{"id":179647,"date":"2024-05-16T14:59:05","date_gmt":"2024-05-16T13:59:05","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=179647"},"modified":"2024-02-25T11:06:48","modified_gmt":"2024-02-25T10:06:48","slug":"introduction-to-multidimensional-architectures","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures","title":{"rendered":"Introduction to multidimensional architectures"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"179647\" class=\"elementor elementor-179647\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4d625ec e-flex e-con-boxed e-con\" data-id=\"4d625ec\" 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-5c469bb elementor-widget elementor-widget-text-editor\" data-id=\"5c469bb\" 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>Multidimensional analysis is the ability to analyze data that has been aggregated along several dimensions. In this article, you&#8217;ll discover star and snowflake patterns, the most popular multidimensional data models. You&#8217;ll find a table comparing these two models at the end of the article.<\/h2>\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-e9d921b e-flex e-con-boxed e-con\" data-id=\"e9d921b\" 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-cc23845 elementor-widget elementor-widget-text-editor\" data-id=\"cc23845\" 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<h3>What is an OLAP cube?<\/h3>\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-91b0183 e-flex e-con-boxed e-con\" data-id=\"91b0183\" 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<div class=\"elementor-element elementor-element-ddd298a e-con-full e-flex e-con\" data-id=\"ddd298a\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7a0e95a elementor-widget elementor-widget-text-editor\" data-id=\"7a0e95a\" 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><a href=\"https:\/\/datascientest.com\/en\/olap-everything-you-need-to-know-about-this-database-organization\">OLAP hypercubes, for Online Analytical Processing<\/a>, are used to represent n-dimensional data. The data is structured along several multi-dimensional analysis axes, such as time, location, etc.<\/p><p>A cell is the intersection of different dimensions. Each cell is calculated on loading. This ensures a stable response time, whatever the query.<\/p><p>The cubes are designed to be used by all company employees, and are capable of reporting millions of records at once.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-4452e5c e-con-full e-flex e-con\" data-id=\"4452e5c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c9aab7e elementor-widget elementor-widget-image\" data-id=\"c9aab7e\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/08\/illu_database_schema_cube-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\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-24bc8c2 e-flex e-con-boxed e-con\" data-id=\"24bc8c2\" 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-c2fd6ff elementor-widget elementor-widget-text-editor\" data-id=\"c2fd6ff\" 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><strong>Star and snowflake schemas<\/strong> are the most popular multidimensional <a href=\"https:\/\/datascientest.com\/en\/transactional-database-model-all-about-this-type-of-database\">data models.<\/a> While there are crucial differences between these two technologies, they both use fact tables and dimension tables to create and build a schema.<\/p><p>These database models are commonly used to meet the analysis needs of enterprise<a href=\"https:\/\/datascientest.com\/en\/data-warehouse-2\"> data warehouses.<\/a><\/p><p>First, let&#8217;s take a look at the difference between these two tables, then move on to data models.<\/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-0d57fd4 e-flex e-con-boxed e-con\" data-id=\"0d57fd4\" 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-0aeefe0 elementor-widget elementor-widget-text-editor\" data-id=\"0aeefe0\" 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<h3>Table of facts and table of dimensions<\/h3>\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-37dc537 e-flex e-con-boxed e-con\" data-id=\"37dc537\" 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-cb8d8af elementor-widget elementor-widget-text-editor\" data-id=\"cb8d8af\" 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>The<strong> fact table and the dimension table<\/strong> are used to create data schemas. A fact table record is a combination of attributes from different dimension tables. The fact table enables the user to <strong>analyze the various component functions of the company:<\/strong> sales, HR, support&#8230;<\/p><p>This will help to make decisions to improve the various activities that revolve around the company.<\/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-4112a6a elementor-widget elementor-widget-image\" data-id=\"4112a6a\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/08\/illu_database_schema_table-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\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-ae7e2ca elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"ae7e2ca\" 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\">Discover our courses<\/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-72de1cd e-flex e-con-boxed e-con\" data-id=\"72de1cd\" 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-4307ff3 elementor-widget elementor-widget-text-editor\" data-id=\"4307ff3\" 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>Here are a few things to bear in mind:<\/p><ul><li>The fact table contains measurements on the attributes of a dimension table.<\/li><li>The fact table contains more records and fewer attributes than the dimension table.<\/li><li>The fact table increases in size vertically, while the dimension table increases horizontally.<\/li><li>Each dimension table contains a primary key to identify each record in the table, while the fact table contains a concatenated key which is a combination of all the primary keys of all the dimension tables.<\/li><li>The dimension table must be registered before the fact table is created.