{"id":62728,"date":"2021-05-19T15:56:28","date_gmt":"2021-05-19T14:56:28","guid":{"rendered":"https:\/\/datascientest.com\/?p=62728"},"modified":"2023-11-09T14:07:19","modified_gmt":"2023-11-09T14:07:19","slug":"mapreduce","status":"publish","type":"post","link":"https:\/\/datascientest.com\/mapreduce","title":{"rendered":"MapReduce : comment l&rsquo;utiliser pour le Big Data ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"62728\" class=\"elementor elementor-62728\" 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-3ab194a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3ab194a\" 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-6e6432b\" data-id=\"6e6432b\" 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-c67dba5 elementor-widget elementor-widget-heading\" data-id=\"c67dba5\" 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\">MapReduce est le mod\u00e8le de programmation du framework Hadoop. Il permet d'analyser les immenses volumes de donn\u00e9es Big Data gr\u00e2ce au traitement parall\u00e8le. D\u00e9couvrez tout ce que vous devez savoir : pr\u00e9sentation, fonctionnement, alternatives, avantages, formations...<\/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-c360a36 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c360a36\" 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-63c549f\" data-id=\"63c549f\" 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-d9c8467 elementor-widget elementor-widget-text-editor\" data-id=\"d9c8467\" 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>Les volumes massifs du Big Data offrent de nombreuses opportunit\u00e9s pour les entreprises. Toutefois, il peut \u00eatre difficile de traiter ces donn\u00e9es de mani\u00e8re rapide et efficace sur des syst\u00e8mes traditionnels. Il est n\u00e9cessaire de<strong> se tourner vers de nouvelles solutions<\/strong> sp\u00e9cialement con\u00e7ues \u00e0 cet effet.<\/p><p>Le <strong>mod\u00e8le de programmation MapReduce<\/strong> fait partie de ces solutions. Il fut initialement cr\u00e9\u00e9 par Google pour analyser les r\u00e9sultats de son moteur de recherche. Avec le temps, cet outil est devenu extr\u00eamement populaire pour sa capacit\u00e9 \u00e0 d\u00e9composer des terabytes de donn\u00e9es pour les traiter en parall\u00e8le. Cette approche permet d&rsquo;obtenir des r\u00e9sultats plus rapidement.<\/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-381b4ef elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"381b4ef\" 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-e3e03b6\" data-id=\"e3e03b6\" 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-46ef6b6 elementor-widget elementor-widget-heading\" data-id=\"46ef6b6\" 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 MapReduce ?<\/h3>\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-363f08e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"363f08e\" 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-932e81e\" data-id=\"932e81e\" 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-d34957a elementor-widget elementor-widget-text-editor\" data-id=\"d34957a\" 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>Le mod\u00e8le de programmation MapReduce est l&rsquo;un des principaux composants <a href=\"https:\/\/datascientest.com\/hadoop\" target=\"_blank\" rel=\"noopener\">du framework Hadoop<\/a>. Il est utilis\u00e9 pour <strong>acc\u00e9der aux donn\u00e9es Big Data<\/strong> stock\u00e9es au sein du Hadoop File System (HDFS).<\/p><p>L&rsquo;int\u00e9r\u00eat de MapReduce est de<strong> faciliter le traitement concurrent des donn\u00e9es<\/strong>. Pour parvenir \u00e0 cette prouesse, les volumes massifs de donn\u00e9es, de l&rsquo;ordre de plusieurs petabytes, sont d\u00e9compos\u00e9s en plusieurs parties de moindres envergures.<\/p><p>Ces morceaux de donn\u00e9es sont <strong>trait\u00e9s en parall\u00e8le, sur les serveurs Hadoop<\/strong>. Apr\u00e8s le traitement, les donn\u00e9es en provenance des multiples serveurs sont agr\u00e9g\u00e9es pour renvoyer un r\u00e9sultat consolid\u00e9 \u00e0 l&rsquo;application.