{"id":212561,"date":"2023-09-25T07:30:00","date_gmt":"2023-09-25T07:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=212561"},"modified":"2024-01-15T13:30:01","modified_gmt":"2024-01-15T13:30:01","slug":"data-lake-vs-data-warehouse","status":"publish","type":"post","link":"https:\/\/datascientest.com\/data-lake-vs-data-warehouse","title":{"rendered":"Data Lake vs Data Warehouse : Quelles diff\u00e9rences ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"212561\" class=\"elementor elementor-212561\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7263538 e-flex e-con-boxed e-con\" data-id=\"7263538\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-115466a elementor-widget elementor-widget-heading\" data-id=\"115466a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Dans le domaine du num\u00e9rique, les Data Lakes et Data Warehouses sont deux solutions largement utilis\u00e9es pour stocker des donn\u00e9es. Pour autant, leurs avantages et cas d\u2019utilisations sont souvent confondus. Savoir les distinguer est donc essentiel, car ils r\u00e9pondent \u00e0 des objectifs diff\u00e9rents qui n\u00e9cessitent des ressources et des comp\u00e9tences qui le sont tout autant. Un data lake peut convenir \u00e0 une entreprise, l\u00e0 o\u00f9 une data warehouse sera plus adapt\u00e9e pour une autre. Dans cet article, vous apprendrez \u00e0 faire la diff\u00e9rence entre ces deux termes, quels sont leurs avantages et dans quoi ils excellent. Ainsi, il sera plus simple pour vous de faire votre choix.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f02b9d1 e-flex e-con-boxed e-con\" data-id=\"f02b9d1\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-551fa46 elementor-widget elementor-widget-heading\" data-id=\"551fa46\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Qu\u2019est-ce qu\u2019un Data Lake ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-19479f5 elementor-widget elementor-widget-text-editor\" data-id=\"19479f5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">Un <a href=\"https:\/\/datascientest.com\/data-lake-tout-savoir\"><span style=\"text-decoration: underline;\"><strong>Data Lake, ou lac de donn\u00e9es<\/strong><\/span><\/a>, est <\/span><b>un d\u00e9p\u00f4t de stockage<\/b><span style=\"font-weight: 400;\"> qui permet de stocker de larges quantit\u00e9s de donn\u00e9es structur\u00e9es, non structur\u00e9es ou semi-structur\u00e9es. Il est possible d\u2019y entreposer <\/span><b>tous les diff\u00e9rents types de donn\u00e9es dans leur format natif<\/b><span style=\"font-weight: 400;\">. Comme dans un v\u00e9ritable lac, les donn\u00e9es d\u00e9coulent de diff\u00e9rentes sources en temps r\u00e9el.<\/span><\/p><p><span style=\"font-weight: 400;\">Ce type de plateforme ne pose aucune contrainte en ce qui concerne la taille ou la cat\u00e9gorie de fichier. Elle permet l\u2019analyse de donn\u00e9es \u00e0 haute performance et l\u2019int\u00e9gration native.<\/span><\/p><p><span style=\"font-weight: 400;\">Diff\u00e9rents types d\u2019analyse de donn\u00e9es peuvent \u00eatre effectu\u00e9s, comme le traitement Big Data, l\u2019analyse en temps r\u00e9el, <\/span><span style=\"font-weight: 400;\">le <a href=\"https:\/\/datascientest.com\/machine-learning-tout-savoir\"><span style=\"text-decoration: underline;\"><strong>Machine Learning<\/strong><\/span><\/a> ou encore la production de tableaux de bord et de visualisations de donn\u00e9es.<\/span><\/p><p><span style=\"font-weight: 400;\">Au sein du lac de donn\u00e9es, <\/span><b>chaque \u00e9l\u00e9ment de donn\u00e9e re\u00e7oit un identifiant unique<\/b><span style=\"font-weight: 400;\">. Chaque donn\u00e9e est associ\u00e9e \u00e0 un ensemble de m\u00e9tadonn\u00e9es. L\u2019architecture n\u2019est pas hi\u00e9rarchique, contrairement \u00e0 celle d\u2019une Data Warehouse.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7538c2d elementor-widget elementor-widget-image\" data-id=\"7538c2d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"860\" height=\"492\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-lake.jpg\" class=\"attachment-full size-full wp-image-212567\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-lake.jpg 860w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-lake-300x172.