<\/li><li>A schema contains fewer fact tables but more dimension tables.<\/li><li>Fact table attributes are both numeric and textual, while dimension tables have only textual attributes.<\/li><\/ul>\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-9a997cc e-flex e-con-boxed e-con\" data-id=\"9a997cc\" 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-9128627 elementor-widget elementor-widget-text-editor\" data-id=\"9128627\" 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<h3>Definition of the star schematic<\/h3>\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-0fc6810 e-flex e-con-boxed e-con\" data-id=\"0fc6810\" 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-b1bcf56 elementor-widget-tablet__width-initial elementor-widget elementor-widget-image\" data-id=\"b1bcf56\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/08\/illu_database_schema_etoile-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Representation of a \"star\" architecture<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-5136746 e-flex e-con-boxed e-con\" data-id=\"5136746\" 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-deba947 elementor-widget elementor-widget-text-editor\" data-id=\"deba947\" 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>The star schema is the most common and widely used architectural model for developing data warehouses in which data is organized into facts and dimensions.<\/p><p>The schema mimics a star, with a dimension table presented in a spread-out pattern surrounding the central fact table. The dimension table is connected to the fact tables via primary and foreign keys.<\/p><p>Simplicity is one of the appealing aspects of the star schema, and the main justifications for its use are its performance and ease of understanding.<\/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\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bf0e096 e-flex e-con-boxed e-con\" data-id=\"bf0e096\" 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-1dff9fa elementor-widget elementor-widget-text-editor\" data-id=\"1dff9fa\" 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<h3>Defining the snowflake pattern<\/h3>\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-8046b26 e-flex e-con-boxed e-con\" data-id=\"8046b26\" 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-80df596 elementor-widget-tablet__width-initial elementor-widget elementor-widget-image\" data-id=\"80df596\" 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<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2022\/08\/illu_database_schema_flocon-1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Representation of a \"Snowflake\" architecture<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-85609eb e-flex e-con-boxed e-con\" data-id=\"85609eb\" 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-345e766 elementor-widget elementor-widget-text-editor\" data-id=\"345e766\" 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><a href=\"https:\/\/datascientest.com\/en\/snowflake-training-master-the-1st-cloud-data-warehouse\">The snowflake schema<\/a> is a type of star schema that includes the hierarchical form of dimensional tables. In this schema, there is a fact table made up of different dimension and sub-dimension tables linked by primary and foreign keys to the fact table.<\/p><p>It&#8217;s an extension of the star schema, with added functionality. Unlike the star schema, the dimension tables of the snowflake schema are normalized into several associated tables. The snowflake schema is designed to answer queries on a dimension with complex relationships between its levels.<\/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\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-de08ab8 e-flex e-con-boxed e-con\" data-id=\"de08ab8\" 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-9bdfc17 elementor-widget elementor-widget-text-editor\" data-id=\"9bdfc17\" 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>This type of schema is suitable for dimensions whose levels are linked by n-to-n and 1-to-n relationships.<br \/>This type of schema uses normalization to partition data into tables. Partitioning reduces redundancy and avoids memory leaks. A snowflake schema is easier to maintain but more complex to design and understand than a star schema.<\/p><p>It can also reduce navigation efficiency, as more joins are required to execute a query.<\/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-6d31792 e-flex e-con-boxed e-con\" data-id=\"6d31792\" 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-bd8e374 elementor-widget elementor-widget-text-editor\" data-id=\"bd8e374\" 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<h3>Star or snowflake pattern: Which to choose?<\/h3>\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-d16dcdd e-flex e-con-boxed e-con\" data-id=\"d16dcdd\" 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-7ab9e23 elementor-widget elementor-widget-text-editor\" data-id=\"7ab9e23\" 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<h4>Data integrity<\/h4>\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-cc2cdb9 e-flex e-con-boxed e-con\" data-id=\"cc2cdb9\" 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-4902880 elementor-widget elementor-widget-text-editor\" data-id=\"4902880\" 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>The <strong>main difference between the two relational database models is normalization.<\/strong> Dimension tables in the star schema are not normalized, which means that the commercial model uses relatively more disk space to store dimension tables.<\/p><p>This generates more redundant records, which can be a source of inconsistencies. The flake schema, on the other hand, minimizes data redundancy since dimension tables are normalized.<\/p><p>Dimensions are preserved through referential integrity, meaning that relationships can be maintained independently between data stores.