{"id":63348,"date":"2021-05-14T07:48:51","date_gmt":"2021-05-14T06:48:51","guid":{"rendered":"https:\/\/datascientest.com\/?p=63348"},"modified":"2023-11-09T14:07:20","modified_gmt":"2023-11-09T14:07:20","slug":"apache-hive","status":"publish","type":"post","link":"https:\/\/datascientest.com\/apache-hive","title":{"rendered":"Apache Hive Hadoop : Le SQL au service de la prise de d\u00e9cision"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"63348\" class=\"elementor elementor-63348\" 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-fc0c627 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc0c627\" 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-2006b00\" data-id=\"2006b00\" 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-05a74aa elementor-widget elementor-widget-heading\" data-id=\"05a74aa\" 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\">Le framework open-source de la principale plateforme du Big Data, Hadoop, se r\u00e9v\u00e8le id\u00e9al pour le stockage et le traitement de quantit\u00e9s massives de donn\u00e9es. Cependant, pour l\u2019extraction de donn\u00e9es, cette plateforme se r\u00e9v\u00e8le souvent complexe, chronophage et co\u00fbteuse. C\u2019est pourquoi, la fondation Apache a d\u00e9velopp\u00e9 une nouvelle alternative. Il s\u2019agit de Apache Hive.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9e4582 elementor-widget elementor-widget-text-editor\" data-id=\"b9e4582\" 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-family: var( --e-global-typography-text-font-family ), Sans-serif; font-size: var( --e-global-typography-text-font-size ); font-weight: var( --e-global-typography-text-font-weight );\">Pour rappel, en programmation informatique, un framework d\u00e9signe un ensemble coh\u00e9rent de composants logiciels structurels qui sert \u00e0 cr\u00e9er les fondations ainsi que l\u2019architecture d\u2019un logiciel.<\/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-c2cac9c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"c2cac9c\" 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 Apache Hive<\/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-065c795 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"065c795\" 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-77a4df4\" data-id=\"77a4df4\" 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-3a78e29 elementor-widget elementor-widget-heading\" data-id=\"3a78e29\" 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\">Apache Hive \u2013 de quoi s\u2019agit-il ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7e3d12 elementor-widget elementor-widget-text-editor\" data-id=\"b7e3d12\" 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><b>Apache Hive<\/b><span style=\"font-weight: 400;\"> est un <\/span><a href=\"https:\/\/datascientest.com\/data-warehouse\">datawarehouse (entrep\u00f4t de donn\u00e9es)<\/a><span style=\"font-weight: 400;\">\u00a0open source pour Hadoop. Un entrep\u00f4t de donn\u00e9es fonctionne comme un d\u00e9p\u00f4t central o\u00f9 <strong>l\u2019<\/strong><\/span><strong>information <\/strong><span style=\"font-weight: 400;\">provient d\u2019une ou de plusieurs <\/span><b>sources de donn\u00e9es<\/b><span style=\"font-weight: 400;\">. Il recueille ainsi des donn\u00e9es de sources vari\u00e9es et h\u00e9t\u00e9rog\u00e8nes dans le but principal de soutenir l&rsquo;<\/span><b>analyse<\/b><span style=\"font-weight: 400;\">, le <\/span><b>requ\u00eatage<\/b><span style=\"font-weight: 400;\"> avec un langage proche syntaxiquement de<\/span> <a href=\"https:\/\/datascientest.com\/sql-tout-savoir\">SQL <\/a><span style=\"font-weight: 400;\">et faciliter le processus de <\/span><b>prise de d\u00e9cision<\/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\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-090eb9a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"090eb9a\" 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-0769deb\" data-id=\"0769deb\" 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-8c980ea elementor-widget elementor-widget-image\" data-id=\"8c980ea\" 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=\"800\" height=\"439\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-1024x562.png\" class=\"attachment-large size-large wp-image-63435\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-1024x562.png 1024w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-300x165.