<\/p><p>Hadoop est capable d&rsquo;ex\u00e9cuter des programmes MapReduce \u00e9crits dans <strong>divers langages : Java, Ruby, Python, C++<\/strong>&#8230;<\/p><p>L&rsquo;acc\u00e8s et le stockage des donn\u00e9es sont<strong> bas\u00e9s sur disque<\/strong>. Les \u00a0\u00bb input \u00a0\u00bb (entr\u00e9es) sont stock\u00e9es sous forme de fichiers contenant des donn\u00e9es structur\u00e9es, semi-structur\u00e9es ou non structur\u00e9es. Le \u00a0\u00bb output \u00a0\u00bb (sortie) est aussi stock\u00e9e sous forme de fichiers.<\/p><p>Prenons pour exemple<strong> un ensemble de donn\u00e9es de 5 terabytes<\/strong>. Si l&rsquo;on r\u00e9partit le traitement sur un cluster Hadoop de 10 000 serveurs, chacun doit traiter environ 500 megabytes de donn\u00e9es. Le dataset complet peut donc \u00eatre trait\u00e9 beaucoup plus rapidement qu&rsquo;avec un traitement s\u00e9quentiel traditionnel.<\/p><p>Fondamentalement, MapReduce permet<strong> d&rsquo;ex\u00e9cuter la logique directement sur le serveur<\/strong> o\u00f9 r\u00e9sident les donn\u00e9es. Cette approche s&rsquo;oppose \u00e0 celle d&rsquo;envoyer les donn\u00e9es \u00e0 l&#8217;emplacement o\u00f9 r\u00e9side la logique ou l&rsquo;application. C&rsquo;est ce qui permet d&rsquo;acc\u00e9l\u00e9rer le traitement.<\/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-d731262 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d731262\" 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-03bd429\" data-id=\"03bd429\" 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-bd3cd2e elementor-widget elementor-widget-video\" data-id=\"bd3cd2e\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=A4v6hKD-qcU&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.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-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0b9fdcb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0b9fdcb\" 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-2914329\" data-id=\"2914329\" 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-4233248 elementor-widget elementor-widget-heading\" data-id=\"4233248\" 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\">Les alternatives \u00e0 MapReduce : Hive, Pig...<\/h3>\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-585320e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"585320e\" 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-e08f909\" data-id=\"e08f909\" 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-18a453e elementor-widget elementor-widget-text-editor\" data-id=\"18a453e\" 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>Jadis, <strong>MapReduce \u00e9tait la seule m\u00e9thode<\/strong> permettant de r\u00e9cup\u00e9rer les donn\u00e9es stock\u00e9es dans HDFS. Ce n&rsquo;est plus le cas aujourd&rsquo;hui. Il existe d&rsquo;autres syst\u00e8mes bas\u00e9s sur les requ\u00eates, comme Hive et Pig.<\/p><p>Ces derniers permettent de <strong>r\u00e9cup\u00e9rer les donn\u00e9es depuis HDFS<\/strong> gr\u00e2ce \u00e0 des requ\u00eates SQL. La plupart du temps, toutefois, elles sont ex\u00e9cut\u00e9es aux c\u00f4t\u00e9s de jobs \u00e9crits avec le mod\u00e8le MapReduce pour tirer profit de ses multiples avantages.<\/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-6578cf3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6578cf3\" 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-ee9c080\" data-id=\"ee9c080\" 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-480bb15 elementor-widget elementor-widget-heading\" data-id=\"480bb15\" 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 fonctionne MapReduce ?<\/h3>\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-ccf6404 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ccf6404\" 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-a82997d\" data-id=\"a82997d\" 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-fe5a61c elementor-widget elementor-widget-text-editor\" data-id=\"fe5a61c\" 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>Le fonctionnement de MapReduce s&rsquo;articule principalement autour de <strong>deux fonctions : Map, et Reduce<\/strong>. Pour faire simple, Map sert \u00e0 d\u00e9composer et \u00e0 cartographier les donn\u00e9es. Reduce m\u00e9lange et r\u00e9duit les donn\u00e9es.<\/p><p>Ces<strong> fonctions sont s\u00e9quenc\u00e9es l&rsquo;une apr\u00e8s l&rsquo;autre<\/strong>. Pour d\u00e9signer les serveurs ex\u00e9cutant les fonctions Map et Reduce, on utilise les termes de Mappers et Reducers. Il peut toutefois s&rsquo;agir des m\u00eames serveurs.