{"id":181724,"date":"2022-11-22T14:26:45","date_gmt":"2022-11-22T14:26:45","guid":{"rendered":"https:\/\/datascientest.com\/?p=181724"},"modified":"2023-11-09T13:55:50","modified_gmt":"2023-11-09T13:55:50","slug":"pipeline-de-donnees","status":"publish","type":"post","link":"https:\/\/datascientest.com\/pipeline-de-donnees","title":{"rendered":"Pipeline : D\u00e9finition, fonctionnement et utilit\u00e9 en Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"181724\" class=\"elementor elementor-181724\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ef29e86 e-flex e-con-boxed e-con\" data-id=\"ef29e86\" 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-c289d6c elementor-widget elementor-widget-text-editor\" data-id=\"c289d6c\" 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<h2>Un pipeline est un ensemble de processus et d\u2019outils utilis\u00e9s pour collecter des donn\u00e9es brutes \u00e0 partir de plusieurs sources, les analyser et pr\u00e9senter les r\u00e9sultats dans un format compr\u00e9hensible. Les entreprises utilisent les pipelines de donn\u00e9es pour r\u00e9pondre \u00e0 des questions commerciales sp\u00e9cifiques et prendre des d\u00e9cisions strat\u00e9giques bas\u00e9es sur des donn\u00e9es r\u00e9elles. Tous les sets de donn\u00e9es disponibles, (internes ou externes), sont analys\u00e9s pour obtenir ces informations.<\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3dce89f elementor-widget elementor-widget-text-editor\" data-id=\"3dce89f\" 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>Par exemple, votre \u00e9quipe commerciale souhaite se fixer des objectifs r\u00e9alistes pour le prochain trimestre. Le pipeline leur permet de recueillir des donn\u00e9es \u00e0 partir d&rsquo;enqu\u00eates ou de commentaires de clients, d&rsquo;historiques de commandes, de tendances du secteur, etc. Des <strong>outils d&rsquo;analyse robustes<\/strong> vont alors permettre d\u2019\u00e9tudier en profondeur les donn\u00e9es et d\u2019identifier les tendances et les mod\u00e8les cl\u00e9s. Les \u00e9quipes peuvent ensuite <strong>cr\u00e9er des objectifs sp\u00e9cifiques, bas\u00e9s sur les donn\u00e9es<\/strong>, qui augmenteront les ventes.<\/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-cf5ffa5 e-flex e-con-boxed e-con\" data-id=\"cf5ffa5\" 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-c09ee19 elementor-widget elementor-widget-heading\" data-id=\"c09ee19\" 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><h3 class=\"elementor-heading-title elementor-size-default\">Data Science pipeline vs pipeline ETL<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47d9435 elementor-widget elementor-widget-text-editor\" data-id=\"47d9435\" 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>Bien que les termes \u00ab\u00a0Data Science pipelines\u00a0\u00bb et \u00ab\u00a0pipelines ETL\u00a0\u00bb font tous deux r\u00e9f\u00e9rence au processus de <strong>transfert de donn\u00e9es d&rsquo;un syst\u00e8me \u00e0 un autre<\/strong>, il existe des diff\u00e9rences essentielles entre les deux :<\/p><ul><li>Le pipeline <a href=\"https:\/\/datascientest.com\/extract-transform-load-tout-savoir\">ETL<\/a> s&rsquo;arr\u00eate lorsque les donn\u00e9es sont charg\u00e9es dans un entrep\u00f4t de donn\u00e9es (<a href=\"https:\/\/datascientest.com\/data-warehouse\">Data Warehouse<\/a>) ou une base de donn\u00e9es. Le Data Science pipeline ne s\u2019arr\u00eate pas \u00e0 cette \u00e9tape et inclut des \u00e9tapes suppl\u00e9mentaires telles que du <strong>Feature Engineering<\/strong> ou du <strong>Machine Learning<\/strong>.<\/li><li>Les pipelines ETL impliquent toujours une <strong>\u00e9tape de transformation des donn\u00e9es<\/strong> (ETL signifie Extract Transform Load), contrairement aux Data Science pipelines pour lesquels l\u2019essentiel des \u00e9tapes se fait avec les donn\u00e9es brutes.<\/li><li>Les Data Science pipelines s&rsquo;ex\u00e9cutent g\u00e9n\u00e9ralement en temps r\u00e9el, tandis que les pipelines ETL transf\u00e8rent les donn\u00e9es <strong>par blocs ou \u00e0 des intervalles de temps<\/strong> r\u00e9guliers.