{"id":41076,"date":"2021-02-08T10:30:15","date_gmt":"2021-02-08T09:30:15","guid":{"rendered":"https:\/\/datascientest.com\/?p=41076"},"modified":"2023-11-09T14:10:08","modified_gmt":"2023-11-09T14:10:08","slug":"les-methodes-propres-aux-projets-big-data","status":"publish","type":"post","link":"https:\/\/datascientest.com\/les-methodes-propres-aux-projets-big-data","title":{"rendered":"Les m\u00e9thodes propres aux projets big data"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"41076\" class=\"elementor elementor-41076\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cffcb81 e-flex e-con-boxed e-con\" data-id=\"cffcb81\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7a6f644 elementor-widget elementor-widget-heading\" data-id=\"7a6f644\" 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\">La num\u00e9risation de la donn\u00e9e et l\u2019essor du big data ont provoqu\u00e9 un afflux massif d\u2019informations au sein des entreprises. Beaucoup d\u2019informations produites mais aussi beaucoup d\u2019informations \u00e0 traiter. Pourtant cette masse de donn\u00e9es repr\u00e9sente un v\u00e9ritable enjeu \u00e9conomique et strat\u00e9gique pour les entreprises, \u00e0 condition de savoir bien les traiter et les utiliser en d\u00e9ployant les bonnes m\u00e9thodes pour ses projets big data.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fc040e3 e-flex e-con-boxed e-con\" data-id=\"fc040e3\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5887e11 elementor-widget elementor-widget-text-editor\" data-id=\"5887e11\" 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=\"color: #000000;\"><span style=\"font-weight: 400;\">Trop d\u2019informations tue l\u2019information, c\u2019est ce que l\u2019on a coutume de dire. Et cela semble se v\u00e9rifier avec la mont\u00e9e en puissance du <a href=\"https:\/\/datascientest.com\/big-data-tout-savoir\">Big Data<\/a><\/span><span style=\"font-weight: 400;\">. Avec la multiplication et le d\u00e9veloppement des supports num\u00e9riques, les entreprises doivent faire face \u00e0 un afflux de donn\u00e9es \u00e0 g\u00e9rer. Si les entreprises commencent \u00e0 comprendre l\u2019importance de traiter ces volumes de donn\u00e9es et <a href=\"https:\/\/datascientest.com\/vos-donnees-ont-de-la-valeur-datascientest-vous-aide-les-analyser\">de leur valeur<\/a><\/span><span style=\"font-weight: 400;\">, il n\u2019est en revanche pas toujours facile pour elles de traiter ces donn\u00e9es rapidement. Un traitement rapide des donn\u00e9es implique la mise \u00e0 disposition imm\u00e9diate des outils de big data, d\u2019o\u00f9 l\u2019importance de conna\u00eetre les m\u00e9thodologies projets \u00e0 d\u00e9ployer pour conduire un projet de big data efficace.<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5c9d025 e-flex e-con-boxed e-con\" data-id=\"5c9d025\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b3674ff elementor-widget elementor-widget-heading\" data-id=\"b3674ff\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelles m\u00e9thodes propres utiliser pour r\u00e9ussir ses projets  Big Data ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b7bd69 elementor-widget elementor-widget-text-editor\" data-id=\"6b7bd69\" 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; color: #000000;\">La gestion de projet big data fait appel \u00e0 diff\u00e9rentes m\u00e9thodologies en fonction des \u00e9quipes m\u00e9tiers mais aussi des modes.<\/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-f73ec69 elementor-widget elementor-widget-heading\" data-id=\"f73ec69\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">La m\u00e9thode CRISP dans les projets Big Data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68acf81 elementor-widget elementor-widget-text-editor\" data-id=\"68acf81\" 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;\">La m\u00e9thode CRISP, appel\u00e9e CRISP-DM \u00e0 sa cr\u00e9ation en 1996 par <\/span><a style=\"color: #000000;\" href=\"https:\/\/www.ibm.com\/fr-fr\"><span style=\"font-weight: 400;\">IBM<\/span><\/a><span style=\"font-weight: 400;\">, a \u00e9t\u00e9 con\u00e7ue \u00e0 la base pour des projets de <a href=\"https:\/\/datascientest.com\/formation-data-mining\">data mining<\/a>. Totalement ind\u00e9pendante des outils et technologies utilis\u00e9s en entreprise, cette m\u00e9thode doit son succ\u00e8s et sa g\u00e9n\u00e9ralisation \u00e0 tous projets de big data gr\u00e2ce \u00e0 son sch\u00e9ma d\u2019application standard. B\u00e2tie en six \u00e9tapes, la