One of the biggest hurdles in data analytics projects is ensuring high data quality (DQ). Poor DQ can originate from unreliable source systems or the lack of robust business logic and requirements, resulting in inaccurate insights and delayed project timelines.
In this session, we’ll explore how Microsoft Fabric simplifies the DQ process with its built-in capabilities and how public frameworks can further streamline implementation. You’ll also see how Microsoft Purview’s latest DQ features integrate with Microsoft Fabric to enable consistent monitoring and governance across lakehouses, warehouses, and semantic models.
By the end of the session, you’ll know how to:
* Leverage Microsoft Fabric’s DQ features for real-world projects.
* Implement and adapt public frameworks for your use case.
* Know how and where to check for DQ issues in your Microsoft Fabric data preparation pipelines.
* Utilize Microsoft Purview to enhance data governance and quality management.
* use the replicated Purview metadata in Fabric to build your own DQ reports.
Whether you’re a data engineer, analyst, or architect, this session equips you with practical knowledge and tools to elevate your data quality strategy with Microsoft’s ecosystem.