{"id":139061,"date":"2021-01-18T10:26:00","date_gmt":"2021-01-18T09:26:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=139061"},"modified":"2024-04-15T11:46:06","modified_gmt":"2024-04-15T11:46:06","slug":"formation-hadoop-spark","status":"publish","type":"post","link":"https:\/\/datascientest.com\/formation-hadoop-spark","title":{"rendered":"Formation Hadoop Spark : comment apprendre \u00e0 manier les outils Big Data ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"139061\" class=\"elementor elementor-139061\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6e16ad8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6e16ad8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-644693d\" data-id=\"644693d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7f2b966 elementor-widget elementor-widget-heading\" data-id=\"7f2b966\" 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\">Une formation Hadoop et Spark vous permettra de devenir un professionnel de la Data Science. D\u00e9couvrez pourquoi et comment ma\u00eetriser ces outils de traitement du Big Data.\nLe traitement du Big Data requiert de nouveaux outils, capables de traiter de vastes volumes de donn\u00e9es. Parmi les principaux logiciels utilis\u00e9s par les Data Scientists ou les Data Engineers, on peut citer Hadoop et Spark.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-06fb9e8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"06fb9e8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-215cad2\" data-id=\"215cad2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1ccc4d7 elementor-widget elementor-widget-heading\" data-id=\"1ccc4d7\" 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'est-ce que Apache Hadoop ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e39cdfb elementor-widget elementor-widget-text-editor\" data-id=\"e39cdfb\" 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;\"><strong><a href=\"https:\/\/datascientest.com\/hadoop\">Apache Hadoop<\/a> est un framework open source, utilis\u00e9 pour stocker et traiter de larges ensembles de donn\u00e9es<\/strong>. Il permet d&rsquo;analyser les donn\u00e9es en parall\u00e8le sur un cluster de multiples ordinateurs, plut\u00f4t que sur une seule machine. Ceci permet un important gain de vitesse.<\/span><\/p><p><span style=\"font-weight: 400;\">Quatre principaux modules composent Hadoop. Le HDFS (<strong>Hadoop Distributed File System<\/strong>) est un syst\u00e8me de fichier distribu\u00e9 pouvant \u00eatre ex\u00e9cut\u00e9 sur du hardware standard ou bas de gamme. Il offre de <strong>meilleures performances<\/strong> et <strong>une tol\u00e9rance accrue aux erreurs par rapport aux syst\u00e8mes de fichiers classiques<\/strong>.<\/span><\/p><p><span style=\"font-weight: 400;\">Le YARN (Yet Another Resource Negotiator) permet de <strong>g\u00e9rer et de surveiller les noeuds de clusters et l&rsquo;usage des ressources<\/strong>. Il sert aussi \u00e0 planifier les t\u00e2ches et les jobs.<\/span><\/p><p><span style=\"font-weight: 400;\">Le framework MapReduce aide les programmes \u00e0 effectuer des calculs parall\u00e8les sur les donn\u00e9es. Enfin, Hadoop Common fournit des <strong>biblioth\u00e8ques Java communes<\/strong> pouvant \u00eatre utilis\u00e9es avec tous les modules.<\/span><\/p><p><span style=\"font-weight: 400;\">Gr\u00e2ce \u00e0 Hadoop, il est plus facile d&rsquo;utiliser toute <strong>la capacit\u00e9 de stockage et de traitement des serveurs en cluster <\/strong>et d&rsquo;ex\u00e9cuter des traitements distribu\u00e9s sur de larges volumes de donn\u00e9es. Ce framework fournit les <strong>blocs de constructions <\/strong>sur lesquels sont construits les applications et services.<\/span><\/p><p><span style=\"font-weight: 400;\">Les donn\u00e9es en provenance de diff\u00e9rentes sources et dans divers formats peuvent \u00eatre transf\u00e9r\u00e9es vers Hadoop en utilisant une API pour se connecter \u00e0 <strong>NameNode<\/strong>. Des morceaux de chaque fichier sont r\u00e9pliqu\u00e9s sur des <strong>DataNodes<\/strong>. On utilise ensuite MapReduce sur les donn\u00e9es r\u00e9parties entre les DataNodes.<\/span><\/p><p><span style=\"font-weight: 400;\">Au fil des ann\u00e9es, l&rsquo;\u00e9cosyst\u00e8me Hadoop s&rsquo;est d\u00e9velopp\u00e9 et <strong>regroupe d\u00e9sormais de nombreux outils et applications d\u00e9di\u00e9s au Big Data<\/strong>. On peut citer le moteur SQL Presto, l&rsquo;interface analytique Hive, la base de donn\u00e9es non relationnelles HBase, le notebook interactif Zeppelin, ou encore le syst\u00e8me de traitement distribu\u00e9 <strong>Apache Spark<\/strong>.