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-lake-768x439.jpg 768w\" sizes=\"(max-width: 860px) 100vw, 860px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c59f639 e-flex e-con-boxed e-con\" data-id=\"c59f639\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d139601 elementor-widget elementor-widget-heading\" data-id=\"d139601\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Qu\u2019est-ce qu\u2019une Data Warehouse ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d8ab7f3 elementor-widget elementor-widget-text-editor\" data-id=\"d8ab7f3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Un <a href=\"https:\/\/datascientest.com\/data-warehouse\"><span style=\"text-decoration: underline;\"><strong>Data Warehouse, ou entrep\u00f4t de donn\u00e9es<\/strong><\/span><\/a>, est <\/span><b>une plateforme<\/b><span style=\"font-weight: 400;\"> utilis\u00e9e pour collecter et analyser des donn\u00e9es en provenance de multiples sources h\u00e9t\u00e9rog\u00e8nes. Elle occupe une place centrale au sein d&rsquo;un <a href=\"https:\/\/datascientest.com\/business-intelligence\"><span style=\"text-decoration: underline;\"><strong>syst\u00e8me de Business Intelligence<\/strong><\/span><\/a>.<\/span><\/p><p><span style=\"font-weight: 400;\">Cette plateforme marie plusieurs technologies et composants permettant d\u2019exploiter la donn\u00e9e. Elle permet <\/span><b>le stockage d\u2019un large volume de donn\u00e9es<\/b><span style=\"font-weight: 400;\">, mais aussi la requ\u00eate et l\u2019analyse. L\u2019objectif est de transformer les donn\u00e9es brutes en informations utiles, et de les rendre disponibles et accessibles aux utilisateurs.<\/span><\/p><p><span style=\"font-weight: 400;\">Une Data Warehouse est g\u00e9n\u00e9ralement s\u00e9par\u00e9e de la base de donn\u00e9es op\u00e9rationnelle d\u2019une entreprise. Il permet aux utilisateurs de <\/span><b>s\u2019appuyer sur les donn\u00e9es historiques et actuelles<\/b><span style=\"font-weight: 400;\"> pour prendre de meilleures d\u00e9cisions.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-44377aa elementor-widget elementor-widget-image\" data-id=\"44377aa\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"769\" height=\"430\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-warehouse.jpg\" class=\"attachment-full size-full wp-image-212571\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-warehouse.jpg 769w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-warehouse-300x168.jpg 300w\" sizes=\"(max-width: 769px) 100vw, 769px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba51c8c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"ba51c8c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-analyst\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Se former \u00e0 l'analyse de donn\u00e9es<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e616bf4 e-flex e-con-boxed e-con\" data-id=\"e616bf4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4c9b23a elementor-widget elementor-widget-heading\" data-id=\"4c9b23a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelles sont les diff\u00e9rences entre ces deux solutions ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc66ae4 elementor-widget elementor-widget-text-editor\" data-id=\"cc66ae4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Si le Data Lake et la Data Warehouse sont similaires parce qu\u2019ils sont des solutions de stockage, ils sont pourtant <\/span><b>tr\u00e8s diff\u00e9rents sur plusieurs points<\/b><span style=\"font-weight: 400;\"> : <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6245acb elementor-widget elementor-widget-heading\" data-id=\"6245acb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">L\u2019usage de chacun<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ed1abf elementor-widget elementor-widget-text-editor\" data-id=\"8ed1abf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">En premier lieu, ces deux solutions sont utilis\u00e9es dans <\/span><b>des domaines diff\u00e9rents<\/b><span style=\"font-weight: 400;\">. On retrouve princ<\/span>ipalement des Data Lake dans le domaine de la sant\u00e9, de l\u2019\u00e9ducation, des transports ou encore en <a href=\"https:\/\/datascientest.com\/intelligence-artificielle-definition\"><span style=\"text-decoration: underline;\">Intelligence artificielle<\/span><\/a>.<\/p><p><span style=\"font-weight: 400;\">Dans ces domaines, le Data Lake est tr\u00e8s utile pour <\/span><b>sa capacit\u00e9 \u00e0 stocker et analyser<\/b><span style=\"font-weight: 400;\"> des quantit\u00e9s massives de donn\u00e9es qui proviennent de diff\u00e9rentes sources, sans \u00eatre structur\u00e9es.