m\u00e9thode CRISP est id\u00e9ale pour r\u00e9soudre un probl\u00e8me d\u00e9fini tant elle met l\u2019accent sur l\u2019identification des besoins et sur les objectifs m\u00e9tiers :<\/span><\/span><\/p><ul><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 1 : compr\u00e9hension du probl\u00e8me m\u00e9tier ;<\/span><\/li><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 2 : compr\u00e9hension des donn\u00e9es ;<\/span><\/li><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 3 : pr\u00e9paration des donn\u00e9es ;<\/span><\/li><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 4 : mod\u00e9lisation des donn\u00e9es ;<\/span><\/li><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 5 : \u00e9valuation ;<\/span><\/li><li><span style=\"font-weight: 400; color: #000000;\">\u00e9tape 6 : d\u00e9ploiement.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-073fd82 elementor-widget elementor-widget-text-editor\" data-id=\"073fd82\" 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; color: #000000;\">Avec son approche cyclique et it\u00e9rative, la m\u00e9thode CRISP permet ainsi de s\u2019adapter en fonction de l\u2019avanc\u00e9e du projet car elle encourage \u00e0 la collaboration et \u00e0 la communication entre l\u2019\u00e9quipe projet et les experts m\u00e9tiers, et favorise les retours en arri\u00e8re. Compatible avec des m\u00e9thodologies agile, la m\u00e9thode CRISP est ainsi tr\u00e8s largement utilis\u00e9e pour des projets Big Data ou de l\u2019<a href=\"https:\/\/datascientest.com\/analyse-predictive\">analyse pr\u00e9dictive<\/a>.<\/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-6b2de28 elementor-widget elementor-widget-heading\" data-id=\"6b2de28\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Les m\u00e9thodes Agiles dans le Big Data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e454b0 elementor-widget elementor-widget-text-editor\" data-id=\"0e454b0\" 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;\">Con\u00e7ue \u00e0 la base pour des projets data, la m\u00e9thode CRISP peut n\u00e9anmoins manquer d\u2019agilit\u00e9 dans certains projets, dans le sens o\u00f9 elle ne met pas au centre du process, le client et la production de valeur. C\u2019est pourquoi diff\u00e9rentes variantes sont apparues au fil des ans :<\/span><\/p><ul><li><b>AgileKDD<\/b><span style=\"font-weight: 400;\"> : cette m\u00e9thode est fond\u00e9e sur le cycle de vie OpenUp, fond\u00e9 sur quatre phases \u00e0 savoir inception, \u00e9laboration, construction et transition. Chaque phase contient une ou plusieurs it\u00e9rations divis\u00e9es en s\u00e9quences, sprints, planifi\u00e9es sur des d\u00e9lais fixes.<\/span><\/li><li><b>ASUM-DM<\/b><span> : d\u00e9velopp\u00e9e par IBM, la m\u00e9thode ASUM-DM est une extension \u00e0 CRISP-DM, combinant agilit\u00e9 et gestion de projet traditionnelle. Elle se d\u00e9roule en 3 phases principales qui sont une grande phase d\u2019analyse et compr\u00e9hension m\u00e9tier et donn\u00e9es, de design puis de configuration et construction, une phase de d\u00e9ploiement et une troisi\u00e8me phase d\u2019op\u00e9ration et optimisation.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ad6880 elementor-widget elementor-widget-heading\" data-id=\"2ad6880\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Les m\u00e9thodes propres aux projets Big Data<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c745b71 elementor-widget elementor-widget-text-editor\" data-id=\"c745b71\" 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; color: #000000;\">D\u2019autres m\u00e9thodes\u00a0 pour r\u00e9aliser des projets big data ont \u00e9galement \u00e9t\u00e9 d\u00e9velopp\u00e9es. C\u2019est le cas par exemple de la m\u00e9thode Stampede ou de la m\u00e9thode AABA.<\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">D\u00e9velopp\u00e9e par IBM pour ses clients, la m\u00e9thode <\/span><b>Stampede<\/b><span style=\"font-weight: 400;\"> s\u2019appuie sur la mise \u00e0 disposition de ressources d\u2019experts. Elle doit permettre \u00e0 toute entreprise de se lancer rapidement dans un projet g\u00e9n\u00e9rant de la valeur \u00e0 partir du big data. Stampede s\u2019applique dans le cadre d\u2019un projet pilote d\u00e9fini apr\u00e8s une s\u00e9ance de travail intensive d\u2019environ quatre mois servant \u00e0 d\u00e9terminer le projet big data, identifier les ressources n\u00e9cessaires, organiser un plan de travail et d\u00e9finir la valeur g\u00e9n\u00e9r\u00e9e pour l\u2019entreprise.