2_copy_copy_c_1520074195","text_copy_860073138__746839706","text_copy_copy_copy_","text_copy_860073138__1202161416"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"}},":itemsOrder":["container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_981691511":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"container-24a7fdf87b",":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1340528379":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"life",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-642bfe6aad","type":"heading2","lines":["Cycle de vie des données"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-c9d0dd623d","text":"\u003Cp\u003EAu sein d’un data lake, le cycle de vie des données correspond aux étapes par lesquelles les données passent, de leur création ou acquisition initiale à leur archivage ou suppression finale. Il s’agit d’un processus continu qui contribue à garantir que les données sont gérées, utilisées et gouvernées efficacement tout au long de leur existence dans le data lake. Voici un aperçu du cycle de vie type des données dans un data lake, même si chaque mise en œuvre peut varier :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1340528379":{"id":"text-f1b10ebcac","text":"\u003Cp\u003E\u003Cb\u003E1. Ingestion des données : \u003C/b\u003Eil s’agit de l’étape initiale au cours de laquelle les données provenant de divers systèmes sources sont intégrées dans le data lake. Ces sources peuvent être variées : bases de données structurées, journaux semi-structurés, documents non structurés, flux de données provenant d’appareils IoT, fils d’actualités sur les réseaux sociaux, etc. L’ingestion dans un data lake suit souvent une approche caractéristique selon laquelle les données sont ingérées « telles quelles », ce qui signifie qu’elles sont généralement chargées dans leur format natif brut sans transformation initiale significative ni définition de schéma. Cette approche préserve une flexibilité maximale pour les analyses futures. Les outils et processus utilisés à cette étape incluent notamment le chargement par batch, l’ingestion de flux en temps réel et les connecteurs de données.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2. Stockage et persistance des données :\u003C/b\u003E une fois ingérées, les données brutes sont stockées dans le data lake. L’architecture s’appuie souvent sur des systèmes de stockage distribués tels que Hadoop Distributed File System (HDFS) ou un stockage d’objets basé sur le cloud (par ex., Amazon S3, Azure Data Lake Storage, Google Cloud Storage). Les données conservent leur format d’origine, afin de permettre des approches analytiques diverses. La couche de stockage doit être évolutive et économique pour gérer les volumes de données parfois considérables dans un data lake. Différents niveaux de stockage peuvent être utilisés en fonction de la fréquence d’accès aux données et des politiques de conservation.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3. Traitement et transformation des données : \u003C/b\u003Ecette étape consiste à préparer les données brutes en vue de leur analyse et de leur utilisation. En fonction du cas d’usage analytique voulu, les données peuvent subir diverses transformations (nettoyage, filtrage, jonction, agrégation, enrichissement, etc.). C’est là qu’entre en jeu le « schéma en lecture » : ce schéma est appliqué lorsque les données sont traitées dans un but spécifique, plutôt qu’au moment de l’ingestion. Divers moteurs et frameworks de traitement sont utilisés à cette étape, tels que Spark, Hadoop MapReduce, des outils de data warehousing connectés au data lake ou encore des services de calcul serverless.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E4. Exploration et analyse de données : \u003C/b\u003Ec’est à cette étape que les data scientists, les analystes et les utilisateurs métiers explorent les données traitées pour découvrir des tendances, extraire des informations et répondre à des questions stratégiques. Ils peuvent utiliser divers outils et diverses techniques, notamment des requêtes de type SQL, des outils de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-visualization/\"\u003Evisualisation des données\u003C/a\u003E, des packages d’analyse statistique et des algorithmes de machine learning. Grâce à sa flexibilité, le data lake permet d’appliquer diverses approches analytiques aux mêmes données, selon les besoins.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E5. Consommation de données et actions : \u003C/b\u003Eles informations et les données traitées sont ensuite exploitées par diverses applications et divers utilisateurs en aval, pour générer des rapports et des tableaux de bord, fournir des données à des systèmes opérationnels, alimenter des applications en temps réel ou prendre des décisions éclairées. Les données peuvent se présenter dans différents formats et être accessibles via différentes interfaces, en fonction de l’application qui les utilise.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E6. Gouvernance et sécurité des données :\u003C/b\u003E tout au long du cycle de vie des données, \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/data-governance/\"\u003Ela gouvernance\u003C/a\u003E et la sécurité sont essentielles. Pour les garantir, il faut définir et appliquer des politiques relatives à la \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/a-practical-guide-to-data-quality/\"\u003Equalité\u003C/a\u003E, à la traçabilité et au \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Emasquage des données\u003C/a\u003E, à la gestion des métadonnées, aux contrôles d’accès, ainsi qu’à la conformité aux réglementations. Une gouvernance efficace contribue à s’assurer que les données dans le data lake sont fiables, sécurisées et correctement gérées.