{"id":4754,"date":"2026-01-05T10:00:56","date_gmt":"2026-01-05T09:00:56","guid":{"rendered":"https:\/\/data4success.de\/?p=4754"},"modified":"2026-01-21T10:18:40","modified_gmt":"2026-01-21T09:18:40","slug":"lakehouse-vs-data-warehouse-in-microsoft-fabric","status":"publish","type":"post","link":"https:\/\/data4success.de\/en\/lakehouse-vs-data-warehouse-in-microsoft-fabric\/","title":{"rendered":"Lakehouse vs. data warehouse in Microsoft Fabric"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"4754\" class=\"elementor elementor-4754\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f39c76a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f39c76a\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f147a9b\" data-id=\"f147a9b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7afd061 elementor-widget elementor-widget-text-editor\" data-id=\"7afd061\" data-element_type=\"widget\" data-e-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\t\t<p>With <strong data-start=\"336\" data-end=\"356\">Microsoft Fabric<\/strong> Microsoft has created a platform that is both <strong data-start=\"409\" data-end=\"422\">Lakehouse<\/strong> as well as <strong data-start=\"432\" data-end=\"450\">Data Warehouse<\/strong> integrated. Both terms are often used, but what exactly is behind them - and when is which model suitable?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a77c3d7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a77c3d7\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-553a46b\" data-id=\"553a46b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-72de1af elementor-widget elementor-widget-text-editor\" data-id=\"72de1af\" data-element_type=\"widget\" data-e-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\t\t<h3 data-start=\"563\" data-end=\"581\">Similarities<\/h3><ul data-start=\"582\" data-end=\"1046\"><li data-start=\"582\" data-end=\"687\"><p data-start=\"584\" data-end=\"687\"><strong data-start=\"584\" data-end=\"607\">Central database<\/strong>Both concepts are based on <strong data-start=\"636\" data-end=\"647\">OneLake<\/strong> the central storage in Fabric.<\/p><\/li><li data-start=\"688\" data-end=\"797\"><p data-start=\"690\" data-end=\"797\"><strong data-start=\"690\" data-end=\"718\">Integration with Power BI<\/strong>Both Lakehouse and Warehouse can be used seamlessly for reports.<\/p><\/li><li data-start=\"798\" data-end=\"920\"><p data-start=\"800\" data-end=\"920\"><strong data-start=\"800\" data-end=\"815\">SQL support<\/strong>In both approaches, data can be queried using SQL - a familiar approach for many analysts.<\/p><\/li><li data-start=\"921\" data-end=\"1046\"><p data-start=\"923\" data-end=\"1046\"><strong data-start=\"923\" data-end=\"941\">Scalability<\/strong>Both systems use Fabric's cloud architecture, which allows computing power to be scaled flexibly.<\/p><\/li><\/ul>\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-d10c1b4 elementor-widget elementor-widget-text-editor\" data-id=\"d10c1b4\" data-element_type=\"widget\" data-e-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\t\t<h3 data-start=\"1053\" data-end=\"1070\">Differences<\/h3><div class=\"_tableContainer_1rjym_1\"><div class=\"group _tableWrapper_1rjym_13 flex w-fit flex-col-reverse\" tabindex=\"-1\"><table class=\"w-fit min-w-(--thread-content-width)\" data-start=\"1072\" data-end=\"1945\"><thead data-start=\"1072\" data-end=\"1112\"><tr data-start=\"1072\" data-end=\"1112\"><th data-start=\"1072\" data-end=\"1082\" data-col-size=\"sm\">Feature<\/th><th data-start=\"1082\" data-end=\"1094\" data-col-size=\"lg\">Lakehouse<\/th><th data-start=\"1094\" data-end=\"1112\" data-col-size=\"md\">Data Warehouse<\/th><\/tr><\/thead><tbody data-start=\"1154\" data-end=\"1945\"><tr data-start=\"1154\" data-end=\"1370\"><td data-start=\"1154\" data-end=\"1174\" data-col-size=\"sm\"><strong data-start=\"1156\" data-end=\"1173\">Data structure<\/strong><\/td><td