One of the main challenges in data analytics projects is the lack of data quality (DQ). This issue can stem from either the source system or the absence of proper business logic and requirements.

But the question remains: is it really necessary to "reinvent" the DQ process for every project, or are there existing frameworks and concepts already integrated into Microsoft Fabric?

Join this session to learn about the data quality approaches built into Microsoft Fabric and the available public frameworks ready to use within Fabric. We'll discuss methods for testing data quality in lakehouses, warehouses, and even semantic models. Additionally, don't miss out on Microsoft Purview and its new data quality features—see them in action and understand how they integrate with Microsoft Fabric data.

By the end of the session, you'll have a comprehensive overview of what's available, what's possible, and what's still missing in the data quality landscape for Microsoft Fabric.