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Du c\u00f4t\u00e9 de la Data Warehouse, on l\u2019utilise beaucoup dans les secteurs de<\/span><b> la finance, de l\u2019aviation ou du secteur public<\/b><span style=\"font-weight: 400;\">. Ces domaines g\u00e9n\u00e8rent chaque jour des milliers de donn\u00e9es r\u00e9parties dans diff\u00e9rentes structures ou architectures, <\/span><b>un mode de stockage plus adapt\u00e9<\/b><span style=\"font-weight: 400;\"> pour la Data Warehouse. La prise de d\u00e9cision est ainsi facilit\u00e9e gr\u00e2ce \u00e0 la Data Warehouse, elle permet de trier efficacement les donn\u00e9es et de les rendre plus exploitables. Cette technologi<\/span>e est notamment exploit\u00e9e en machine learning, contrairement au data lake qui est plus optimal pour du <a href=\"https:\/\/datascientest.com\/deep-learning-definition\"><span style=\"text-decoration: underline;\">Deep Learning<\/span><\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1475120 elementor-widget elementor-widget-heading\" data-id=\"1475120\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">L\u2019exploitation des donn\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb7904b elementor-widget elementor-widget-text-editor\" data-id=\"eb7904b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Dans une Data Warehouse, <\/span><b>le stockage des donn\u00e9es est pour un but sp\u00e9cifique<\/b><span style=\"font-weight: 400;\">, un projet ou l&rsquo;entra\u00eenement d\u2019un mod\u00e8le. Chaque donn\u00e9e \u00e0 son importance et servira \u00e0 d\u00e9finir le r\u00e9sultat du projet.<\/span><\/p><p><span style=\"font-weight: 400;\">Les informations stock\u00e9es dans un Data Lake ne sont pas toujours destin\u00e9es \u00e0 \u00eatre utilis\u00e9es pour un but sp\u00e9cifique. <\/span><b>Elles pourront servir dans un futur proche<\/b><span style=\"font-weight: 400;\"> et constituent souven<\/span>t une <a href=\"https:\/\/datascientest.com\/base-de-donnees-definition\"><span style=\"text-decoration: underline;\">base de donn\u00e9es cons\u00e9cutive<\/span><\/a>, d<span style=\"font-weight: 400;\">isponible quand le besoin se fait sentir.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f80e1fe elementor-widget elementor-widget-heading\" data-id=\"f80e1fe\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">L\u2019acc\u00e8s aux donn\u00e9es stock\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-50e2dad elementor-widget elementor-widget-text-editor\" data-id=\"50e2dad\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Lorsqu&rsquo;on souhaite acc\u00e9der aux donn\u00e9es d\u2019un Data Lake, il est tr\u00e8s simple d&rsquo;extraire les donn\u00e9es ou de les mod<\/span>ifier. <a href=\"https:\/\/datascientest.com\/metier-data-scientist\"><span style=\"text-decoration: underline;\">Les Data Scientists qui les manipulent<\/span><\/a> n\u2019o<span style=\"font-weight: 400;\">nt que tr\u00e8s peu de restrictions.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">En revanche, les Data Warehouse sont des espaces de stockage complexes o\u00f9 toutes les modifications ne sont pas permises. Malgr\u00e9 un entreposage et un traitement des donn\u00e9es efficace, <\/span><b>la modification des donn\u00e9es n\u00e9cessite des ressources tr\u00e8s co\u00fbteuses<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0e7ea4 elementor-widget elementor-widget-heading\" data-id=\"e0e7ea4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\">Les technologies utilis\u00e9es<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87ec0c2 elementor-widget elementor-widget-text-editor\" data-id=\"87ec0c2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">On pourrait penser que comme ces deux solutions sont des unit\u00e9s de stockage, elles utilisent la m\u00eame technologie, et bien non. Pour construire un Data Lake et le traiter, les gestionnaires de donn\u00e9es se tournent principalement vers<\/span> la<b>\u00a0<\/b><a href=\"https:\/\/datascientest.com\/hadoop\" target=\"_blank\" rel=\"noopener\">solution sur mesure Hadoop<\/a><span style=\"font-weight: 400;\">. Avec ses offres Kafka, Spark-Streaming et Storm, elle permet aux Data Scientists de traiter les donn\u00e9es avant leur introduction dans le Data Lake. Le NoSQL et <\/span><span style=\"font-weight: 400;\">le<\/span>s <a href=\"https:\/\/datascientest.com\/cloud-computing\"><span style=\"text-decoration: underline;\">solutions cloud<\/span><\/a> t<span style=\"font-weight: 400;\">elles que Google Cloud Platform ou Amazon Web Services font \u00e9galement partie de la liste des technologies permettant de g\u00e9rer les Data Lakes.