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-768x422.png 768w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-1536x843.png 1536w, https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/schema_hadoop-116-2048x1124.png 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\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<\/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-97f3041 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"97f3041\" 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-e250541\" data-id=\"e250541\" 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-b76fc78 elementor-widget elementor-widget-heading\" data-id=\"b76fc78\" 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 Apache Hive ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9c9ed99 elementor-widget elementor-widget-text-editor\" data-id=\"9c9ed99\" 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=\"color: #000000;\"><span style=\"font-weight: 400;\">Apache Hive traduit les programmes r\u00e9dig\u00e9s en <strong>langage<\/strong> <\/span><b>HiveQL<\/b><span style=\"font-weight: 400;\"> (proche de SQL) en une ou plusieurs t\u00e2ches Java MapReduce, Apache Tez, ou Apache Spark. Il s\u2019agit de trois <\/span><b>moteurs d\u2019ex\u00e9cution<\/b><span style=\"font-weight: 400;\"> pouvant \u00eatre lanc\u00e9s sur Hadoop. Ensuite, Apache Hive organise les donn\u00e9es <\/span><b>en tableau <\/b><span style=\"font-weight: 400;\">pour le <\/span><b>fichier HDFS<\/b><span style=\"font-weight: 400;\"> (Hadoop Distributed Filed System) afin d\u2019ex\u00e9cuter les t\u00e2ches sur un cluster pour produire une r\u00e9ponse.<\/span><\/span><\/p><p><span style=\"color: #000000;\">Les tableaux Apache Hive sont similaires \u00e0 ceux d\u2019une<\/span><a href=\"https:\/\/www.oracle.com\/fr\/database\/base-de-donnees-relationnelle-definition.html\"> <b>base de donn\u00e9es relationnelle<\/b><\/a><span style=\"color: #000000;\">, et les unit\u00e9s de donn\u00e9es sont organis\u00e9es de l\u2019unit\u00e9 la plus large \u00e0 la plus granulaire. Les bases de donn\u00e9es sont constitu\u00e9es de <strong>tableaux<\/strong>compos\u00e9s de <strong>partitions<\/strong>, pouvant \u00e0 nouveau \u00eatre d\u00e9compos\u00e9es en\u00a0 \u00ab <strong>buckets<\/strong>\u00bb .<\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">Les donn\u00e9es sont <\/span><b>accessibles <\/b><span style=\"font-weight: 400;\">via HiveQL. Au sein de chaque base de donn\u00e9es, les donn\u00e9es sont num\u00e9rot\u00e9es et chaque tableau correspond \u00e0 un <\/span><b>r\u00e9pertoire HDFS<\/b><span style=\"font-weight: 400;\">.<\/span><\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">Au sein de l\u2019architecture d\u2019Apache Hive<\/span><b>,\u00a0<\/b><span style=\"font-weight: 400;\">de <\/span><b>multiples interfaces <\/b><span style=\"font-weight: 400;\">sont <\/span><b>disponibles <\/b><span style=\"font-weight: 400;\">comme notamment interface web, CLI,<\/span> <span style=\"font-weight: 400;\">ou encore celles des clients externes.<\/span> <span style=\"font-weight: 400;\">En effet, le serveur \u00ab\u00a0Apache Hive Thrift\u00a0\u00bb permet aux clients distants de soumettre des commandes et des requ\u00eates \u00e0 Apache Hive en utilisant divers langages de programmation. Le r\u00e9pertoire central d\u2019Apache Hive est un \u00ab\u00a0metastore\u00a0\u00bb contenant toutes les informations.<\/span><\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">Le\u00a0moteur permettant le fonctionnement de Hive est appel\u00e9 <\/span><b>\u00ab\u00a0le pilote\u00a0\u00bb<\/b><span style=\"font-weight: 400;\">. Il regroupe un <\/span><b>compilateur <\/b><span style=\"font-weight: 400;\">et un <\/span><b>optimisateur<\/b><span style=\"font-weight: 400;\"> pour d\u00e9terminer le meilleur plan d\u2019ex\u00e9cution, ainsi qu\u2019un <\/span><b>ex\u00e9cuteur<\/b><span style=\"font-weight: 400;\">.