{"id":168677,"date":"2023-06-15T14:44:09","date_gmt":"2023-06-15T13:44:09","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=168677"},"modified":"2023-10-30T19:37:18","modified_gmt":"2023-10-30T18:37:18","slug":"all-about-data-pipeline","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-data-pipeline","title":{"rendered":"Pipeline: Definition, operation and usefulness in Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"168677\" class=\"elementor elementor-168677\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-357f19f e-flex e-con-boxed e-con\" data-id=\"357f19f\" 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-4ba7f80 elementor-widget elementor-widget-heading\" data-id=\"4ba7f80\" 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\">What is a pipeline ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-35800c8 elementor-widget elementor-widget-text-editor\" data-id=\"35800c8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><span style=\"font-weight: 400;\">A pipeline is a <\/span><b>set of processes and tools<\/b><span style=\"font-weight: 400;\"> used to collect raw data from multiple sources, analyze it and present the results in an understandable format. Companies use data pipelines to <\/span><b>answer specific business questions<\/b><span style=\"font-weight: 400;\"> and make strategic decisions based on real data. All available <\/span><b>data sets<\/b><span style=\"font-weight: 400;\"> (internal or external) are analyzed to obtain this information.<\/span><\/p><p><span style=\"font-weight: 400;\">For example, your sales team wants to set realistic targets for the next quarter. The pipeline enables them to gather data from customer surveys or feedback, order history, industry trends, etc. <\/span><b>Robust analysis tools<\/b><span style=\"font-weight: 400;\"> will then enable an in-depth study of the data, identifying key trends and patterns. Teams can then create specific, <\/span><a href=\"https:\/\/datascientest.com\/en\/data-driven-definition-benefits-and-methods\"><span style=\"font-weight: 400;\">data-driven objectives<\/span><\/a><span style=\"font-weight: 400;\"> that will increase sales.<\/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-a90db09 e-flex e-con-boxed e-con\" data-id=\"a90db09\" 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-916c569 elementor-widget elementor-widget-heading\" data-id=\"916c569\" 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\">Data Science pipeline vs. ETL pipeline<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e2e3a0 elementor-widget elementor-widget-text-editor\" data-id=\"3e2e3a0\" 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;\">Although the terms &#8220;Data Science pipelines&#8221; and &#8220;ETL pipelines&#8221; both refer to the <\/span><b>process of transferring data<\/b><span style=\"font-weight: 400;\"> from one system to another, there are key differences between the two:<\/span><\/p><p><span style=\"font-weight: 400;\">The ETL pipeline stops when the data is loaded into a <\/span><a href=\"https:\/\/datascientest.com\/en\/data-warehouse-2\"><span style=\"font-weight: 400;\">data warehouse<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/datascientest.com\/en\/database-what-is-it\"><span style=\"font-weight: 400;\">database<\/span><\/a><span style=\"font-weight: 400;\">. The Data Science pipeline does not stop at this stage and includes additional steps such as Feature Engineering or <\/span><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">ETL pipelines always involve a data transformation step (ETL stands for <\/span><b>Extract Transform Load<\/b><span style=\"font-weight: 400;\">), unlike Data Science pipelines, where most of the steps are carried out with raw data.<\/span><\/p><p><span style=\"font-weight: 400;\">Data Science pipelines generally run in real-time, whereas ETL pipelines transfer <\/span><b>data in blocks<\/b><span style=\"font-weight: 400;\"> or at regular intervals.<\/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-641e9f0 elementor-widget__width-initial elementor-widget elementor-widget-image\" data-id=\"641e9f0\" 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\/en\/files\/2023\/06\/etl-data-pipeline.jpg\" class=\"attachment-large size-large wp-image-168679\" alt=\"etl-data-pipeline\" srcset=\"https:\/\/datascientest.com\/en\/files\/2023\/06\/etl-data-pipeline.jpg 800w, https:\/\/datascientest.com\/en\/files\/2023\/06\/etl-data-pipeline-300x131.jpg 300w, https:\/\/datascientest.com\/en\/files\/2023\/06\/etl-data-pipeline-768x336.jpg 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<div class=\"elementor-element elementor-element-2ac89f3 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"2ac89f3\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\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\">Learn to use pipeline in Data Science<\/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<div class=\"elementor-element elementor-element-f5f2ffb e-flex e-con-boxed e-con\" data-id=\"f5f2ffb\" 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-bdfd775 elementor-widget elementor-widget-heading\" data-id=\"bdfd775\" 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\">Why is the Data Science pipeline important?