<\/span><\/p><p><span style=\"font-weight: 400;\">La gestion d&rsquo;un Data Warehouse peut se faire gr\u00e2ce \u00e0 <\/span><b>de nombreuses solutions propri\u00e9taires<\/b><span style=\"font-weight: 400;\"> o<\/span>u <a href=\"https:\/\/datascientest.com\/open-source\"><span style=\"text-decoration: underline;\">open source<\/span><\/a>\u00a0: Ab Initio Software, <a href=\"https:\/\/datascientest.com\/connaissez-vous-amazon-redshift\">Amazon Redshift<\/a>, AnalytiX DS, CodeFutures. Ces technologies sont<span style=\"font-weight: 400;\"> essentiellement bas\u00e9es sur le Cloud et sur le langage SQL.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6fa585b elementor-widget elementor-widget-image\" data-id=\"6fa585b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"840\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis.jpg\" class=\"attachment-full size-full wp-image-212578\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis.jpg 840w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-300x179.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-768x457.jpg 768w\" sizes=\"(max-width: 840px) 100vw, 840px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1524bd3 e-flex e-con-boxed e-con\" data-id=\"1524bd3\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a047e5b elementor-widget elementor-widget-heading\" data-id=\"a047e5b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelle solution est la meilleure ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a88ada elementor-widget elementor-widget-text-editor\" data-id=\"5a88ada\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Le choix entre un Data Lake et une Data Warehouse <\/span><b>d\u00e9pend des besoins sp\u00e9cifiques de votre entreprise<\/b><span style=\"font-weight: 400;\">. Si votre entreprise souhaite explorer des donn\u00e9es vari\u00e9es, non structur\u00e9es et en constante \u00e9volution, un Data Lake pourrait \u00eatre la meilleure option. En revanche, si votre priorit\u00e9 est d&rsquo;obtenir des analyses rapides et pr\u00e9cises \u00e0 partir de donn\u00e9es structur\u00e9es, une Data Warehouse serait plus appropri\u00e9e.<\/span><\/p><p><span style=\"font-weight: 400;\">En r\u00e9alit\u00e9, de nombreuses entreprises adoptent <\/span><b>une approche hybride<\/b><span style=\"font-weight: 400;\">, et utilisent \u00e0 la fois des Data Lakes et des Data Warehouses pour tirer parti des avantages des deux solutions. Une autre tendance \u00e9merge depuis quelques ann\u00e9es, <\/span><b>l\u2019utilisation de Data Lakehouse<\/b><span style=\"font-weight: 400;\">, qui vise \u00e0 combiner le Data Lake avec les capacit\u00e9s de gestion des donn\u00e9es d&rsquo;une Data Warehouse.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e92a029 elementor-widget elementor-widget-image\" data-id=\"e92a029\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"803\" height=\"500\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-1.jpg\" class=\"attachment-full size-full wp-image-212582\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-1.jpg 803w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-1-300x187.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-analysis-1-768x478.jpg 768w\" sizes=\"(max-width: 803px) 100vw, 803px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-79925e7 e-flex e-con-boxed e-con\" data-id=\"79925e7\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8c33d8b elementor-widget elementor-widget-text-editor\" data-id=\"8c33d8b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Vous savez d\u00e9sormais faire la diff\u00e9rence entre un Data Lake et une Data Warehouse, et aussi choisir lequel est le mieux pour votre projet data.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a12849e elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a12849e\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formations-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">En savoir plus sur nos formations Data <\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c150154 e-flex e-con-boxed e-con\" data-id=\"c150154\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2bfc107 elementor-widget elementor-widget-html\" data-id=\"2bfc107\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu\u2019est-ce qu\u2019un Data Lake ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Un Data Lake, ou lac de donn\u00e9es, est un d\u00e9p\u00f4t de stockage qui permet de stocker de larges quantit\u00e9s de donn\u00e9es structur\u00e9es, non structur\u00e9es ou semi-structur\u00e9es. Il est possible d\u2019y entreposer tous les diff\u00e9rents types de donn\u00e9es dans leur format natif. Comme dans un v\u00e9ritable lac, les donn\u00e9es d\u00e9coulent de diff\u00e9rentes sources en temps r\u00e9el.