<\/span><\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">Enfin,\u00a0<\/span><b>la s\u00e9curit\u00e9 est assur\u00e9e par Hadoop<\/b><span style=\"font-weight: 400;\">. Elle repose donc sur <\/span><b>Kerberos<\/b> <span style=\"font-weight: 400;\">\u00a0pour <\/span><b>l\u2019authentification <\/b><span style=\"font-weight: 400;\">mutuelle entre le client et le serveur. Les <\/span><b>permissions <\/b><span style=\"font-weight: 400;\">pour les fichiers nouvellement cr\u00e9\u00e9s dans Apache Hive sont dict\u00e9es par HDFS, qui permet <strong>l\u2019<\/strong><\/span><strong>autorisation<\/strong><span style=\"font-weight: 400;\"> par utilisateur, groupe ou autre<\/span><\/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-bee7359 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bee7359\" 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-b4b97aa\" data-id=\"b4b97aa\" 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-65232be elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"65232be\" 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\">Suivre une formation d'Ing\u00e9nieur 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<\/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-de9f726 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"de9f726\" 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-73f5db1\" data-id=\"73f5db1\" 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-7ab8d1a elementor-widget elementor-widget-heading\" data-id=\"7ab8d1a\" 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 int\u00e9r\u00eats d\u2019utiliser Apache Hive ?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-290b12c elementor-widget elementor-widget-text-editor\" data-id=\"290b12c\" 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;\">Apache Hive est une<\/span><b>\u00a0solution id\u00e9ale <\/b><span style=\"font-weight: 400;\">pour les<\/span><b> requ\u00eates <\/b><span style=\"font-weight: 400;\">et les<\/span><b> analyses de donn\u00e9es<\/b><span style=\"font-weight: 400;\">. Elle permet donc d\u2019obtenir des <\/span><b>informations qualitatives<\/b><span style=\"font-weight: 400;\"> (\u00ab\u00a0insights\u00a0\u00bb) procurant un <\/span><b>avantage comp\u00e9titif<\/b><span style=\"font-weight: 400;\"> et facilitant la r\u00e9activit\u00e9 face \u00e0 la demande du march\u00e9.<\/span><\/p><p><span style=\"font-weight: 400;\">Parmi les principaux avantages de Apache Hive, on peut citer\u00a0la<\/span><b> simplicit\u00e9 d\u2019utilisation<\/b><span style=\"font-weight: 400;\">\u00a0li\u00e9e \u00e0 son langage\u00a0 \u00ab\u00a0SQL-friendly\u00a0\u00bb\u00a0. En outre, ce logiciel\u00a0<\/span><b>acc\u00e9l\u00e8re l\u2019insertion initiale de donn\u00e9es<\/b><span style=\"font-weight: 400;\">\u00a0puisque les donn\u00e9es n\u2019ont pas besoin d\u2019\u00eatre lues ni num\u00e9rot\u00e9es sur un disque dans le format interne de la <\/span><a href=\"https:\/\/datascientest.com\/base-de-donnees-definition\"><b>base de donn\u00e9es<\/b><\/a>.<\/p><p>Sachant que <strong>les donn\u00e9es sont stock\u00e9es dans le HDFS<\/strong>, il est possible de stocker de larges datasets pouvant aller jusqu\u2019\u00e0 des centaines de <strong>petabytes <\/strong>de donn\u00e9es sur Apache Hive. De fait, cette solution est <strong>bien plus scalable<\/strong> qu\u2019une base de donn\u00e9es traditionnelle. Sachant qu\u2019il s\u2019agit d\u2019un service Cloud, Apache Hive permet aux utilisateurs de lancer rapidement des serveurs virtuels en fonction des fluctuations de workloads (soit, des t\u00e2ches).<\/p><p>La <strong>s\u00e9curit\u00e9<\/strong> est \u00e9galement au rendez-vous, avec la\u00a0<strong>possibilit\u00e9 de r\u00e9pliquer les workloads<\/strong> critiques\u00a0pour la restauration en cas de probl\u00e8me. Enfin, la <strong>capacit\u00e9 de travail<\/strong> est hors pair puisqu\u2019il est possible d\u2019effectuer\u00a0<strong>jusqu\u2019\u00e0 100 000 requ\u00eates par heure<\/strong>.