<\/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-bf71ea0 elementor-widget elementor-widget-image\" data-id=\"bf71ea0\" 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=\"350\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/etl-pipeline-de-donnees.png\" class=\"attachment-full size-full wp-image-181733\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/etl-pipeline-de-donnees.png 800w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/etl-pipeline-de-donnees-300x131.png 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/etl-pipeline-de-donnees-768x336.png 768w\" 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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6f456ab e-flex e-con-boxed e-con\" data-id=\"6f456ab\" 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-12c3ad4 elementor-widget elementor-widget-heading\" data-id=\"12c3ad4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Pourquoi le Data Science pipeline est-il important ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0fceff9 elementor-widget elementor-widget-text-editor\" data-id=\"0fceff9\" 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 entreprises cr\u00e9ent <strong>des milliards de donn\u00e9es chaque jour<\/strong> et chacune de ces donn\u00e9es contient des informations exploitables. Le <a href=\"https:\/\/datascientest.com\/integrer-la-data-science-dans-votre-strategie-marketing\">Data Science<\/a> pipeline permet de tirer le maximum d\u2019information en rassemblant les donn\u00e9es de toutes les \u00e9quipes, en les nettoyant et en les pr\u00e9sentant de mani\u00e8re facilement assimilable. Cela permet de <strong>prendre des d\u00e9cisions rapides<\/strong> \u00e9tay\u00e9es par des donn\u00e9es.<\/p><p>Les Data Science pipelines vous permettent d&rsquo;<strong>\u00e9viter le processus fastidieux et sujet aux erreurs de collecte manuelle des donn\u00e9es<\/strong>. En utilisant des outils intelligents d\u2019ingestion de donn\u00e9es (tels que Talend ou Fivetran), vous aurez un acc\u00e8s constant \u00e0 des donn\u00e9es propres, fiables et mises \u00e0 jour, essentielles pour garder une longueur d&rsquo;avance sur la concurrence.<\/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-4db98b2 e-flex e-con-boxed e-con\" data-id=\"4db98b2\" 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-e0e1138 elementor-widget elementor-widget-heading\" data-id=\"e0e1138\" 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\">Avantages des Data Science pipelines<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a6caf33 elementor-widget elementor-widget-text-editor\" data-id=\"a6caf33\" 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<ol><li>Augmenter l&rsquo;agilit\u00e9 pour r\u00e9pondre aux besoins changeants de l&rsquo;entreprise et aux pr\u00e9f\u00e9rences des clients.<\/li><li>Simplifier l&rsquo;acc\u00e8s aux informations sur l&rsquo;entreprise et les clients.<\/li><li>Acc\u00e9l\u00e9rer le processus de prise de d\u00e9cision.<\/li><li>\u00c9liminer les silos de donn\u00e9es et les goulots d&rsquo;\u00e9tranglement qui retardent l&rsquo;action et gaspillent les ressources.<\/li><li>Simplifier et acc\u00e9l\u00e9rer le processus d&rsquo;analyse des donn\u00e9es.<\/li><\/ol>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96bf949 elementor-widget elementor-widget-image\" data-id=\"96bf949\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"387\" height=\"433\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/Fichier-82-8-1.png\" class=\"attachment-large size-large wp-image-184952\" alt=\"Les avantages des pipelines de donn\u00e9es\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/Fichier-82-8-1.png 387w, https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/Fichier-82-8-1-268x300.png 268w\" sizes=\"(max-width: 387px) 100vw, 387px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b1d7d8b e-flex e-con-boxed e-con\" data-id=\"b1d7d8b\" 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-3a01b39 elementor-widget elementor-widget-heading\" data-id=\"3a01b39\" 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 un Data Science pipeline ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb3e45a elementor-widget elementor-widget-text-editor\" data-id=\"cb3e45a\" 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>Avant de d\u00e9placer des donn\u00e9es brutes dans le pipeline, il est essentiel d&rsquo;identifier les questions sp\u00e9cifiques auxquelles vous souhaitez que les donn\u00e9es r\u00e9pondent. Cela aide les utilisateurs \u00e0 se concentrer sur les donn\u00e9es int\u00e9ressantes pour obtenir les informations appropri\u00e9es.