Power BI's latest game-changer is here: TMDL (Tabular Model Definition Language) is revolutionizing how we handle data modeling. Imagine setting up your entire measure framework in seconds, viewing all your measures in a single screen, and automating your best practices right from the start. In our latest blog post, we dive deep into this powerful new feature that's bridging the gap between casual BI users and professional developers. Whether you're a Power BI veteran or just getting started, discover how TMDL can transform your workflow and save you countless hours of development time. Ready to supercharge your Power BI development? Read on to learn how TMDL is reshaping the future of data modeling.

Data protection is very important in today's data-driven environment. To achieve this,  organizations must apply specific policies and restrictions to maintain data integrity and confidentiality. In Power BI, we have data protection policies like sensitivity labels which allow organizations to classify and secure data in alignment with their company policies.These labels ensure that data is appropriately governed, controlled, and handled with care throughout its lifecycle. In this blog we will discuss on how Power BI’s data protection policies, like sensitivity labels, can help organizations meet their security needs while enabling efficient and responsible data collaboration

Organizations often juggle vast amounts of Power BI content - reports, dashboards, datasets designed for sharing and reuse. With so much content available, how do users pinpoint trustworthy, high-quality resources? That’s where endorsement comes into play. This feature helps streamline the process by marking specific Power BI content as reliable and authoritative. These blog provides the closer look at how Power BI content can be certified and endorsed for building trust and fostering in smarter decision-making.

Unlock enterprise-grade version control and CI/CD for your Microsoft Fabric dashboards. Learn how to implement GitHub integration for real-time analytics, complete with code samples and deployment best practices.

Managing stock levels dynamically is essential for effective inventory management. A crucial aspect of this task involves calculating cumulative quantities to track the balance of products over time. In this example, we have Sales and Purchasing tables, and we aim to create a Matrix visual in Power BI to display the purchased and sold quantities accurately. The ultimate goal is to dynamically track the stock balance (leftover products) at any given point by calculating the difference between cumulative purchases and cumulative sales.

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