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ef58a74 elementor-widget elementor-widget-text-editor\" data-id=\"ef58a74\" 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;\">Companies create billions of pieces of data every day, and each of these contains valuable information. The <\/span><b>Data Science pipeline<\/b><span style=\"font-weight: 400;\"> makes the most of this information by bringing together data from all teams, cleansing it, and presenting it in an easily digestible way. This enables you to make rapid, data-driven decisions.<\/span><\/p><p><span style=\"font-weight: 400;\">Data Science pipelines enable you to avoid the tedious and error-prone process of manual data collection. By using intelligent <\/span><b>data ingestion tools<\/b><span style=\"font-weight: 400;\"> (such as Talend or Fivetran), you&#8217;ll have constant access to clean, reliable, and up-to-date data, essential for staying ahead of the competition.<\/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-67de5f8 e-flex e-con-boxed e-con\" data-id=\"67de5f8\" 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-b4721f1 elementor-widget elementor-widget-heading\" data-id=\"b4721f1\" 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\">Benefits of Data Science Pipelines\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-81c1a2a elementor-widget elementor-widget-text-editor\" data-id=\"81c1a2a\" 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 style=\"font-weight: 400;\" aria-level=\"1\"><b>Increase agility<\/b><span style=\"font-weight: 400;\"> to meet changing business needs and customer preferences.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Simplify<\/b><span style=\"font-weight: 400;\"> access to company and customer information.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Accelerate<\/b><span style=\"font-weight: 400;\"> decision-making.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Eliminate data silos<\/b><span style=\"font-weight: 400;\"> and bottlenecks that delay action and waste resources.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Simplify<\/b><span style=\"font-weight: 400;\"> and accelerate the data analysis process.<\/span><\/li><\/ol>\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-7cf11c4 e-flex e-con-boxed e-con\" data-id=\"7cf11c4\" 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-5cfe4e4 elementor-widget elementor-widget-heading\" data-id=\"5cfe4e4\" 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\">How does a Data Science pipeline work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-91c0cd6 elementor-widget elementor-widget-text-editor\" data-id=\"91c0cd6\" 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;\">Before moving <\/span><b>raw data<\/b><span style=\"font-weight: 400;\"> into the pipeline, it&#8217;s essential to identify the specific questions you want the data to answer. This helps users to focus on the data of interest in order to obtain the appropriate information.<\/span><\/p><p><span style=\"font-weight: 400;\">The Data Science pipeline is made up of <\/span><b>several stages<\/b><span style=\"font-weight: 400;\">, including :<\/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-a9ec9f3 elementor-widget elementor-widget-heading\" data-id=\"a9ec9f3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Obtaining the data<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-799901d elementor-widget elementor-widget-text-editor\" data-id=\"799901d\" 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;\">This is where data from<\/span><b> internal, external, and third-party sources<\/b><span style=\"font-weight: 400;\"> is collected and transformed into a usable format (XML, JSON, .csv, etc.).<\/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-92989d2 elementor-widget elementor-widget-heading\" data-id=\"92989d2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data cleansing\n<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-27da6e4 elementor-widget elementor-widget-text-editor\" data-id=\"27da6e4\" 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;\">This is the most time-consuming stage of the process. Data may contain anomalies such as duplicate parameters, missing values, or irrelevant information, which need to be cleaned up before creating a <\/span><a href=\"https:\/\/datascientest.com\/en\/dataviz-definition-objectives-and-uses\"><span style=\"font-weight: 400;\">data visualization<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">This step can be divided into two categories:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Examination of the data<\/b><span style=\"font-weight: 400;\"> to identify errors, missing values, or corrupted records.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data cleansing, which involves filling in gaps, correcting errors, removing duplicates, and deleting irrelevant records or information.