<\/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-1e88aa8 e-flex e-con-boxed e-con\" data-id=\"1e88aa8\" 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-d0be56d elementor-widget elementor-widget-text-editor\" data-id=\"d0be56d\" 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<h4>Query performance<\/h4>\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-2a1cc08 e-flex e-con-boxed e-con\" data-id=\"2a1cc08\" 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-4a669fb elementor-widget elementor-widget-text-editor\" data-id=\"4a669fb\" 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>The<strong> star schema<\/strong> has fewer joins between the dimension table and the fact table than the snowflake schema, which means lower query complexity.<\/p><p>As the dimensions in a star schema are linked via a central fact table, the join paths are clear, which means fast query response times and therefore better performance.<\/p><p>The Snowflake schema has a higher number of joins, which lengthens query response times, making them more complex, thus compromising performance.<\/p><p>There you have it, I hope you now have a clear idea of the differences and advantages of each of the multidimensional data models.<\/p><p>I promised you a gift at the end of the article, and here it is: a summary table you can save to keep you up to date!<\/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-0f6ea1e e-flex e-con-boxed e-con\" data-id=\"0f6ea1e\" 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-98cff8a elementor-widget elementor-widget-text-editor\" data-id=\"98cff8a\" 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<h4>Comparative table of the two schemes<\/h4>\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-a699d75 e-flex e-con-boxed e-con\" data-id=\"a699d75\" 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-b8e89b6 elementor-widget elementor-widget-text-editor\" data-id=\"b8e89b6\" 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<table><thead><tr><th>Aspect<\/th><th>Star Schema<\/th><th>Snowflake Schema<\/th><\/tr><\/thead><tbody><tr><td>Structure<\/td><td>Denormalized<\/td><td>Normalized<\/td><\/tr><tr><td>Dimension Tables<\/td><td>Fewer, typically 1 per dimension<\/td><td>More, separate tables for each dimension<\/td><\/tr><tr><td>Fact Table<\/td><td>Single, central fact table<\/td><td>May have multiple fact tables<\/td><\/tr><tr><td>Performance<\/td><td>Generally faster query performance<\/td><td>May have slower query performance<\/td><\/tr><tr><td>Storage<\/td><td>Requires more storage space<\/td><td>Requires less storage space<\/td><\/tr><tr><td>Data Integrity<\/td><td>Easier to maintain data integrity<\/td><td>May require more effort for data integrity<\/td><\/tr><tr><td>Complexity<\/td><td>Simpler to understand and implement<\/td><td>More complex to design and implement<\/td><\/tr><tr><td>Scalability<\/td><td>May become less scalable with growth<\/td><td>More scalable with proper indexing<\/td><\/tr><tr><td>Normalization<\/td><td>Not normalized<\/td><td>Fully or partially normalized<\/td><\/tr><\/tbody><\/table>\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-3ae1ad4 e-flex e-con-boxed e-con\" data-id=\"3ae1ad4\" 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-fda7cf1 elementor-widget elementor-widget-text-editor\" data-id=\"fda7cf1\" 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<ul><li>Are you interested in multidimensional data architectures?<\/li><li>Would you like to learn how to manage large databases?<\/li><li>Then take a look at our <a href=\"https:\/\/datascientest.com\/en\/data-engineer-course\">Data Engineer course!<\/a><\/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-51bdc91 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"51bdc91\" 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\">Discover our courses<\/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-5e2d5c3 elementor-widget elementor-widget-html\" data-id=\"5e2d5c3\" 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\": \"Un cube OLAP d\u00e9finition :\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Les hypercubes OLAP, pour Online Analytical Processing sont utilis\u00e9s pour repr\u00e9senter des donn\u00e9es \u00e0 n dimensions. Les donn\u00e9es sont structur\u00e9es suivant plusieurs axes d'analyse multidimensionnels comme le temps, la localisation ...\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"C'est quoi une table de faits et une table de dimensions ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"La table de faits et la table de dimensions sont utilis\u00e9es pour cr\u00e9er des sch\u00e9mas de donn\u00e9es. L\u2019enregistrement d\u2019une table de faits est une combinaison d\u2019attributs de diff\u00e9rentes tables de dimension. La table de faits permet \u00e0 l\u2019utilisateur d\u2019analyser les diff\u00e9rents m\u00e9tiers composants de l\u2019entreprise : Commerce, RH, service support\u2026\r\n\r\nCela va aider \u00e0 prendre des d\u00e9cisions pour am\u00e9liorer les diff\u00e9rentes activit\u00e9s qui gravitent autour de la soci\u00e9t\u00e9.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"D\u00e9finition du sch\u00e9ma en \u00e9toile\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le sch\u00e9ma en \u00e9toile est le mod\u00e8le architectural le plus r\u00e9pandu et le plus largement utilis\u00e9 pour d\u00e9velopper des entrep\u00f4ts de donn\u00e9es dans lesquels les donn\u00e9es sont organis\u00e9es en faits et en dimensions.\u00a0\r\n\r\nLe sch\u00e9ma imite une \u00e9toile, avec une table de dimension pr\u00e9sent\u00e9e dans un motif \u00e9tal\u00e9 entourant la table de faits centrale. La table de dimensions est connect\u00e9e aux tables de faits via la cl\u00e9 primaire et la cl\u00e9 \u00e9trang\u00e8re.\r\n\r\nLa simplicit\u00e9 est l\u2019un des aspects attrayants du sch\u00e9ma en \u00e9toile, les principales justifications \u00e0 son utilisation sont sa performance et sa facilit\u00e9 de compr\u00e9hension.