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E7. Archivage et purge des données : \u003C/b\u003Eà mesure que les données vieillissent et que leur valeur stratégique diminue, il peut être nécessaire de les archiver pour des raisons de conformité ou pour optimiser les coûts de stockage. À terme, les données devenues inutiles peuvent être purgées, dans le respect des politiques de conservation définies. Cette dernière étape permet de maintenir l’efficacité et la conformité du data lake au fil du temps.\u003C/p\u003E\r\n\u003Cp\u003EEn résumé, le cycle de vie des données dans un data lake est conçu pour être flexible et adaptable, afin de permettre aux entreprises d’ingérer de grandes quantités de données diverses, de les traiter selon les besoins pour des cas d’usage analytiques spécifiques et d’en extraire des informations précieuses tout en assurant une gouvernance et une sécurité appropriées. Le principe du « schéma en lecture » et la séparation du stockage et du calcul sont des caractéristiques clés qui distinguent un data lake des approches traditionnelles de data warehousing.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","text_copy_860073138__1340528379"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"flexible_column_cont":{"id":"flexible-column-container-7a02dc60ee","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-a524e19a36",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"card_v2_copy":{"id":"card-v2-111297b7bf","modalId":"video","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Elysium Analytics améliore son data lake de sécurité sémantique avec les Snowflake Native Apps"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=rznH2KyV6MY"},"linkTargetContentType":"GENERIC",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_EXTERNAL","text":"Regarder la vidéo"},"image":{"id":"image","alt":"elysium logo","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--a62365e1-1fbd-4c9e-9dd1-7e7f8d1b749b/elysium%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-10e9ad326f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["container_copy_copy_","flexible_column_cont"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"container_copy_copy__1663285187":{"columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","text_copy_1241045613_1885071390":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"why",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-a35e245aa3","additionalClasses":"headline-decoration","type":"heading2","lines":["L’importance des data lakes"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-6956893145","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EQu’on s’intéresse au concept ou à l’architecture, la valeur des data lakes réside dans leur capacité à répondre aux besoins data modernes. Voici les principaux avantages des data lakes : \u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EÉvolutivité :\u003C/b\u003E prise en charge simple de pétaoctets de données\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EFlexibilité : \u003C/b\u003Eingestion de tous les types de données, provenant de n’importe quelle source, à n’importe quelle vitesse\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EÉconomies :\u003C/b\u003E réduction des coûts de stockage par rapport aux systèmes traditionnels\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EAnalyse avancée : \u003C/b\u003Esocle pour l’IA, le machine learning et l’analyse en temps réel\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EDémocratisation des données :\u003C/b\u003E accès étendu aux données pour les équipes techniques et non techniques\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-592bc4b15f","type":"heading4","lines":["Évolution vers un data lake moderne"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment snowflake-responsive-component-top-padding-extra-small"},"text_copy_1241045613_1885071390":{"id":"text-bc8cf6ad3a","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELes data lakes traditionnels évoluent vers des architectures de data lake modernes, souvent appelées « lakehouses », des systèmes hybrides qui combinent le stockage flexible des data lakes avec l’interrogation structurée et les performances des data warehouses.\u003C/p\u003E\n\u003Cp\u003E\u003Cb\u003ETendances autour de l’architecture de data lake moderne :\u003C/b\u003E\u003Cbr\u003E\n \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EDéploiement 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aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"use",":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-42819920a0","additionalClasses":"headline-decoration","type":"heading2","lines":["Limites et cas d’usage des data lakes"],":type":"snowflake-site/components/title-v2"},"title_v2_copy_copy_c":{"id":"title-v2-6f9d48beeb","type":"heading4","lines":["Limites des data lakes"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy_1241045613_1885071390":{"id":"text-e06547664a","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EDécouvrabilité des données : \u003C/b\u003Een raison de l’absence de catalogage et de gestion des métadonnées, il est difficile pour les utilisateurs de localiser et de comprendre les données dont ils ont besoin.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003ESécurité et gouvernance : \u003C/b\u003Epour assurer la conformité et protéger les informations sensibles dans un data lake, il faut mettre en place des mesures de sécurité et de gouvernance efficaces, ce qui peut s’avérer difficile face à l’immensité des data lakes et à la diversité des sources de données.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EComplexité d’intégration : \u003C/b\u003Een raison de la diversité et bien souvent de l’absence de structure des sources de données, sans parler de l’ampleur des data lakes, il est nécessaire d’utiliser des outils sophistiqués et modernes pour éviter de créer un « marécage de données » ingérable et inaccessible.\u003C/li\u003E\n\u003C/ul\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_422171653":{"id":"title-v2-7fa34ca9a6","type":"heading4","lines":["Cas 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