{"id":199492,"date":"2025-10-23T06:22:00","date_gmt":"2025-10-23T05:22:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=199492"},"modified":"2025-11-03T18:22:52","modified_gmt":"2025-11-03T17:22:52","slug":"all-about-data-modeling","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-data-modeling","title":{"rendered":"Data Modeling: What is it? How to use it?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"199492\" class=\"elementor elementor-199492\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-13e45ff e-flex e-con-boxed e-con\" data-id=\"13e45ff\" 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-a2c43f1 elementor-widget elementor-widget-text-editor\" data-id=\"a2c43f1\" 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><b>Data Modeling is a frequently underestimated yet crucial step for the success of any data project. Discover what data modeling involves, the major types of models, and the best tools to use!<\/b><\/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-11f4b34 elementor-widget elementor-widget-text-editor\" data-id=\"11f4b34\" 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;\">Collecting data is one thing, but the most important part is understanding it. However, raw data is often as valuable as it is indigestible. Structuring it to make it intelligible, reliable, and scalable? That&#8217;s where it all truly begins. That is the role of <\/span><b>Data Modeling<\/b><span style=\"font-weight: 400;\">. It transforms chaotic streams into coherent, readable, and most importantly, durable schemas.<\/span><\/p><p><span style=\"font-weight: 400;\">Whether it\u2019s to <\/span><a href=\"https:\/\/datascientest.com\/en\/dashboard-what-is-it\"><span style=\"font-weight: 400;\">feed a BI dashboard<\/span><\/a><span style=\"font-weight: 400;\">, design a <\/span><b>solid relational database<\/b><span style=\"font-weight: 400;\">, or structure an <\/span><a href=\"https:\/\/datascientest.com\/en\/data-warehouse-definition\"><span style=\"font-weight: 400;\">AI-oriented data warehouse<\/span><\/a><span style=\"font-weight: 400;\">, it&#8217;s an essential step. Like an architectural plan, a <\/span><b>good data model<\/b><span style=\"font-weight: 400;\"> isn&#8217;t visible directly, but it determines the entire subsequent project.<\/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-40e0623 e-flex e-con-boxed e-con\" data-id=\"40e0623\" 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-ded3f5a elementor-widget elementor-widget-heading\" data-id=\"ded3f5a\" 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-large\">What is data modeling?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a97ce46 elementor-widget elementor-widget-text-editor\" data-id=\"a97ce46\" 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;\">Data Modeling involves formally representing the <\/span><b>logical structures of data<\/b><span style=\"font-weight: 400;\"> used in an information system. In other words, it\u2019s a way to map out data, their relationships, and how they will be organized, stored, and manipulated. This representation unfolds over several levels.<\/span><\/p><p><b>The conceptual model<\/b><span style=\"font-weight: 400;\">, the most abstract, is used to represent business entities (customers, orders, products\u2026) and their relationships, without technical constraints. It\u2019s the \u201cbusiness language.\u201d <\/span><b>The logical model<\/b><span style=\"font-weight: 400;\">, on the other hand, structures the data more rigorously. It complies with the rules of a management system like <\/span><a href=\"https:\/\/datascientest.com\/en\/sql-learn-all-about-the-programming-language-for-databases\"><span style=\"font-weight: 400;\">SQL<\/span><\/a><span style=\"font-weight: 400;\">. Types, relationships, and cardinalities are defined&#8230;<\/span><\/p><p><span style=\"font-weight: 400;\">With <\/span><b>the physical model<\/b><span style=\"font-weight: 400;\">, we move to concrete implementation. Column names, indexes, primary and foreign keys\u2026 this is what will be truly deployed in the database. Each stage has a specific role. <\/span><b>The conceptual model<\/b><span style=\"font-weight: 400;\"> allows business teams and tech teams to understand each other.<\/span><\/p><p><b>The logical model<\/b><span style=\"font-weight: 400;\"> brings coherency and robustness. The physical model optimizes performance and maintenance. Without Data Modeling, <a href=\"https:\/\/datascientest.com\/en\/database-types-how-to-make-the-right-choice\">a database<\/a> can quickly become an illegible patchwork, difficult to maintain, and a source of <\/span><b>costly errors<\/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<div class=\"elementor-element elementor-element-a3e6f9a elementor-widget elementor-widget-image\" data-id=\"a3e6f9a\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-5.webp\" class=\"attachment-full size-full wp-image-199495\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-5.