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3b59e08 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3b59e08\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-313294a\" data-id=\"313294a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-34bcc8e elementor-widget elementor-widget-heading\" data-id=\"34bcc8e\" 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'est-ce que Apache Spark ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14237db elementor-widget elementor-widget-text-editor\" data-id=\"14237db\" 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;\"><strong><a href=\"https:\/\/datascientest.com\/apache-spark\">Apache Spark<\/a> est un syst\u00e8me de traitement distribu\u00e9 utilis\u00e9 pour les workloads de Big Data<\/strong>. Il utilise le caching in-memory et l&rsquo;ex\u00e9cution de requ\u00eate optimis\u00e9e pour permettre des requ\u00eates rapides sur des donn\u00e9es de n&rsquo;importe quelle taille. Pour faire simple, il s&rsquo;agit d&rsquo;<strong>un moteur rapide pour le traitement Big Data<\/strong>.<\/span><\/p><p><span style=\"font-weight: 400;\">Il offre de meilleures performances que les pr\u00e9c\u00e9dents outils Big Data tels que MapReduce. Son secret est qu&rsquo;il fonctionne sur la m\u00e9moire vive RAM, offrant un traitement plus rapide que sur des disques durs. Ce moteur g\u00e9n\u00e9ral peut \u00eatre utilis\u00e9 pour les <strong>requ\u00eates SQL distribu\u00e9es<\/strong>, pour la cr\u00e9ation de pipelines de donn\u00e9es, pour l&rsquo;ingestion de donn\u00e9es dans une base de donn\u00e9es, pour <strong>l&rsquo;ex\u00e9cution d&rsquo;algorithmes de Machine Learning<\/strong> ou pour travailler avec des flux de donn\u00e9es et des graphiques.<\/span><\/p><p><span style=\"font-weight: 400;\">De nos jours, Spark est inclus avec la plupart des distributions Hadoop. Il est devenu <strong>le principal framework de traitement Big Data<\/strong>, gr\u00e2ce \u00e0 plusieurs avantages \u00e0 commencer par sa vitesse et son API <strong>tr\u00e8s simple d&rsquo;utilisation pour les d\u00e9veloppeurs<\/strong>.<\/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-be99d7b elementor-widget elementor-widget-image\" data-id=\"be99d7b\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"327\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/01\/big-data-1024x419.jpg\" class=\"attachment-large size-large wp-image-139073\" alt=\"big-data\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/01\/big-data-1024x419.jpg 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/01\/big-data-300x123.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/01\/big-data-768x314.jpg 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/01\/big-data.jpg 1520w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f36e176 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f36e176\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a7bf349\" data-id=\"a7bf349\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-306b3ac elementor-widget elementor-widget-heading\" data-id=\"306b3ac\" 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\">Pourquoi suivre une formation Hadoop et Spark ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16f3b7e elementor-widget elementor-widget-text-editor\" data-id=\"16f3b7e\" 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;\">Pour travailler en tant que Data Scientist, Data Analyst ou Data Engineer, <strong>ma\u00eetriser les outils de Big Data comme Apache Hadoop et Spark est indispensable<\/strong>. En suivant une formation, vous pourrez acqu\u00e9rir une expertise tr\u00e8s recherch\u00e9e en entreprise.<\/span><\/p><p><span style=\"font-weight: 400;\">En 2021, Glassdoor estime que la Data Science est <strong>le deuxi\u00e8me secteur d&rsquo;activit\u00e9 \u00e0 plus forte croissance aux \u00c9tats-Unis<\/strong>. Les professionnels sont convoit\u00e9s dans tous les secteurs, \u00e0 l&rsquo;heure o\u00f9 le volume mondial de donn\u00e9es explose en m\u00eame temps que l&rsquo;adoption de l&rsquo;intelligence artificielle.<\/span><\/p><p><span style=\"font-weight: 400;\">En France, selon notre enqu\u00eate men\u00e9e aupr\u00e8s du CAC 40, un Data Scientist peut gagner <strong>entre 35 000 et 55 000\u20ac par an<\/strong> en tant que d\u00e9butant, et <strong>entre 45 000 et 60 000 euros avec un peu d&rsquo;exp\u00e9rience<\/strong>. Un Data Analyst quant \u00e0 lui gagne entre 35 000\u20ac et 60 000\u20ac par an.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e9af413 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e9af413\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-02eabb9\" data-id=\"02eabb9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3da2070 elementor-widget elementor-widget-heading\" data-id=\"3da2070\" 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\">Comment suivre une formation Hadoop et Spark ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b6f1bd elementor-widget elementor-widget-text-editor\" data-id=\"6b6f1bd\" 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;\">Pour apprendre \u00e0 utiliser Hadoop et Spark, vous pouvez choisir les <strong>formations DataScientest<\/strong>. Ces outils Big Data sont au coeur de nos programmes Data Engineer, Data Scientist et Data Analyst.