{"id":4872,"date":"2026-02-02T10:15:33","date_gmt":"2026-02-02T09:15:33","guid":{"rendered":"https:\/\/data4success.de\/?p=4872"},"modified":"2026-01-21T10:13:34","modified_gmt":"2026-01-21T09:13:34","slug":"automate-transform-scale-the-role-of-notebooks-in-microsoft-fabric","status":"publish","type":"post","link":"https:\/\/data4success.de\/en\/automate-transform-scale-the-role-of-notebooks-in-microsoft-fabric\/","title":{"rendered":"Automate, transform, scale: The role of notebooks in Microsoft Fabric"},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"4872\" class=\"elementor elementor-4872\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7ef3a41 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7ef3a41\" 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-6b86bf7\" data-id=\"6b86bf7\" 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-31d0e36 elementor-widget elementor-widget-text-editor\" data-id=\"31d0e36\" 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><style>\na {\n    text-decoration: none;\n    color: #464feb;\n}\ntr th, tr td {\n    border: 1px solid #e6e6e6;\n}\ntr th {\n    background-color: #f5f5f5;\n}\n<\/style><\/p><div><p><strong>Microsoft Fabric<\/strong> combines data integration, analysis, data science and real-time processing in a single platform. A central tool within it: <strong>Notebooks<\/strong>.<br \/>They enable data engineers and analysts to execute code, transform data and automate processes - directly in the browser and closely interlinked with Lakehouse, Warehouse and Pipelines.<\/p><p>Internal project documents show that notebooks are used in particular for data cleansing, API automation, multi-threaded queries and machine learning scenarios.<br \/>Business Central APIs also have dependencies around outbound access and authentication, which is described in detail in your BC requirements documents.<\/p><\/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-becd10e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"becd10e\" 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-56716c5\" data-id=\"56716c5\" 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-b1b0561 elementor-widget elementor-widget-text-editor\" data-id=\"b1b0561\" 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<div><h3>What is a notebook in Microsoft Fabric?<\/h3><p>A notebook is an interactive document that combines code cells (Python, SQL or Spark\/PySpark), text cells and visualizations.<br \/>In Fabric, notebooks run in a <strong>Spark-based compute engine<\/strong>, supplemented by Pandas support for smaller amounts of data. This is confirmed in your internal test documentation.<\/p><p>Typical areas of application:<\/p><ul><li>Data cleansing (e.g. removal of incorrect NAV data)<\/li><li>Transformation of large amounts of data (e.g. ledger entries from Business Central)<\/li><li>API queries &amp; automations (e.g. REST POST processes)<\/li><li>Data science: train, test and deploy ML models (Fabric calls up models in the notebook and operationalizes them).<\/li><\/ul><\/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-86c93e0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"86c93e0\" 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-b8ce547\" data-id=\"b8ce547\" 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-ae4c455 elementor-widget elementor-widget-text-editor\" data-id=\"ae4c455\" 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<div><h3>How do notebooks work technically?<\/h3><p>1. spark cluster starts automatically<\/p><p>As soon as the notebook is executed, Fabric initializes a Spark compute cluster.<br \/>Advantages according to internal documentation:<\/p><ul><li>Extremely fast parallel processing<\/li><li>Division of large tables into partitions<\/li><li>Ideal for large NAV\/BC data volumes, e.g. booking data<\/li><\/ul><p>\u00a0<\/p><p>2. access to Lakehouse &amp; Warehouse<\/p><p>Notebooks work directly with Lakehouse delta tables, but can also:<\/p><ul><li>Read CSV files from NAV exports<\/li><li>Process JSON files<\/li><li>Use SQL warehouse for queries<\/li><li>Create or update views<\/li><\/ul><p>\u00a0<\/p><p>3. mix of Spark &amp; Pandas<\/p><ul><li><strong>PySpark<\/strong> for large BC data load<\/li><li><strong>Pandas<\/strong> for smaller NAV files (e.g. article master data)<\/li><\/ul><p>Internal notes explicitly recommend using Pandas for smaller files because the data types are automatically recognized better.<\/p><p>\u00a0<\/p><p>4. integration into pipelines<\/p><p>Notebooks can be run seamlessly through pipelines.<br \/>In the BC data flow, for example:<\/p><ul><li>Pipeline loads data \u2192 Notebook transforms data \u2192 Gold layer updated<\/li><li>ML workflow: Pipeline loads raw data \u2192 Notebook performs scoring \u2192 Results end up in the lakehouse.<\/li><\/ul><p>\u00a0<\/p><p>5. API automation<\/p><p>Internal team conversations describe how notebooks call REST APIs, e.g:<\/p><ul><li>Synchronization between CI-Journey and Fabric<\/li><li>Business Central API calls via app registrations and outbound access protection settings<\/li><\/ul><\/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-9cd8f09 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9cd8f09\" 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-3da8efc\" data-id=\"3da8efc\" 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-0607250 elementor-widget elementor-widget-text-editor\" data-id=\"0607250\" 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<div><p>Notebooks are one of the most powerful tools in Microsoft Fabric.<br \/>They combine code, analysis and automation in a single, flexible environment.<\/p><ul><li>Flexible API connections<\/li><li>Fast and scalable transformation<\/li><li>Migration of historical NAV data<\/li><li>Development of modern data architectures<\/li><li>Implementation of machine learning use cases.<\/li><\/ul><p>This makes notebooks an indispensable tool in any modern fabric data platform.\u00a0<\/p><p><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><\/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<\/div>","protected":false},"excerpt":{"rendered":"<p>Microsoft Fabric vereint Datenintegration, Analyse, Data Science und Echtzeit\u2011Verarbeitung in einer einzigen Plattform. Ein zentrales Werkzeug darin: Notebooks. Sie erm\u00f6glichen Data Engineers und Analysten, Code auszuf\u00fchren, Daten zu transformieren und Prozesse zu automatisieren \u2013 direkt im Browser und eng verzahnt mit Lakehouse, Warehouse und Pipelines. Interne Projektdokumente zeigen, dass Notebooks insbesondere bei Datenbereinigung, API\u2011Automatisierungen, Multi\u2011Thread\u2011Abfragen [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":4880,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4872","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\/4872","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=4872"}],"version-history":[{"count":5,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4872\/revisions"}],"predecessor-version":[{"id":4881,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/posts\/4872\/revisions\/4881"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media\/4880"}],"wp:attachment":[{"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/media?parent=4872"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/categories?post=4872"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/data4success.de\/en\/wp-json\/wp\/v2\/tags?post=4872"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}