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-5-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-5-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-5-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-1ff5402 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"1ff5402\" 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\/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\">Learn everything about Data Modeling<\/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-ef8f451 e-flex e-con-boxed e-con\" data-id=\"ef8f451\" 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-053acd2 elementor-widget elementor-widget-heading\" data-id=\"053acd2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Major modeling approaches<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c3998a5 elementor-widget elementor-widget-text-editor\" data-id=\"c3998a5\" 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;\">Not all data models are alike. Depending on the use case (analytical, operational, <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-non-relational-databases\"><span style=\"font-weight: 400;\">NoSQL<\/span><\/a><span style=\"font-weight: 400;\">&#8230;), <\/span><b>different approaches<\/b><span style=\"font-weight: 400;\"> have emerged.<\/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-1e9083b elementor-widget elementor-widget-heading\" data-id=\"1e9083b\" 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\">The ERD model<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3c927fc elementor-widget elementor-widget-text-editor\" data-id=\"3c927fc\" 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 classic among classics is <\/span><b>the Entity-Relationship Diagram model<\/b><span style=\"font-weight: 400;\">. It is used to graphically represent entities (like &#8220;User&#8221;, &#8220;Order&#8221;) and their relationships.<\/span><\/p><p><span style=\"font-weight: 400;\">For instance: &#8220;a user can place multiple orders&#8221;. It allows for <\/span><b>modeling business rules<\/b><span style=\"font-weight: 400;\"> clearly, even before considering the underlying technology.<\/span><\/p><p><span style=\"text-decoration: underline;\"><b>Good to know<\/b><\/span><span style=\"font-weight: 400;\">: this model is often used to create the conceptual model, the first cornerstone of a good data project.<\/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-40e002c elementor-widget elementor-widget-heading\" data-id=\"40e002c\" 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\">The relational model<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7f87f9 elementor-widget elementor-widget-text-editor\" data-id=\"b7f87f9\" 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;\">With <\/span><b>the relational model<\/b><span style=\"font-weight: 400;\">, entities become tables, attributes become columns, and relationships are ensured by primary and foreign keys. This is the dominant approach in <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-pl-sql\"><span style=\"font-weight: 400;\">SQL databases<\/span><\/a><span style=\"font-weight: 400;\">. Solid, time-tested, but rigid: it imposes a strict structure that must be carefully planned in advance.<\/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-927b66c elementor-widget elementor-widget-heading\" data-id=\"927b66c\" 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\">The dimensional model<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ccf615d elementor-widget elementor-widget-text-editor\" data-id=\"ccf615d\" 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;\">On the other hand, <\/span><b>the dimensional model<\/b><span style=\"font-weight: 400;\"> is designed for <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-dimension-tables\"><span style=\"font-weight: 400;\">Data Warehouses<\/span><\/a><span style=\"font-weight: 400;\">. It is based on <\/span><b>the separation between facts and dimensions<\/b><span style=\"font-weight: 400;\">. Two schemas are popular. First, <\/span><b>the star schema<\/b><span style=\"font-weight: 400;\">, with a central fact table (for example &#8220;Sales&#8221;), linked to dimension tables (Client, Product, Time\u2026). But also its more normalized version, <\/span><b>the snowflake schema<\/b><span style=\"font-weight: 400;\">, where dimensions themselves are decomposed.<\/span><\/p><p><span style=\"font-weight: 400;\">This type of modeling is widely used in <a href=\"https:\/\/datascientest.com\/en\/business-intelligence-for-digital-marketing\">BI<\/a>, as it facilitates <\/span><b>analytical queries<\/b><span style=\"font-weight: 400;\"> and optimizes performance in <\/span><b>reporting tools<\/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<div class=\"elementor-element elementor-element-ab717be elementor-widget elementor-widget-heading\" data-id=\"ab717be\" 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\">The NoSQL model<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb227f5 elementor-widget elementor-widget-text-editor\" data-id=\"cb227f5\" 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;\">However, in the realm of <a href=\"https:\/\/datascientest.com\/en\/all-about-non-relational-databases\">NoSQL databases<\/a>, it\u2019s <\/span><b>the document-oriented model<\/b><span style=\"font-weight: 400;\"> that prevails. We forget complex relationships and store data as JSON documents, nested and flexible. An example with <\/span><a href=\"https:\/\/datascientest.com\/en\/mongodb-all-about-the-document-oriented-nosql-database\"><span style=\"font-weight: 400;\">MongoDB<\/span><\/a><span style=\"font-weight: 400;\">: a customer record can directly contain their order history, without the need for joins.