<\/p><p>Le Data Science pipeline est compos\u00e9 de plusieurs \u00e9tapes qui incluent :<\/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-d5d68c8 elementor-widget elementor-widget-heading\" data-id=\"d5d68c8\" 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\"><strong>L\u2019obtention des donn\u00e9es<\/strong><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d84baf5 elementor-widget elementor-widget-text-editor\" data-id=\"d84baf5\" 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>C&rsquo;est \u00e0 cette \u00e9tape que les donn\u00e9es provenant de <strong>sources internes, externes et tierces<\/strong> sont collect\u00e9es et transform\u00e9es dans un format utilisable (XML, JSON, .csv, etc.).<\/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-66eb39a elementor-widget elementor-widget-heading\" data-id=\"66eb39a\" 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\"><strong>Le nettoyage des donn\u00e9es<\/strong><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-577cc2a elementor-widget elementor-widget-text-editor\" data-id=\"577cc2a\" 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>C&rsquo;est l&rsquo;\u00e9tape la plus chronophage du processus. Les donn\u00e9es peuvent contenir des anomalies telles que des param\u00e8tres en double, des valeurs manquantes ou des informations non pertinentes qui doivent \u00eatre nettoy\u00e9es avant de <a href=\"https:\/\/datascientest.com\/formation-datavisualisation\">cr\u00e9er une visualisation de donn\u00e9es<\/a>.<\/p><p>Cette \u00e9tape peut \u00eatre divis\u00e9e en deux cat\u00e9gories :<\/p><ul><li>Examen des donn\u00e9es pour identifier les erreurs, les valeurs manquantes ou les enregistrements corrompus.<\/li><li>Nettoyage des donn\u00e9es, ce qui implique de combler les manques, de corriger les erreurs, de supprimer les doublons et de supprimer les enregistrements ou informations non pertinentes.<\/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-714184a elementor-widget elementor-widget-heading\" data-id=\"714184a\" 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\"><strong>L\u2019exploration et la mod\u00e9lisation des donn\u00e9es<\/strong><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1047b7 elementor-widget elementor-widget-text-editor\" data-id=\"a1047b7\" 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>Une fois les donn\u00e9es soigneusement nettoy\u00e9es, elles peuvent ensuite \u00eatre utilis\u00e9es pour identifier des patterns. C&rsquo;est l\u00e0 que les outils de Machine Learning entrent en jeu. Ces outils vous aideront \u00e0 <strong>trouver des mod\u00e8les et appliquer des r\u00e8gles sp\u00e9cifiques aux donn\u00e9es ou aux mod\u00e8les de donn\u00e9es<\/strong>. Ces r\u00e8gles peuvent ensuite \u00eatre test\u00e9es sur des exemples de donn\u00e9es pour d\u00e9terminer comment les performances, les revenus ou la croissance seraient affect\u00e9s.<\/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-6e2229f elementor-widget elementor-widget-heading\" data-id=\"6e2229f\" 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\"><strong>L\u2019interpr\u00e9tation des donn\u00e9es<\/strong><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9ac0dba elementor-widget elementor-widget-text-editor\" data-id=\"9ac0dba\" 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;objectif de cette \u00e9tape est d&rsquo;abord d&rsquo;identifier les informations et de les corr\u00e9ler aux r\u00e9sultats de vos donn\u00e9es. Vous pouvez ensuite communiquer vos r\u00e9sultats aux chefs d&rsquo;entreprise ou \u00e0 vos coll\u00e8gues \u00e0 l&rsquo;aide de graphiques, de tableaux de bord ou de rapports.