In this session, we will explore the critical aspects of managing large data models in Power BI Semantic Models. As organisations increasingly rely on Power BI for enterprise-level reporting and analy...

There’s a moment in every Fabric semantic model lifecycle where the “click it in the UI” approach stops scaling. It usually happens when you need to rename dozens (or hundreds) of fields to match a business glossary, or when Dev is stable and you’re ready to point the same model at a new Lakehouse for Test/Prod. That’s when the model stops being a diagram and starts being an artifact—something you want to treat like code. This guide reflows the whole workflow end-to-end, using the Fabric service Edit in Desktop experience to open the model, exporting it to a PBIP project stored as a TMDL folder, editing that folder externally (no scripting inside Power BI Desktop), and then getting those changes back into the service—

  If you've ever struggled with bulk-editing Power BI models, wished you could version control your semantic models properly, or wanted to share reusable model components with your team, you're in for a treat. The Tabular Model Definition Language (TMDL) view in Power BI Desktop is transforming how developers work with semantic models, and it might just become your new favorite feature. In this post, I'll walk you through the practical benefits of TMDL view with real-world examples that demonstrate why this feature deserves a spot in every Power BI developer's toolkit.      

Microsoft Fabric is retiring auto-generated semantic models. Learn about the impact, new workflows, and how to prepare for the changes.

YouTube video by Microsoft Hates Greg

While working with a mature Power BI semantic model, we faced a simple yet critical question: Where is the data for each table actually coming from? To solve this problem, we built a simple automated approach that extracts the data source information for every table directly from the semantic model. In this article, I will walk through the concept and implementation.

Many organizations end up creating multiple Power BI datasets / semantic models for different reports even though the underlying data is the same. This results in duplication, inconsistent reporting and inefficiencies.

This article explores how scalable semantic models bring structure, consistency, and trust to enterprise analytics.