<\/span><\/p><p><span style=\"font-weight: 400;\">This model shines for its flexibility in contexts of <\/span><b>semi-structured data<\/b><span style=\"font-weight: 400;\">, but can quickly become a nightmare if the structure evolves poorly. Each approach has its advantages, but also its pitfalls. The whole point of <\/span><b>data modeling<\/b><span style=\"font-weight: 400;\"> is to choose the right model for the right use case and to know how to mix them if necessary. Indeed, <\/span><b>hybridizing approaches<\/b><span style=\"font-weight: 400;\"> is sometimes the best solution&#8230;<\/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-7a99143 elementor-widget elementor-widget-image\" data-id=\"7a99143\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-4.webp\" class=\"attachment-full size-full wp-image-199496\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-4.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-4-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-4-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-4-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-d38f78c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d38f78c\" 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\/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\">Train in Data Modeling<\/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-58b4c96 e-flex e-con-boxed e-con\" data-id=\"58b4c96\" 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-43ac484 elementor-widget elementor-widget-heading\" data-id=\"43ac484\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Data modeling and data architecture<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de77df8 elementor-widget elementor-widget-text-editor\" data-id=\"de77df8\" 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;\">Be careful not to confuse data modeling with data architecture. The two are often associated, sometimes even merged, but they address distinct issues.<\/span><\/p><p><span style=\"font-weight: 400;\">Data modeling is above all a <\/span><b>logical view of data<\/b><span style=\"font-weight: 400;\">: thinking in terms of entities, relationships, dependencies, business rules. It is a design activity, often carried out by a <\/span><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\"><span style=\"font-weight: 400;\">data analyst<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-role-skills-salary\"><span style=\"font-weight: 400;\">data engineer<\/span><\/a><span style=\"font-weight: 400;\">, or <a href=\"https:\/\/datascientest.com\/en\/data-architect-training-skills-salary-education\">data architect<\/a>, with <\/span><b>strong interaction with the business<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Data architecture, on the other hand, concerns the technical implementation of this vision. It involves tools, <\/span><b>databases<\/b><span style=\"font-weight: 400;\">, <\/span><b>pipelines<\/b><span style=\"font-weight: 400;\">, <\/span><b>cloud<\/b><span style=\"font-weight: 400;\">, <\/span><b>security<\/b><span style=\"font-weight: 400;\">, <\/span><b>storage<\/b><span style=\"font-weight: 400;\">, and <\/span><b>governance<\/b><span style=\"font-weight: 400;\">. It&#8217;s the technical skeleton that carries the data model into reality. Consider <\/span><b>the data model<\/b><span style=\"font-weight: 400;\"> as the house plan, and data architecture as the foundations, walls, materials, and plumbing.<\/span><\/p><p><span style=\"font-weight: 400;\">Good <\/span><b>modeling without thinking about architecture<\/b><span style=\"font-weight: 400;\"> risks being unrealistic. Thinking about <\/span><b>architecture without a model<\/b><span style=\"font-weight: 400;\"> exposes chaos. Balancing the two is what makes the difference between a shaky data project and a reliable, maintainable, and scalable system.<\/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-6ee5406 e-flex e-con-boxed e-con\" data-id=\"6ee5406\" 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-6307ed1 elementor-widget elementor-widget-heading\" data-id=\"6307ed1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Why does modeling change everything in a data project?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b71d80 elementor-widget elementor-widget-text-editor\" data-id=\"0b71d80\" 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;\">One might think that modeling is a boring formality, but it&#8217;s quite the opposite. Data modeling is <\/span><b>the anchor point of the entire project<\/b><span style=\"font-weight: 400;\">. First reason: <a href=\"https:\/\/datascientest.com\/en\/data-quality-10-mistakes-not-to-make\">to ensure data quality<\/a>. A good model imposes validation rules, avoids redundancies, and documents the sources. This allows for <\/span><b>cleaner, more coherent, and thus more reliable data<\/b><span style=\"font-weight: 400;\">. The second reason is to facilitate collaboration.<\/span><\/p><p><span style=\"font-weight: 400;\">With a well-designed schema, everyone speaks the same language. Businesses know what each table corresponds to, <\/span><b>data analysts know how to query it, and data engineers know how to ingest it<\/b><span style=\"font-weight: 400;\">. No more need to decode obscure column names or patched-up structures. The third advantage, and not the least, is saving time (and money).