<\/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-a5f0810 elementor-widget elementor-widget-heading\" data-id=\"a5f0810\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data exploration and modeling<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f9744af elementor-widget elementor-widget-text-editor\" data-id=\"f9744af\" 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;\">Once data has been thoroughly cleansed, it can then be used to identify patterns. This is where <\/span><b>Machine Learning<\/b><span style=\"font-weight: 400;\"> tools come in. These tools will help you find patterns and apply specific rules to <\/span><b>data or data models<\/b><span style=\"font-weight: 400;\">. These rules can then be tested on sample data to determine how performance, revenue, or growth would be affected.<\/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-6270aee elementor-widget elementor-widget-heading\" data-id=\"6270aee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data interpretation<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6394f2 elementor-widget elementor-widget-text-editor\" data-id=\"d6394f2\" 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;\">The aim of this step is first to identify the information and correlate it with the results in your data. You can then <\/span><b>communicate your results<\/b><span style=\"font-weight: 400;\"> to business leaders or colleagues using graphs, dashboards, or reports.<\/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-53290c9 elementor-widget elementor-widget-heading\" data-id=\"53290c9\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h5 class=\"elementor-heading-title elementor-size-default\">Data review<\/h5>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d834f4 elementor-widget elementor-widget-text-editor\" data-id=\"4d834f4\" 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;\">As business requirements change, or as more data becomes available, it&#8217;s important to review your model periodically and make revisions as necessary.<\/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-0757332 e-flex e-con-boxed e-con\" data-id=\"0757332\" 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-42b5e3c elementor-widget elementor-widget-heading\" data-id=\"42b5e3c\" 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-8e9c7b9 elementor-widget elementor-widget-text-editor\" data-id=\"8e9c7b9\" 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;\">In this article, we have described the use of pipelines in the <\/span><b>Data industry<\/b><span style=\"font-weight: 400;\">. As a <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-course\"><span style=\"font-weight: 400;\">Data Engineer<\/span><\/a><span style=\"font-weight: 400;\"> or Analytics Engineer, the creation and maintenance of data pipelines are necessary to guarantee their quality and availability for the creation of Machine Learning models, or in a <\/span><a href=\"https:\/\/datascientest.com\/en\/introduction-to-business-intelligence\"><span style=\"font-weight: 400;\">Business Intelligence approach<\/span><\/a><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<div class=\"elementor-element elementor-element-14381d2 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"14381d2\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-engineer-course\">\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\">Start a Data Engineer training<\/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<div class=\"elementor-element elementor-element-35552f4 e-flex e-con-boxed e-con\" data-id=\"35552f4\" 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-6100e33 elementor-widget elementor-widget-html\" data-id=\"6100e33\" 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\": \"What is a pipeline ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"A pipeline is a set of processes and tools used to collect raw data from multiple sources, analyze it and present the results in an understandable format. 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A pipeline is a set of processes and tools used to collect raw data from multiple sources, analyze it and present the results in an understandable format. Companies use data pipelines to answer specific business questions and make strategic decisions based on real data. All available data sets (internal or [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":168678,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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: Definition, operation and usefulness in Data Science<\/title>\n<meta name=\"description\" content=\"A pipeline is a set of processes and tools used to collect raw data from multiple sources, analyze it and present the results in an understandable format.\" \/>\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\/en\/all-about-data-pipeline\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pipeline: Definition, operation and usefulness in Data Science\" \/>\n<meta property=\"og:description\" content=\"What is a pipeline ? 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