<\/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-42c732b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"42c732b\" 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-fdc86e9\" data-id=\"fdc86e9\" 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-4fb076c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"4fb076c\" 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-engineer\">\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\">Apprendre \u00e0 utiliser MapReduce<\/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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-148a647 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"148a647\" 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-b54ee70\" data-id=\"b54ee70\" 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-e56b3a7 elementor-widget elementor-widget-heading\" data-id=\"e56b3a7\" 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\">La fonction Map<\/h3>\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-190a967 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"190a967\" 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-8fda779\" data-id=\"8fda779\" 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-1f44909 elementor-widget elementor-widget-text-editor\" data-id=\"1f44909\" 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>Les<strong> donn\u00e9es d&rsquo;input sont d\u00e9compos\u00e9es en blocs<\/strong> de moindre envergure. Chacun de ces blocs est assign\u00e9 \u00e0 un \u00a0\u00bb mapper \u00a0\u00bb pour le traitement.<\/p><p>Prenons<strong> l&rsquo;exemple d&rsquo;un fichier contenant 100 enregistrements<\/strong> \u00e0 traiter. Il est possible d&rsquo;utiliser 100 mappers simultan\u00e9ment pour traiter chaque enregistrement s\u00e9par\u00e9ment. Toutefois, on peut aussi confier plusieurs enregistrements \u00e0 chaque mapper.<\/p><p>En r\u00e9alit\u00e9,<strong> le framework Hadoop se charge de d\u00e9cider automatiquement<\/strong> combien de mappers utiliser. Ce choix d\u00e9pend de la taille des donn\u00e9es \u00e0 traiter et des blocs de m\u00e9moire disponibles sur chaque serveur.<\/p><p>La<strong> fonction Map re\u00e7oit l&rsquo;input \u00e0 partir du disque<\/strong> sous forme de paires \u00a0\u00bb cl\u00e9\/valeur \u00ab\u00a0. Ces paires sont trait\u00e9es, et un autre ensemble de cl\u00e9\/valeur interm\u00e9diaire est produit.<\/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-0186e16 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0186e16\" 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-56ac87a\" data-id=\"56ac87a\" 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-17bc16b elementor-widget elementor-widget-heading\" data-id=\"17bc16b\" 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\">La fonction Reduce<\/h3>\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-d2efa36 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d2efa36\" 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-b2bae31\" data-id=\"b2bae31\" 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-928e930 elementor-widget elementor-widget-text-editor\" data-id=\"928e930\" 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>Apr\u00e8s que tous les mappers aient termin\u00e9 leurs t\u00e2ches de traitement, <strong>le framework m\u00e9lange et organise<\/strong> les r\u00e9sultats. Il les transmet ensuite aux \u00a0\u00bb reducers \u00ab\u00a0. Pr\u00e9cisons qu&rsquo;un reducer ne peut d\u00e9marrer si un mapper est encore actif.<\/p><p>La fonction Reduce re\u00e7oit aussi <strong>les inputs sous forme de paires cl\u00e9\/valeur<\/strong>. Toutes les valeurs produites par map ayant la m\u00eame cl\u00e9 sont assign\u00e9es \u00e0 un reducer unique. Celui-ci se charge d&rsquo;agr\u00e9ger les valeurs pour cette cl\u00e9. Reduce produit ensuite un ouput final, toujours sous forme de paie cl\u00e9\/valeur.<\/p><p>Toutefois, <strong>le type de cl\u00e9s et de valeurs varie selon les cas d&rsquo;usage<\/strong>. Tous les inputs et outputs sont stock\u00e9s dans le HDFS. Pr\u00e9cisons que la fonction map est imp\u00e9rative pour filtrer et trier les donn\u00e9es initiales. En revanche, la fonction reduce est optionnelle.