data-start=\"1174\" data-end=\"1289\" data-col-size=\"lg\">Can store unstructured, semi-structured and structured data (e.g. JSON, Parquet, CSV, ERP data)<\/td><td data-start=\"1289\" data-end=\"1370\" data-col-size=\"md\">Strictly structured data, optimized for tables and classic BI analyses<\/td><\/tr><tr data-start=\"1371\" data-end=\"1520\"><td data-start=\"1371\" data-end=\"1390\" data-col-size=\"sm\"><strong data-start=\"1373\" data-end=\"1389\">Flexibility<\/strong><\/td><td data-start=\"1390\" data-end=\"1444\" data-col-size=\"lg\">Very flexible, ideal for data science, AI, big data<\/td><td data-start=\"1444\" data-end=\"1520\" data-col-size=\"md\">More standardized, optimized for controlling &amp; management reporting<\/td><\/tr><tr data-start=\"1521\" data-end=\"1655\"><td data-start=\"1521\" data-end=\"1535\" data-col-size=\"sm\"><strong data-start=\"1523\" data-end=\"1534\">Access<\/strong><\/td><td data-start=\"1535\" data-end=\"1598\" data-col-size=\"lg\">Data can be saved raw and transformed later<\/td><td data-start=\"1598\" data-end=\"1655\" data-col-size=\"md\">Only cleansed and structured data is available<\/td><\/tr><tr data-start=\"1656\" data-end=\"1801\"><td data-start=\"1656\" data-end=\"1678\" data-col-size=\"sm\"><strong data-start=\"1658\" data-end=\"1677\">Speed<\/strong><\/td><td data-start=\"1678\" data-end=\"1753\" data-col-size=\"lg\">Very good for large amounts of data, but transformations can take time<\/td><td data-start=\"1753\" data-end=\"1801\" data-col-size=\"md\">Optimized for fast SQL queries and KPIs<\/td><\/tr><tr data-start=\"1802\" data-end=\"1945\"><td data-start=\"1802\" data-end=\"1819\" data-col-size=\"sm\"><strong data-start=\"1804\" data-end=\"1818\">Target group<\/strong><\/td><td data-start=\"1819\" data-end=\"1899\" data-col-size=\"lg\">Data scientists, analysts, companies with many different data sources<\/td><td data-start=\"1899\" data-end=\"1945\" data-col-size=\"md\">Controllers, management, business analysts<\/td><\/tr><\/tbody><\/table><\/div><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2b5fe63 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2b5fe63\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bb12698\" data-id=\"bb12698\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ec8806f elementor-widget elementor-widget-text-editor\" data-id=\"ec8806f\" data-element_type=\"widget\" data-e-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\t\t<h3 data-start=\"1952\" data-end=\"1977\">Advantages &amp; disadvantages<\/h3><h4 data-start=\"1979\" data-end=\"2005\">Lakehouse advantages<\/h4><ul data-start=\"2006\" data-end=\"2167\"><li data-start=\"2006\" data-end=\"2054\"><p data-start=\"2008\" data-end=\"2054\">Universal: supported <strong data-start=\"2032\" data-end=\"2051\">all data types<\/strong>.<\/p><\/li><li data-start=\"2055\" data-end=\"2103\"><p data-start=\"2057\" data-end=\"2103\">Perfect for <strong data-start=\"2069\" data-end=\"2100\">Data science and AI models<\/strong>.<\/p><\/li><li data-start=\"2104\" data-end=\"2167\"><p data-start=\"2106\" data-end=\"2167\">No duplicate memory required - raw data remains available.<\/p><\/li><\/ul><h4 data-start=\"2169\" data-end=\"2196\">Lakehouse disadvantages<\/h4><ul data-start=\"2197\" data-end=\"2348\"><li data-start=\"2197\" data-end=\"2283\"><p data-start=\"2199\" data-end=\"2283\">For <strong data-start=\"2203\" data-end=\"2228\">classic BI reports<\/strong> sometimes too flexible \u2192 higher modeling effort.<\/p><\/li><li data-start=\"2284\" data-end=\"2348\"><p data-start=\"2286\" data-end=\"2348\">Performance for standard KPIs often lower than in the warehouse.<\/p><\/li><\/ul><h4 data-start=\"2355\" data-end=\"2386\">Data warehouse advantages<\/h4><ul data-start=\"2387\" data-end=\"2612\"><li data-start=\"2387\" data-end=\"2428\"><p data-start=\"2389\" data-end=\"2428\"><strong data-start=\"2389\" data-end=\"2408\">Very high performance<\/strong> for SQL queries.