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu\u2019est-ce qu\u2019une Data Warehouse ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Un Data Warehouse, ou entrep\u00f4t de donn\u00e9es, est une plateforme utilis\u00e9e pour collecter et analyser des donn\u00e9es en provenance de multiples sources h\u00e9t\u00e9rog\u00e8nes. Elle occupe une place centrale au sein d'un syst\u00e8me de Business Intelligence.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quelles sont les diff\u00e9rences entre ces deux solutions ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"En premier lieu, ces deux solutions sont utilis\u00e9es dans des domaines diff\u00e9rents. On retrouve principalement des Data Lake dans le domaine de la sant\u00e9, de l\u2019\u00e9ducation, des transports ou encore en Intelligence artificielle. Dans ces domaines, le Data Lake est tr\u00e8s utile pour sa capacit\u00e9 \u00e0 stocker et analyser des quantit\u00e9s massives de donn\u00e9es qui proviennent de diff\u00e9rentes sources, sans \u00eatre structur\u00e9es.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quelle solution est la meilleure ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le choix entre un Data Lake et une Data Warehouse d\u00e9pend des besoins sp\u00e9cifiques de votre entreprise. Si votre entreprise souhaite explorer des donn\u00e9es vari\u00e9es, non structur\u00e9es et en constante \u00e9volution, un Data Lake pourrait \u00eatre la meilleure option. En revanche, si votre priorit\u00e9 est d'obtenir des analyses rapides et pr\u00e9cises \u00e0 partir de donn\u00e9es structur\u00e9es, une Data Warehouse serait plus appropri\u00e9e.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Dans le domaine du num\u00e9rique, les Data Lakes et Data Warehouses sont deux solutions largement utilis\u00e9es pour stocker des donn\u00e9es. Pour autant, leurs avantages et cas d\u2019utilisations sont souvent confondus. Savoir les distinguer est donc essentiel, car ils r\u00e9pondent \u00e0 des objectifs diff\u00e9rents qui n\u00e9cessitent des ressources et des comp\u00e9tences qui le sont tout autant. [&hellip;]<\/p>\n","protected":false},"author":79,"featured_media":212563,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2411],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Lake vs Data Warehouse : Quelles diff\u00e9rences ?<\/title>\n<meta name=\"description\" content=\"Dans cet article, nous vous apportons les d\u00e9tails sur les principales diff\u00e9rences entre Data Lake et Data Warehouse et lequel il faut choisir.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/data-lake-vs-data-warehouse\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Lake vs Data Warehouse : Quelles diff\u00e9rences ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/data-lake-vs-data-warehouse\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2023-09-25T07:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-15T13:30:01+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/09\/data-lake-vs-data-warehouse.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Dan Cohen\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dan Cohen\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/data-lake-vs-data-warehouse\",\"url\":\"https:\/\/datascientest.com\/data-lake-vs-data-warehouse\",\"name\":\"Data Lake vs Data Warehouse : Quelles diff\u00e9rences ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2023-09-25T07:30:00+00:00\",\"dateModified\":\"2024-01-15T13:30:01+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/61997d589648bc1d367f632ba686e354\"},\"description\":\"Dans cet article, nous vous apportons les d\u00e9tails sur les principales diff\u00e9rences entre Data Lake et Data Warehouse et lequel il faut choisir.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/data-lake-vs-data-warehouse\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/61997d589648bc1d367f632ba686e354\",\"name\":\"Dan Cohen\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/dancohen.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/dancohen.jpeg\",\"caption\":\"Dan Cohen\"},\"description\":\"Dan Cohen est actuellement Data Engineer chez DataScientest. 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