<\/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-f910cfe elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f910cfe\" 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-c7c69db\" data-id=\"c7c69db\" 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-5db9a6c elementor-widget elementor-widget-heading\" data-id=\"5db9a6c\" 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\">Combine et Partition<\/h3>\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-db460bf elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"db460bf\" 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-21114f2\" data-id=\"21114f2\" 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-499c8b6 elementor-widget elementor-widget-text-editor\" data-id=\"499c8b6\" 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>Il existe <strong>deux \u00e9tapes interm\u00e9diaires entre Map et Reduce<\/strong>. Ces deux \u00e9tapes sont appel\u00e9es Combine et Partition.<\/p><p>Le<strong> processus Combine est optionnel<\/strong>. Un \u00a0\u00bb combiner \u00a0\u00bb est un \u00a0\u00bb reducer \u00a0\u00bb ex\u00e9cut\u00e9 individuellement sur chaque serveur \u00a0\u00bb mapper \u00ab\u00a0. Il permet de r\u00e9duire davantage les donn\u00e9es sur chaque mapper, dans une forme simplifi\u00e9e. Ceci permet de simplifier le shuffling et l&rsquo;organisation, puisque le volume de donn\u00e9es \u00e0 organiser est r\u00e9duit.<\/p><p>L&rsquo;\u00e9tape de Partition permet quant \u00e0 elle de <strong>traduire les paires \u00a0\u00bb cl\u00e9 valeur \u00ab\u00a0<\/strong> produites par les mappers en un autre ensemble de paires cl\u00e9 valeur avant de les transmettre au reducer. Ce processus d\u00e9cide comment les donn\u00e9es doivent \u00eatre pr\u00e9sent\u00e9es au reducer et les assignent \u00e0 un reducer sp\u00e9cifique.<\/p><p>Le <strong>partionneur par d\u00e9faut d\u00e9termine la valeur \u00a0\u00bb hash \u00ab\u00a0<\/strong> pour la cl\u00e9 produite par le mapper, et lui assigne une partition en fonction de cette valeur. Le nombre de partitions est \u00e9gal au nombre de reducers. D\u00e8s que le partitionnement est complet, les donn\u00e9es en provenance de chaque partition sont envoy\u00e9es \u00e0 un reducer sp\u00e9cifique.<\/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-5bfe73e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5bfe73e\" 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-8ed466d\" data-id=\"8ed466d\" 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-21f9c09 elementor-widget elementor-widget-heading\" data-id=\"21f9c09\" 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\">Exemples de cas d'usage<\/h3>\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-7e2105d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7e2105d\" 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-4978135\" data-id=\"4978135\" 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-dff4a4a elementor-widget elementor-widget-text-editor\" data-id=\"dff4a4a\" 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>Le paradigme de programmation MapReduce est id\u00e9al pour n&rsquo;importe quel probl\u00e8me complexe pouvant \u00eatre r\u00e9solu gr\u00e2ce \u00e0 la parall\u00e9lisation. Il s&rsquo;agit donc d&rsquo;une<strong> approche ad\u00e9quate pour le Big Data<\/strong>.<\/p><p>Les entreprises peuvent s&rsquo;en servir pour <strong>d\u00e9terminer le prix optimal de leurs produits<\/strong> ou pour conna\u00eetre l&rsquo;efficacit\u00e9 d&rsquo;une campagne publicitaire. Elles peuvent analyser les clics, les ventes en ligne ou les tendances Twitter pour d\u00e9cider quel produit lancer afin de r\u00e9pondre \u00e0 la demande des consommateurs.<\/p><p>Alors que ces calculs \u00e9taient jadis tr\u00e8s compliqu\u00e9s,<strong> MapReduce les rend aujourd&rsquo;hui simples et accessibles<\/strong>. Il est possible d&rsquo;effectuer ces fonctions sur un r\u00e9seau de serveurs peu co\u00fbteux, et le traitement du Big Data s&rsquo;en trouve aussi largement moins cher.