<\/p><\/li><li data-start=\"2429\" data-end=\"2517\"><p data-start=\"2431\" data-end=\"2517\"><strong data-start=\"2431\" data-end=\"2453\">Structured KPIs<\/strong> (e.g. contribution margins, cash flow) are easy to calculate.<\/p><\/li><li data-start=\"2518\" data-end=\"2612\"><p data-start=\"2520\" data-end=\"2612\">Specialist areas (e.g. controlling) can <strong data-start=\"2560\" data-end=\"2579\">Get started right away<\/strong>, without complex modeling.<\/p><\/li><\/ul><h4 data-start=\"2614\" data-end=\"2646\">Data warehouse disadvantages<\/h4><ul data-start=\"2647\" data-end=\"2812\"><li data-start=\"2647\" data-end=\"2692\"><p data-start=\"2649\" data-end=\"2692\">Only for <strong data-start=\"2657\" data-end=\"2680\">Structured data<\/strong> suitable.<\/p><\/li><li data-start=\"2693\" data-end=\"2751\"><p data-start=\"2695\" data-end=\"2751\">Less flexible for data science or AI applications.<\/p><\/li><li data-start=\"2752\" data-end=\"2812\"><p data-start=\"2754\" data-end=\"2812\">Transformations often have to <strong data-start=\"2782\" data-end=\"2802\">previously defined<\/strong> become.<\/p><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b10f5fc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b10f5fc\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2eb69d5\" data-id=\"2eb69d5\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-87eca38 elementor-widget elementor-widget-text-editor\" data-id=\"87eca38\" data-element_type=\"widget\" data-e-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\t\t<p data-start=\"2832\" data-end=\"3001\">The <strong data-start=\"2836\" data-end=\"2849\">Lakehouse<\/strong> is ideal if companies want to merge many different data sources (ERP, IoT, cloud, Excel, logistics) and also use them for AI analyses.<\/p><p data-start=\"3004\" data-end=\"3164\">The <strong data-start=\"3008\" data-end=\"3026\">Data Warehouse<\/strong> is the best choice when it comes to <strong data-start=\"3058\" data-end=\"3110\">Structured reports and fast SQL queries<\/strong> especially in controlling or management.<\/p><p data-start=\"3166\" data-end=\"3326\">The big advantage of Microsoft Fabric: <strong data-start=\"3209\" data-end=\"3254\">Companies do not have to decide<\/strong>. Both models work hand in hand - and that's what makes Fabric so strong.<\/p><p data-start=\"3166\" data-end=\"3326\"><strong>Do you have any questions or would you like to find out more about our methods?<\/strong> <a href=\"https:\/\/data4success.de\/en\/contact\/\">Get in touch with us<\/a> - We show you how you can use your data for sustainable success.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Mit Microsoft Fabric hat Microsoft eine Plattform geschaffen, die sowohl Lakehouse als auch Data Warehouse integriert. Beide Begriffe fallen oft, aber was genau steckt dahinter \u2013 und wann eignet sich welches Modell? \u00c4hnlichkeiten Zentrale Datenbasis: Beide Konzepte greifen auf OneLake zu, den zentralen Speicher in Fabric. Integration mit Power BI: Sowohl Lakehouse als auch Warehouse [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":4882,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4754","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4754","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/comments?post=4754"}],"version-history":[{"count":11,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4754\/revisions"}],"predecessor-version":[{"id":4883,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4754\/revisions\/4883"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media\/4882"}],"wp:attachment":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media?parent=4754"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/categories?post=4754"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/tags?post=4754"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}