{"id":4715,"date":"2025-11-10T10:00:19","date_gmt":"2025-11-10T09:00:19","guid":{"rendered":"https:\/\/data4success.de\/?p=4715"},"modified":"2025-11-27T08:08:19","modified_gmt":"2025-11-27T07:08:19","slug":"lakehouse-in-microsoft-fabric-the-bridge-between-data-lake-and-data-warehouse","status":"publish","type":"post","link":"https:\/\/data4success.de\/en\/lakehouse-in-microsoft-fabric-the-bridge-between-data-lake-and-data-warehouse\/","title":{"rendered":"Lakehouse in Microsoft Fabric - the bridge between data lake and data warehouse"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"4715\" class=\"elementor elementor-4715\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cefb2c9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cefb2c9\" 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-9c20b95\" data-id=\"9c20b95\" 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-79ce132 elementor-widget elementor-widget-text-editor\" data-id=\"79ce132\" 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=\"380\" data-end=\"650\">Traditionally, companies had to make a decision: <strong data-start=\"431\" data-end=\"444\">Data Lake<\/strong> or <strong data-start=\"450\" data-end=\"468\">Data Warehouse<\/strong>. While data lakes flexibly store large volumes of raw data, data warehouses offer a structured environment for clean reporting. Both worlds had advantages and disadvantages.<\/p><p data-start=\"652\" data-end=\"756\">With the <strong data-start=\"660\" data-end=\"693\">Lakehouse in Microsoft Fabric<\/strong> Microsoft combines these approaches in a single platform.<\/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-7ff0dee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7ff0dee\" 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-748248e\" data-id=\"748248e\" 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-6bf27db elementor-widget elementor-widget-text-editor\" data-id=\"6bf27db\" 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=\"758\" data-end=\"785\">What is a lakehouse?<\/h3><p data-start=\"786\" data-end=\"935\">A lakehouse combines the <strong data-start=\"815\" data-end=\"867\">Scalability and flexibility of a data lake<\/strong> with the <strong data-start=\"876\" data-end=\"932\">Structural and query advantages of a data warehouse<\/strong>.<\/p><p data-start=\"937\" data-end=\"962\">In Fabric this means:<\/p><ul data-start=\"963\" data-end=\"1202\"><li data-start=\"963\" data-end=\"1073\"><p data-start=\"965\" data-end=\"1073\">Data can <strong data-start=\"978\" data-end=\"1004\">Raw and unstructured<\/strong> (e.g. from IoT, NAV exports or CSV files) can be saved.<\/p><\/li><li data-start=\"1074\" data-end=\"1202\"><p data-start=\"1076\" data-end=\"1202\">At the same time, this data can <strong data-start=\"1108\" data-end=\"1124\">structured<\/strong> for SQL queries and reporting - without redundant copies.<\/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-9776a42 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9776a42\" 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-034472e\" data-id=\"034472e\" 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-0bd1bb6 elementor-widget elementor-widget-text-editor\" data-id=\"0bd1bb6\" 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=\"1204\" data-end=\"1280\">Why is this exciting for Dynamics NAV, Navision and Business Central?<\/h3><p data-start=\"1281\" data-end=\"1406\">Many medium-sized companies work with ERP systems such as <strong data-start=\"1346\" data-end=\"1375\">NAV or Business Central<\/strong>. Typical challenges:<\/p><ul data-start=\"1407\" data-end=\"1650\"><li data-start=\"1407\" data-end=\"1488\"><p data-start=\"1409\" data-end=\"1488\">Exports end up in Excel or CSV and have to be processed manually.<\/p><\/li><li data-start=\"1489\" data-end=\"1581\"><p data-start=\"1491\" data-end=\"1581\">Data analyses become slow if too many tables or long histories are involved.<\/p><\/li><li data-start=\"1582\" data-end=\"1650\"><p data-start=\"1584\" data-end=\"1650\">Different departments maintain their own databases.