<\/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-8e35f8c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8e35f8c\" 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-dac710e\" data-id=\"dac710e\" 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-6f7919e elementor-widget elementor-widget-heading\" data-id=\"6f7919e\" 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\">Quels sont les avantages de MapReduce ?<\/h3>\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-2c4217f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2c4217f\" 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-b0e8d58\" data-id=\"b0e8d58\" 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-8db5b68 elementor-widget elementor-widget-text-editor\" data-id=\"8db5b68\" 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>L&rsquo;utilisation de MapReduce pr\u00e9sente <strong>de nombreux avantages<\/strong>. Tout d&rsquo;abord, ce mod\u00e8le de programmation rend Hadoop hautement extensible en permettant le stockage de larges ensembles de donn\u00e9es sous une forme distribu\u00e9e entre de multiples serveurs.<\/p><p>C&rsquo;est ce qui rend possible le traitement parall\u00e8le. Les <strong>t\u00e2ches Map et Reduce sont s\u00e9par\u00e9es<\/strong>, et l&rsquo;ex\u00e9cution parall\u00e8le r\u00e9duit le run time dans son ensemble.<\/p><p>Il s&rsquo;agit aussi d&rsquo;une <strong>solution \u00e9conomique pour le stockage<\/strong> et le traitement des donn\u00e9es. Le rapport performances \/ prix est in\u00e9galable.<\/p><p>Avec MapReduce, <strong>Hadoop est aussi extr\u00eamement flexible<\/strong>. Il est possible de stocker et de traiter des donn\u00e9es en provenance de diff\u00e9rentes sources, et m\u00eame des donn\u00e9es non structur\u00e9es.<\/p><p>La <strong>vitesse est aussi l&rsquo;un des principaux points forts<\/strong>. Le syst\u00e8me de fichiers distribu\u00e9 permet de stocker les donn\u00e9es sur le disque local d&rsquo;un cluster et les programmes MapReduce sur les m\u00eames serveurs. Les donn\u00e9es peuvent donc \u00eatre trait\u00e9es plus rapidement, puisqu&rsquo;il n&rsquo;est pas n\u00e9cessaire d&rsquo;y acc\u00e9der \u00e0 partir d&rsquo;autres serveurs.<\/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-1ec87e5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1ec87e5\" 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-d507c80\" data-id=\"d507c80\" 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-632707e elementor-widget elementor-widget-heading\" data-id=\"632707e\" 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 apprendre \u00e0 utiliser MapReduce ?<\/h3>\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-3f5321e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3f5321e\" 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-bf84a89\" data-id=\"bf84a89\" 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-8c8c09a elementor-widget elementor-widget-video\" data-id=\"8c8c09a\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=SNNK6z03TaA&amp;t=57s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-72c2789 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"72c2789\" 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-20bd492\" data-id=\"20bd492\" 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-e8768dd elementor-widget elementor-widget-text-editor\" data-id=\"e8768dd\" 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>Dans tous les secteurs, les entreprises collectent de plus en plus de donn\u00e9es. Elles ont donc<strong> besoin de traiter ces donn\u00e9es pour en tirer profit<\/strong>, et MapReduce fait partie des principales solutions.<\/p><p>Par cons\u00e9quent, apprendre \u00e0 ma\u00eetriser Hadoop et MapReduce offre de nombreuses opportunit\u00e9s professionnelles. Il s&rsquo;agit d&rsquo;une <strong>comp\u00e9tence tr\u00e8s recherch\u00e9e<\/strong>.<\/p><p>Pour acqu\u00e9rir la ma\u00eetrise de cet outil, vous pouvez vous tourner vers DataScientest. Le framework Hadoop et ses diff\u00e9rents modules sont au coeur de <strong>notre formation Data Engineer<\/strong>, au sein du module \u00a0\u00bb Big Data Volume \u00ab\u00a0.<\/p><p>Vous apprendrez notamment \u00e0 utiliser<strong> Hadoop, Hive, Pig, Hbase et Spark<\/strong> et d\u00e9couvrirez tout l&rsquo;aspect th\u00e9orique des architectures Big Data. Les autres modules de ce cursus abordent la programmation en Python, les bases de donn\u00e9es, le Big Data Vitesse et enfin l&rsquo;automatisation et le d\u00e9ploiement.