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"D\u00e9finition du sch\u00e9ma de flocon de neige\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le sch\u00e9ma en flocon de neige est un type de sch\u00e9ma en \u00e9toile qui inclut la forme hi\u00e9rarchique des tables dimensionnelles. Dans ce sch\u00e9ma, il existe une table de faits compos\u00e9e de diff\u00e9rentes tables de dimension et de sous-dimension reli\u00e9es par des cl\u00e9s primaires et \u00e9trang\u00e8res \u00e0 la table de faits.\"\r\n    }\r\n  }]\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Multidimensional analysis is the ability to analyze data that has been aggregated along several dimensions. In this article, you&#8217;ll discover star and snowflake patterns, the most popular multidimensional data models. You&#8217;ll find a table comparing these two models at the end of the article. What is an OLAP cube? OLAP hypercubes, for Online Analytical Processing, [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":179649,"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>Introduction to multidimensional architectures<\/title>\n<meta name=\"description\" content=\"Dive into the world of multidimensional architectures, exploring their fundamental concepts, applications, and key considerations for designing and implementing robust and scalable systems.\" \/>\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\/introduction-to-multidimensional-architectures\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction to multidimensional architectures\" \/>\n<meta property=\"og:description\" content=\"Multidimensional analysis is the ability to analyze data that has been aggregated along several dimensions. In this article, you&#039;ll discover star and\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-16T13:59:05+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-02-25T10:06:48+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2024\/05\/architecture-768x422-1.jpeg\" \/>\n\t<meta property=\"og:image:width\" content=\"768\" \/>\n\t<meta property=\"og:image:height\" content=\"422\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Melanie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Melanie\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\"},\"author\":{\"name\":\"Melanie\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\"},\"headline\":\"Introduction to multidimensional architectures\",\"datePublished\":\"2024-05-16T13:59:05+00:00\",\"dateModified\":\"2024-02-25T10:06:48+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\"},\"wordCount\":980,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures#respond\"]}],\"copyrightYear\":\"2024\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\",\"url\":\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\",\"name\":\"Introduction to multidimensional architectures\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2024-05-16T13:59:05+00:00\",\"dateModified\":\"2024-02-25T10:06:48+00:00\",\"description\":\"Dive into the world of multidimensional architectures, exploring their fundamental concepts, applications, and key considerations for designing and implementing robust and scalable systems.\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\",\"name\":\"Melanie\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"caption\":\"Melanie\"},\"url\":\"https:\/\/datascientest.com\/en\/author\/melaniekrause\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Introduction to multidimensional architectures","description":"Dive into the world of multidimensional architectures, exploring their fundamental concepts, applications, and key considerations for designing and implementing robust and scalable systems.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures","og_locale":"en_GB","og_type":"article","og_title":"Introduction to multidimensional architectures","og_description":"Multidimensional analysis is the ability to analyze data that has been aggregated along several dimensions. In this article, you'll discover star and","og_url":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2024-05-16T13:59:05+00:00","article_modified_time":"2024-02-25T10:06:48+00:00","og_image":[{"width":768,"height":422,"url":"https:\/\/datascientest.com\/en\/files\/2024\/05\/architecture-768x422-1.jpeg","type":"image\/jpeg"}],"author":"Melanie","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Melanie","Estimated reading time":"6 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures"},"author":{"name":"Melanie","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d"},"headline":"Introduction to multidimensional architectures","datePublished":"2024-05-16T13:59:05+00:00","dateModified":"2024-02-25T10:06:48+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures"},"wordCount":980,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Data Science"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures#respond"]}],"copyrightYear":"2024","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures","url":"https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures","name":"Introduction to multidimensional architectures","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2024-05-16T13:59:05+00:00","dateModified":"2024-02-25T10:06:48+00:00","description":"Dive into the world of multidimensional architectures, exploring their fundamental concepts, applications, and key considerations for designing and implementing robust and scalable systems.","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/introduction-to-multidimensional-architectures"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d","name":"Melanie","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","caption":"Melanie"},"url":"https:\/\/datascientest.com\/en\/author\/melaniekrause"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/179647"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/67"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=179647"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/179647\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/179649"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=179647"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=179647"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=179647"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}