<\/p><\/li><\/ul><p data-start=\"1652\" data-end=\"1710\">These problems can be solved with a Lakehouse:<\/p><ul data-start=\"1711\" data-end=\"1944\"><li data-start=\"1711\" data-end=\"1813\"><p data-start=\"1713\" data-end=\"1813\"><strong data-start=\"1713\" data-end=\"1742\">A central storage location<\/strong> for all ERP data, supplemented by external sources (CRM, logistics, IoT).<\/p><\/li><li data-start=\"1814\" data-end=\"1878\"><p data-start=\"1816\" data-end=\"1878\"><strong data-start=\"1816\" data-end=\"1840\">Seamless integration<\/strong> in Power BI for real-time reporting.<\/p><\/li><li data-start=\"1879\" data-end=\"1944\"><p data-start=\"1881\" data-end=\"1944\"><strong data-start=\"1881\" data-end=\"1899\">Historicization<\/strong> of data, without complicated ETL processes.<\/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-afee0d3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"afee0d3\" 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-7134e82\" data-id=\"7134e82\" 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-a6ac2e2 elementor-widget elementor-widget-text-editor\" data-id=\"a6ac2e2\" 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=\"1946\" data-end=\"1975\">Advantages at a glance<\/h3><ul data-start=\"1976\" data-end=\"2292\"><li data-start=\"1976\" data-end=\"2020\"><p data-start=\"1978\" data-end=\"2020\"><strong data-start=\"1978\" data-end=\"2005\">Standardized database<\/strong> instead of silos.<\/p><\/li><li data-start=\"2021\" data-end=\"2096\"><p data-start=\"2023\" data-end=\"2096\"><strong data-start=\"2023\" data-end=\"2042\">Cost savings<\/strong>, as data does not have to be stored multiple times.<\/p><\/li><li data-start=\"2097\" data-end=\"2206\"><p data-start=\"2099\" data-end=\"2206\"><strong data-start=\"2099\" data-end=\"2121\">Future security<\/strong>, as both classic SQL analyses and modern AI models are supported.<\/p><\/li><li data-start=\"2207\" data-end=\"2292\"><p data-start=\"2209\" data-end=\"2292\"><strong data-start=\"2209\" data-end=\"2226\">Speed<\/strong>, as data does not have to be moved from lake to warehouse.<\/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-c7e2ee2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c7e2ee2\" 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-fca794f\" data-id=\"fca794f\" 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-e6ad1e3 elementor-widget elementor-widget-text-editor\" data-id=\"e6ad1e3\" 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=\"2305\" data-end=\"2409\">The Lakehouse in Fabric is the perfect answer to the growing data requirements of SMEs.<\/p><p data-start=\"2411\" data-end=\"2599\">For companies with <strong data-start=\"2431\" data-end=\"2479\">Dynamics NAV, Navision or Business Central<\/strong> it offers the opportunity to use operational data more efficiently - whether for controlling, sales or production.<\/p><p data-start=\"2601\" data-end=\"2735\">This creates a <strong data-start=\"2618\" data-end=\"2662\">Scalable, standardized data platform<\/strong>, that lays the foundations today for the decisions of tomorrow.<\/p><p data-start=\"2601\" data-end=\"2735\"><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>Traditionell mussten sich Unternehmen entscheiden: Data Lake oder Data Warehouse. W\u00e4hrend Data Lakes gro\u00dfe Mengen an Rohdaten flexibel speichern, bieten Data Warehouses eine strukturierte Umgebung f\u00fcr sauberes Reporting. Beide Welten hatten Vor- und Nachteile. Mit dem Lakehouse in Microsoft Fabric vereint Microsoft diese Ans\u00e4tze in einer einzigen Plattform. Was ist ein Lakehouse? Ein Lakehouse kombiniert [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":4724,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4715","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\/4715","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=4715"}],"version-history":[{"count":7,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4715\/revisions"}],"predecessor-version":[{"id":4789,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4715\/revisions\/4789"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media\/4724"}],"wp:attachment":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media?parent=4715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/categories?post=4715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/tags?post=4715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}