<\/span><\/p><p><span style=\"font-weight: 400;\">Fewer bugs, less confusion, fewer painful refactorings. <\/span><b>The cost of a bad model isn\u2019t immediately apparent<\/b><span style=\"font-weight: 400;\">. But it skyrockets as the project grows. Conversely, <\/span><b>a good model allows for faster iteration and hassle-free scaling<\/b><span style=\"font-weight: 400;\">. Modeling also means anticipating. Anticipating future use cases, <a href=\"https:\/\/datascientest.com\/en\/alteryx-what-is-it-how-does-it-work\">advanced analytics<\/a>, <a href=\"https:\/\/datascientest.com\/en\/deep-convolutional-gans-unleashing-creativity-through-convolution\">machine learning<\/a>, or <a href=\"https:\/\/datascientest.com\/en\/cloud-architect-training-how-to-become-a-certified-professional\">cloud migration<\/a>. A good data model is reusable and adaptable.<\/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-43de325 elementor-widget elementor-widget-image\" data-id=\"43de325\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-3.webp\" class=\"attachment-full size-full wp-image-199497\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-3.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-3-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-3-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-3-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-bd5e0fe elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"bd5e0fe\" 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\/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\">Master Data Modeling<\/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-1535edf e-flex e-con-boxed e-con\" data-id=\"1535edf\" 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-e126914 elementor-widget elementor-widget-heading\" data-id=\"e126914\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Business-first or tech-first?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c72abc6 elementor-widget elementor-widget-text-editor\" data-id=\"c72abc6\" 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 the jungle of data projects, a recurring question is: should one model starting from the business, or from the system? <\/span><b>Business-oriented modeling<\/b><span style=\"font-weight: 400;\">, with a business-first vision, is <a href=\"https:\/\/datascientest.com\/en\/all-bout-competitive-intelligence\">the preferred approach in Business Intelligence,<\/a> reporting, or strategic analysis projects. It begins with asking: what entities are important for the business? What relationships make sense for the users?<\/span><\/p><p><span style=\"font-weight: 400;\">The model is designed to be readable, comprehensible, and usable by analysts and decision-makers. For example, in a marketing data warehouse, a <\/span><b>simple structure<\/b><span style=\"font-weight: 400;\"> is preferred, with clear <\/span><b>dimensions<\/b><span style=\"font-weight: 400;\"> like \u201cClient\u201d, \u201cCampaign\u201d, \u201cProduct\u201d. And this, even if it means duplicating certain data to <\/span><b>facilitate analyses<\/b><span style=\"font-weight: 400;\">. The goal is to <\/span><b>gain simplicity and effectiveness in analytical exploitation<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Conversely, some projects require a more technical and normalized structure, designed to ensure operational coherence. This is where <\/span><b>system-oriented modeling<\/b><span style=\"font-weight: 400;\"> comes in. It&#8217;s often the case in transactional systems like <a href=\"https:\/\/datascientest.com\/en\/all-about-oracle-erp\">ERP<\/a>, <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-big-data-marketing\"><span style=\"font-weight: 400;\">e-commerce<\/span><\/a><span style=\"font-weight: 400;\">, or <a href=\"https:\/\/datascientest.com\/en\/crm-consultant\">CRM<\/a>. Highly normalized models are preferred, avoiding redundancy. Models optimized for raw performance, reliability, and maintenance. The aim is integrity, <\/span><b>performance, and technical scalability<\/b><span style=\"font-weight: 400;\">. These two approaches are not opposed: they respond to different objectives. <\/span><b>The art of data modeling<\/b><span style=\"font-weight: 400;\"> is precisely about choosing the right approach according to the need.<\/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-157e998 e-flex e-con-boxed e-con\" data-id=\"157e998\" 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-a610674 elementor-widget elementor-widget-heading\" data-id=\"a610674\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">The best modeling tools<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fecc8e9 elementor-widget elementor-widget-text-editor\" data-id=\"fecc8e9\" 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;\">Modeling is a real task of design, dialogue, and documentation. To do it well, it\u2019s better to be well-equipped. Among the best tools, we can mention <\/span><a href=\"http:\/\/dbdiagram.io\"><span style=\"font-weight: 400;\">dbdiagram.io<\/span><\/a><span style=\"font-weight: 400;\">. Simple and visual, it is perfect <\/span><b>to quickly start an ERD schema<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">For more freeform and collaborative diagrams, one can use <\/span><a href=\"https:\/\/www.lucidchart.com\/pages\"><span style=\"font-weight: 400;\">Lucidchart<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"http:\/\/draw.io\"><span style=\"font-weight: 400;\">Draw.io<\/span><\/a><span style=\"font-weight: 400;\">. There are also solutions designed <\/span><b>for large systems<\/b><span style=\"font-weight: 400;\">, like <\/span><a href=\"https:\/\/erstudio.com\/\"><span style=\"font-weight: 400;\">ER\/Studio<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"https:\/\/www.powerdesigner.biz\/FR\"><span style=\"font-weight: 400;\">PowerDesigner<\/span><\/a><span style=\"font-weight: 400;\">. Tools like <\/span><a href=\"https:\/\/www.metabase.com\/\"><span style=\"font-weight: 400;\">Metabase<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/superset.apache.org\/\"><span style=\"font-weight: 400;\">Superset<\/span><\/a><span style=\"font-weight: 400;\"> allow for visualizing data models and relationships directly from the database.