<\/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-e507e9e elementor-widget elementor-widget-heading\" data-id=\"e507e9e\" 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\"><strong>La r\u00e9vision des donn\u00e9es<\/strong><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37592a3 elementor-widget elementor-widget-text-editor\" data-id=\"37592a3\" 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>\u00c0 mesure que les exigences de l&rsquo;entreprise changent ou que davantage de donn\u00e9es deviennent disponibles, il est important de revoir p\u00e9riodiquement votre mod\u00e8le et d&rsquo;apporter des r\u00e9visions si n\u00e9cessaire.<\/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-3e222f1 e-flex e-con-boxed e-con\" data-id=\"3e222f1\" 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-ec81332 elementor-widget elementor-widget-heading\" data-id=\"ec81332\" 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\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1609e97 elementor-widget elementor-widget-text-editor\" data-id=\"1609e97\" 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 cet article, nous avons d\u00e9crit l\u2019utilisation des pipelines dans le secteur de la Data. En tant que <a href=\"https:\/\/datascientest.com\/data-engineer-tout-savoir\">Data Engineer<\/a> ou <strong>Analytics Engineer<\/strong>, la cr\u00e9ation et l\u2019entretien des pipelines de donn\u00e9es sont n\u00e9cessaires pour garantir leur qualit\u00e9 et leur disponibilit\u00e9 pour la <strong>cr\u00e9ation de mod\u00e8les de<\/strong> <a href=\"https:\/\/datascientest.com\/le-machine-learning-au-service-de-la-cybersecurite-pour-la-detection-de-spams-dans-les-e-mails\">Machine Learning<\/a>, ou encore dans une d\u00e9marche de Business Intelligence.<\/p><p>Pour d\u00e9couvrir les m\u00e9tiers de Data Engineer et Analytics Engineer et <strong>les parcours de formation<\/strong> que nous proposons, n\u2019h\u00e9sitez pas \u00e0 consulter nos pages d\u00e9di\u00e9es.<\/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-7b823e3 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7b823e3\" 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\">D\u00e9couvrir nos formations en data<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Un pipeline est un ensemble de processus et d\u2019outils utilis\u00e9s pour collecter des donn\u00e9es brutes \u00e0 partir de plusieurs sources, les analyser et pr\u00e9senter les r\u00e9sultats dans un format compr\u00e9hensible. Les entreprises utilisent les pipelines de donn\u00e9es pour r\u00e9pondre \u00e0 des questions commerciales sp\u00e9cifiques et prendre des d\u00e9cisions strat\u00e9giques bas\u00e9es sur des donn\u00e9es r\u00e9elles. Tous [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":184951,"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>Pipeline : D\u00e9finition, fonctionnement et utilit\u00e9 en Data Science<\/title>\n<meta name=\"description\" content=\"Essentiels aux entreprises, il est important pour tout futur expert de la data d&#039;avoir des notions sur les pipelines de donn\u00e9es. D\u00e9couvrez tout ce qu&#039;il y a \u00e0 savoir sur le sujet dans cet article.\" \/>\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\/pipeline-de-donnees\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pipeline : D\u00e9finition, fonctionnement et utilit\u00e9 en Data Science\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/pipeline-de-donnees\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2022-11-22T14:26:45+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-11-09T13:55:50+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/11\/Illu_BLOG_pipeline-1.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=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/pipeline-de-donnees\",\"url\":\"https:\/\/datascientest.com\/pipeline-de-donnees\",\"name\":\"Pipeline : D\u00e9finition, fonctionnement et utilit\u00e9 en Data Science\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2022-11-22T14:26:45+00:00\",\"dateModified\":\"2023-11-09T13:55:50+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Essentiels aux entreprises, il est important pour tout futur expert de la data d'avoir des notions sur les pipelines de donn\u00e9es. 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