<\/span><\/p><p><span style=\"font-weight: 400;\">\u00c0 travers ces cursus, vous apprendrez \u00e0 utiliser <strong>Hadoop <\/strong>et <strong>Spark<\/strong>, mais aussi <strong>la programmation en Python<\/strong>, <strong>le langage SQL<\/strong> pour les bases de donn\u00e9es, <strong>le Machine Learning<\/strong>, <strong>le<\/strong> <strong>DevOps<\/strong> ou encore <strong>la DataViz<\/strong>. \u00c0 l&rsquo;issue du parcours, vous aurez toutes les comp\u00e9tences requises pour exercer les m\u00e9tiers du Big Data.<\/span><\/p><p><span style=\"font-weight: 400;\">Que vous soyez en recherche d&#8217;emploi ou en activit\u00e9, vous pouvez choisir entre le format <strong>BootCamp<\/strong> intensif ou la <strong>Formation Continue<\/strong>. Notre approche innovante de Blended Learning marie plateforme coach\u00e9e sur le Cloud et masterclass.<\/span><\/p><p><span style=\"font-weight: 400;\">\u00c0 la fin de la formation, vous recevrez un certificat d\u00e9cern\u00e9 par <strong>MINES ParisTech \/ PSL Executive Education<\/strong>. Ce titre est reconnu par l&rsquo;industrie, et plus de 80% des alumnis ont trouv\u00e9 un emploi imm\u00e9diatement.<\/span><\/p><p><span style=\"font-weight: 400;\">Pour le financement, nos programmes sont \u00e9ligibles au <strong>Compte Personnel de Formation<\/strong>. Alors profitez-en ! D\u00e9couvrez d\u00e8s \u00e0 pr\u00e9sent les formations DataScientest.<\/span><\/p><p><span style=\"font-weight: 400;\">Vous savez tout sur les formations Hadoop \/ Spark. D\u00e9couvrez notre dossier complet sur la Data Science, et notre dossier sur <strong>les algorithmes de Machine Learning<\/strong>.<\/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-a8a2b3b elementor-widget elementor-widget-html\" data-id=\"a8a2b3b\" 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'est-ce que Apache Hadoop ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Apache Hadoop est un framework open source, utilis\u00e9 pour stocker et traiter de larges ensembles de donn\u00e9es. Il permet d'analyser les donn\u00e9es en parall\u00e8le sur un cluster de multiples ordinateurs, plut\u00f4t que sur une seule machine. Ceci permet un important gain de vitesse.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu'est-ce que Apache Spark ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Apache Spark est un syst\u00e8me de traitement distribu\u00e9 utilis\u00e9 pour les workloads de Big Data. Il utilise le caching in-memory et l'ex\u00e9cution de requ\u00eate optimis\u00e9e pour permettre des requ\u00eates rapides sur des donn\u00e9es de n'importe quelle taille. Pour faire simple, il s'agit d'un moteur rapide pour le traitement Big Data.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Pourquoi suivre une formation Hadoop et Spark ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Pour travailler en tant que Data Scientist, Data Analyst ou Data Engineer, ma\u00eetriser les outils de Big Data comme Apache Hadoop et Spark est indispensable. En suivant une formation, vous pourrez acqu\u00e9rir une expertise tr\u00e8s recherch\u00e9e en entreprise.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Comment suivre une formation Hadoop et Spark ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Pour apprendre \u00e0 utiliser Hadoop et Spark, vous pouvez choisir les formations DataScientest. Ces outils Big Data sont au coeur de nos programmes Data Engineer, Data Scientist et Data Analyst.\"\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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1748d9a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1748d9a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6adb308\" data-id=\"6adb308\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d6f4a4e elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d6f4a4e\" 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 les formations DataScientest<\/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<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Une formation Hadoop et Spark vous permettra de devenir un professionnel de la Data Science. D\u00e9couvrez pourquoi et comment ma\u00eetriser ces outils de traitement du Big Data. Le traitement du Big Data requiert de nouveaux outils, capables de traiter de vastes volumes de donn\u00e9es. Parmi les principaux logiciels utilis\u00e9s par les Data Scientists ou les [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":139063,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[1417],"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>Formation Hadoop Spark : comment apprendre \u00e0 manier les outils Big Data ?<\/title>\n<meta name=\"description\" content=\"Hadoop et Spark sont des outils Big Data essentiels pour les experts du Data (Data Scientist, Data Analyst et Data Engineer). 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