<\/span><\/p><p><span style=\"font-weight: 400;\">On the other hand, dbt (Data Build Tool) is heavily used <\/span><b>to document and structure analytical models<\/b><span style=\"font-weight: 400;\"> in modern data warehouses.<\/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-17896c9 elementor-widget elementor-widget-image\" data-id=\"17896c9\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-2.webp\" class=\"attachment-full size-full wp-image-199498\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-2.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-2-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-2-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-2-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-45a0d35 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"45a0d35\" 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\/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\">Train with Data Modeling tools<\/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-31284ab e-flex e-con-boxed e-con\" data-id=\"31284ab\" 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-545a189 elementor-widget elementor-widget-heading\" data-id=\"545a189\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Data modeling in the era of cloud, AI, and NoSQL<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bbe8439 elementor-widget elementor-widget-text-editor\" data-id=\"bbe8439\" 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;\">Long confined to <a href=\"https:\/\/datascientest.com\/en\/amazon-relational-database-service-rds-what-is-it\">traditional relational databases<\/a>, <\/span><b>data modeling<\/b><span style=\"font-weight: 400;\"> has had to adapt to the upheavals of the modern world: cloud computing, big data, <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-machine-learning-metrics\"><span style=\"font-weight: 400;\">machine learning<\/span><\/a><span style=\"font-weight: 400;\">, document-oriented databases&#8230;<\/span><\/p><p><span style=\"font-weight: 400;\">The landscape has changed, and so have the practices. With machine learning pipelines, we no longer work solely with static databases. Data can be massive, noisy, and evolving. Thus, it\u2019s necessary <\/span><b>to model iteratively<\/b><span style=\"font-weight: 400;\">, maintaining maximum flexibility while ensuring <\/span><b>traceability through data lineage and versioning<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Additionally, in MongoDB or <\/span><b>Firebase<\/b><span style=\"font-weight: 400;\">, there are no tables in the SQL sense. Yet, modeling is more essential than ever. It requires thinking about document nesting, acceptable duplications, and read\/write performance. Just because <\/span><b>the structure is &#8220;free&#8221;<\/b><span style=\"font-weight: 400;\"> doesn\u2019t mean one should improvise.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, cloud-native modeling is designed for scalability. With solutions like <\/span><a href=\"https:\/\/datascientest.com\/en\/snowflake-all-about\"><span style=\"font-weight: 400;\">Snowflake<\/span><\/a><span style=\"font-weight: 400;\">, <a href=\"https:\/\/datascientest.com\/en\/google-bigquery-everything-you-need-to-know-about-this-data-warehouse\">BigQuery<\/a>, or <strong>Redshift<\/strong>, the models must be frugal. They need to be <\/span><b>parallelizable<\/b><span style=\"font-weight: 400;\"> and adapted to pay-per-query. Cost, latency, cache, and even governance are considered from the start.<\/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-1a022d2 e-flex e-con-boxed e-con\" data-id=\"1a022d2\" 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-b8522f2 elementor-widget elementor-widget-heading\" data-id=\"b8522f2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-large\">Data Modeling, key to a project that\u2019s on track<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-defb6af elementor-widget elementor-widget-text-editor\" data-id=\"defb6af\" 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;\">Modeling your data is a bit like drawing the map before setting off on an adventure. Without a solid model, even the best tools or algorithms eventually run empty. Conversely, a well-thought-out base allows for <\/span><b>gaining in efficiency, clarity, and most importantly, longevity<\/b><span style=\"font-weight: 400;\">. Good modeling today means avoiding technical debt tomorrow.