<\/span><\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">La m\u00e9thode <\/span><b>AABA<\/b><span style=\"font-weight: 400;\">, pour Architecture-centric Agile Big Data Analytics, est centr\u00e9e sur le mod\u00e8le DevOPs. Elle int\u00e8gre \u00e0 la fois une architecture AAA ainsi que la conception base de donn\u00e9es du syst\u00e8me big data. Gr\u00e2ce \u00e0 son agilit\u00e9, la m\u00e9thode AABA permet de s\u2019adapter rapidement \u00e0 l\u2019\u00e9volution des besoins et des technologies.<\/span><\/span><\/p><p><span style=\"font-weight: 400; color: #000000;\">Comme on peut le voir, il n\u2019y a pas une seule m\u00e9thode big data\u00a0 id\u00e9ale pour chaque projet. Les expertises m\u00e9tiers \u00e9tant diff\u00e9rentes dans chaque entreprise, il n\u2019est pas toujours facile de trouver la m\u00e9thode projet big data qui s\u2019adapte \u00e0 chaque situation. Chaque m\u00e9thode big data a ses avantages et ses inconv\u00e9nients, l\u2019essentiel r\u00e9side dans l\u2019implication des forces en pr\u00e9sence et de la capacit\u00e9 des \u00e9quipes, aussi bien techniques que m\u00e9tiers, \u00e0 s\u2019adapter et \u00e9voluer dans le but d\u2019am\u00e9liorer sans cesse le projet final en cr\u00e9ant de la valeur.<\/span><\/p><p><span style=\"color: #000000;\"><span style=\"font-weight: 400;\">Si vous \u00eates int\u00e9rress\u00e9 par les projets big data ou bien la data science de fa\u00e7on plus globale, n\u2019h\u00e9sitez pas \u00e0 contacter notre \u00e9quipe pour discuter du <a href=\"https:\/\/datascientest.com\/data-engineer-must-have-skills\">m\u00e9tier de Data Engineer<\/a><\/span><\/span><span style=\"font-weight: 400;\"><span style=\"color: #000000;\">, un m\u00e9tier qui traite toutes ces probl\u00e9matiques !<\/span> <\/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-cc25697 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"cc25697\" 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\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>La num\u00e9risation de la donn\u00e9e et l\u2019essor du big data ont provoqu\u00e9 un afflux massif d\u2019informations au sein des entreprises. Beaucoup d\u2019informations produites mais aussi beaucoup d\u2019informations \u00e0 traiter. Pourtant cette masse de donn\u00e9es repr\u00e9sente un v\u00e9ritable enjeu \u00e9conomique et strat\u00e9gique pour les entreprises, \u00e0 condition de savoir bien les traiter et les utiliser en [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":46607,"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>Les m\u00e9thodes propres aux projets big data - multi.datascientest.com<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez quelques m\u00e9thodes propres et op\u00e9rationnelles qui vont vous permettre de mener \u00e0 bien tous vos projets big data.\" \/>\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\/les-methodes-propres-aux-projets-big-data\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Les m\u00e9thodes propres aux projets big data\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/les-methodes-propres-aux-projets-big-data\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2021-02-08T09:30:15+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T14:10:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/02\/illu_methode_bigdata_blog-94.png\" \/>\n\t<meta property=\"og:image:width\" content=\"4268\" \/>\n\t<meta property=\"og:image:height\" content=\"2342\" \/>\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=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/les-methodes-propres-aux-projets-big-data\",\"url\":\"https:\/\/datascientest.com\/les-methodes-propres-aux-projets-big-data\",\"name\":\"Les m\u00e9thodes propres aux projets big data - multi.datascientest.com\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2021-02-08T09:30:15+00:00\",\"dateModified\":\"2023-11-09T14:10:08+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"D\u00e9couvrez quelques m\u00e9thodes propres et op\u00e9rationnelles qui vont vous permettre de mener \u00e0 bien tous vos projets big data.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/les-methodes-propres-aux-projets-big-data\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\",\"name\":\"Raphael Kassel\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/raph.jpeg\",\"caption\":\"Raphael Kassel\"},\"description\":\"Raphael Kassel est le Head of Data Programs chez DataScientest. 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