<\/p><p>\u00c0 l&rsquo;issue de ce parcours, vous aurez <strong>toutes les comp\u00e9tences requises<\/strong> pour commencer \u00e0 travailler directement comme Data Engineer. Ce m\u00e9tier est en plein essor, et vous garantit de trouver un emploi avec un salaire \u00e9lev\u00e9 dans le domaine de votre choix.<\/p><p>Comme toutes nos formations, le parcours Data Engineer adopte <strong>une approche Blended Learning<\/strong> alliant apprentissage en pr\u00e9sentiel et \u00e0 distance. Il peut \u00eatre effectu\u00e9 en Formation Continue on en mode BootCamp intensif.<\/p><p>Apr\u00e8s avoir compl\u00e9t\u00e9 ce programme, les apprenants re\u00e7oivent <strong>un dipl\u00f4me certifi\u00e9 par l&rsquo;Universit\u00e9 de la Sorbonne<\/strong>. Parmi les alumnis, 93% ont trouv\u00e9 un travail imm\u00e9diatement. Ne perdez plus un instant et <a href=\"https:\/\/datascientest.com\/formation-data-engineer\" target=\"_blank\" rel=\"noopener\">d\u00e9couvrez la formation Data Engineer<\/a>.<\/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-2f344b7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2f344b7\" 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-7657033\" data-id=\"7657033\" 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-fadfc1b elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"fadfc1b\" 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-engineer\">\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\">D\u00e9couvrir la formation Data Engineer<\/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<section class=\"elementor-section elementor-top-section elementor-element elementor-element-775d4a1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"775d4a1\" 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-b591cae\" data-id=\"b591cae\" 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-4128af0 elementor-widget elementor-widget-text-editor\" data-id=\"4128af0\" 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>Vous savez tout sur MapReduce. D\u00e9couvrez <a href=\"https:\/\/datascientest.com\/hadoop\" target=\"_blank\" rel=\"noopener\">notre dossier complet sur Hadoop<\/a>, et <a href=\"https:\/\/datascientest.com\/python-tout-savoir\" target=\"_blank\" rel=\"noopener\">notre introduction au langage de programmation Python<\/a>.<\/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\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>MapReduce est le mod\u00e8le de programmation du framework Hadoop. Il permet d&rsquo;analyser les immenses volumes de donn\u00e9es Big Data gr\u00e2ce au traitement parall\u00e8le. D\u00e9couvrez tout ce que vous devez savoir : pr\u00e9sentation, fonctionnement, alternatives, avantages, formations&#8230; Les volumes massifs du Big Data offrent de nombreuses opportunit\u00e9s pour les entreprises. Toutefois, il peut \u00eatre difficile de [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":70819,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"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>MapReduce : comment l&#039;utiliser pour le Big Data ?<\/title>\n<meta name=\"description\" content=\"MapReduce est le mod\u00e8le de programmation du framework Hadoop pour l&#039;analyse de donn\u00e9es Big Data. D\u00e9finition, fonctionnement, formations...\" \/>\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\/mapreduce\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MapReduce : comment l&#039;utiliser pour le Big Data ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/mapreduce\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2021-05-19T14:56:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:07:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/illu_schema_mapreduce-04.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/mapreduce\",\"url\":\"https:\/\/datascientest.com\/mapreduce\",\"name\":\"MapReduce : comment l'utiliser pour le Big Data ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2021-05-19T14:56:28+00:00\",\"dateModified\":\"2023-11-09T14:07:19+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"MapReduce est le mod\u00e8le de programmation du framework Hadoop pour l'analyse de donn\u00e9es Big Data. 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