<\/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-ef6e6b2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ef6e6b2\" 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-d2ffa1b\" data-id=\"d2ffa1b\" 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-fd3a382 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"fd3a382\" 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<\/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-db02921 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"db02921\" 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-36be7b7\" data-id=\"36be7b7\" 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-d8b76b3 elementor-widget elementor-widget-html\" data-id=\"d8b76b3\" 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\": \"Apache Hive \u2013 de quoi s\u2019agit-il ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Apache Hive est un datawarehouse (entrep\u00f4t de donn\u00e9es)\u00a0open source pour Hadoop. Un entrep\u00f4t de donn\u00e9es fonctionne comme un d\u00e9p\u00f4t central o\u00f9 l\u2019information provient d\u2019une ou de plusieurs sources de donn\u00e9es. Il recueille ainsi des donn\u00e9es de sources vari\u00e9es et h\u00e9t\u00e9rog\u00e8nes dans le but principal de soutenir l'analyse, le requ\u00eatage avec un langage proche syntaxiquement de SQL et faciliter le processus de prise de d\u00e9cision.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Comment fonctionne Apache Hive ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Apache Hive traduit les programmes r\u00e9dig\u00e9s en langage HiveQL (proche de SQL) en une ou plusieurs t\u00e2ches Java MapReduce, Apache Tez, ou Apache Spark. Il s\u2019agit de trois moteurs d\u2019ex\u00e9cution pouvant \u00eatre lanc\u00e9s sur Hadoop. Ensuite, Apache Hive organise les donn\u00e9es en tableau pour le fichier HDFS (Hadoop Distributed Filed System) afin d\u2019ex\u00e9cuter les t\u00e2ches sur un cluster pour produire une r\u00e9ponse.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Quels sont les int\u00e9r\u00eats d\u2019utiliser Apache Hive ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Apache Hive est une solution id\u00e9ale pour les requ\u00eates et les analyses de donn\u00e9es. Elle permet donc d\u2019obtenir des informations qualitatives (\u00ab insights \u00bb) procurant un avantage comp\u00e9titif et facilitant la r\u00e9activit\u00e9 face \u00e0 la demande du march\u00e9.\"\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\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Le framework open-source de la principale plateforme du Big Data, Hadoop, se r\u00e9v\u00e8le id\u00e9al pour le stockage et le traitement de quantit\u00e9s massives de donn\u00e9es. Cependant, pour l\u2019extraction de donn\u00e9es, cette plateforme se r\u00e9v\u00e8le souvent complexe, chronophage et co\u00fbteuse. C\u2019est pourquoi, la fondation Apache a d\u00e9velopp\u00e9 une nouvelle alternative. Il s\u2019agit de Apache Hive. Pour [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":69516,"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>Apache Hive Hadoop : Le SQL au service de la prise de d\u00e9cision<\/title>\n<meta name=\"description\" content=\"Le framework open-source de la principale plateforme du Big Data, Hadoop, se r\u00e9v\u00e8le id\u00e9al pour le stockage et le traitement de quantit\u00e9s massives de donn\u00e9es. Cependant, pour l\u2019extraction de donn\u00e9es, cette plateforme se r\u00e9v\u00e8le souvent complexe, chronophage et co\u00fbteuse. C\u2019est pourquoi, la fondation Apache a d\u00e9velopp\u00e9 une nouvelle alternative. Il s\u2019agit de Apache Hive.\" \/>\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\/apache-hive\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apache Hive Hadoop : Le SQL au service de la prise de d\u00e9cision\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/apache-hive\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2021-05-14T06:48:51+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:07:20+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/04\/illu_hive_blog-01.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=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/apache-hive\",\"url\":\"https:\/\/datascientest.com\/apache-hive\",\"name\":\"Apache Hive Hadoop : Le SQL au service de la prise de d\u00e9cision\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2021-05-14T06:48:51+00:00\",\"dateModified\":\"2023-11-09T14:07:20+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Le framework open-source de la principale plateforme du Big Data, Hadoop, se r\u00e9v\u00e8le id\u00e9al pour le stockage et le traitement de quantit\u00e9s massives de donn\u00e9es. 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