<\/span><\/p><p><span style=\"font-weight: 400;\">To <\/span><b>master the fundamentals of <\/b><b><i>data modeling<\/i><\/b><span style=\"font-weight: 400;\">, learn to structure robust pipelines and <\/span><b>build modern data architectures<\/b><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-course\"><span style=\"font-weight: 400;\">the Data Engineer training from DataScientest<\/span><\/a><span style=\"font-weight: 400;\"> is simply ideal. It immerses you in all the gears of the trade: modeling, managing <\/span><a href=\"https:\/\/datascientest.com\/en\/sql-vs-nosql\"><span style=\"font-weight: 400;\">SQL and NoSQL databases<\/span><\/a><span style=\"font-weight: 400;\">, building <\/span><a href=\"https:\/\/datascientest.com\/en\/etl-developer\"><span style=\"font-weight: 400;\">ETL pipelines<\/span><\/a><span style=\"font-weight: 400;\">, orchestration with Airflow, massive data ingestion with Spark, cloud deployment&#8230;<\/span><\/p><p><span style=\"font-weight: 400;\">In summary: everything a <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-role-skills-salary\"><span style=\"font-weight: 400;\">data engineer<\/span><\/a><span style=\"font-weight: 400;\"> needs to know to build solid foundations. Thanks to our <\/span><b>project-oriented approach<\/b><span style=\"font-weight: 400;\">, you&#8217;ll develop immediately applicable skills, directly related to field requirements. The training is certifying, professionalizing, and available <\/span><b>in BootCamp, apprenticeship, or continuing education<\/b><span style=\"font-weight: 400;\">. Eligible for CPF and France Travail. Join <\/span><b>DataScientest<\/b><span style=\"font-weight: 400;\">, and start building the data systems of tomorrow today.<\/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-184380f elementor-widget elementor-widget-image\" data-id=\"184380f\" 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=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-1.webp\" class=\"attachment-full size-full wp-image-199499\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-1.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-1-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-1-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/09\/data-modeling-datascientest-1-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\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-6053614 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6053614\" 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\/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\">Become a Data Modeling expert<\/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<div class=\"elementor-element elementor-element-9adfc7c elementor-widget elementor-widget-text-editor\" data-id=\"9adfc7c\" 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;\">Now you know everything about Data Modeling. For more information on the same subject, discover <a href=\"https:\/\/datascientest.com\/en\/data-architecture-definition-and-importance-in-data-science\">our complete article on Data Architecture<\/a> and <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-role-skills-salary\"><span style=\"font-weight: 400;\">our article on data engineering<\/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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-437d003 e-flex e-con-boxed e-con\" data-id=\"437d003\" 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-542ea66 elementor-widget elementor-widget-html\" data-id=\"542ea66\" 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    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What is data modeling?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Data Modeling involves formally representing the logical structures of data used in an information system, across conceptual, logical, and physical levels.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Major modeling approaches\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Several approaches exist: ERD diagrams, relational models, dimensional models for data warehouses, and NoSQL models like document-based structures.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Data modeling and data architecture: two complementary concepts\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Data modeling focuses on logical structure and relationships, while data architecture concerns the technical implementation and infrastructure.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Why does modeling change everything in a data project?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Modeling ensures data quality, facilitates collaboration across teams, and reduces bugs and costs as projects scale.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Business-first or tech-first? Two schools of thought\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Business-first modeling focuses on usability for analysis, while tech-first prioritizes performance and normalization in operational systems.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"The best modeling tools\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Tools like dbdiagram.io, Lucidchart, Draw.io, PowerDesigner, and dbt help create, document, and maintain robust data models.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Data modeling in the era of cloud, AI, and NoSQL\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Modern modeling adapts to evolving data structures, massive volumes, and cloud platforms, requiring flexible, scalable, and cost-efficient designs.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\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>Data Modeling is a frequently underestimated yet crucial step for the success of any data project. Discover what data modeling involves, the major types of models, and the best tools to use! Collecting data is one thing, but the most important part is understanding it. However, raw data is often as valuable as it is [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":199493,"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>Data Modeling: What is it? 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