Picture this: you’ve just inherited a Power BI report, and your task is to perform data quality checks. Simple enough, until you’re told that every measure in the report must be validated against the source database. Suddenly, what seemed like a straightforward task becomes a time-intensive challenge.

There are several ways to ensure the measures align with the source data, but the process needs to be efficient, repeatable, and, most importantly, self-documenting.

In this session, we'll walk through using Python notebooks in Microsoft Fabric to streamline the data quality validation process. Together, we’ll build a Python script that performs these checks in a single batch and logs any discrepancies for review. You’ll learn how to:
- Utilize Power BI REST APIs to access measures dynamically.
- Connect to a SQL Server database for real-time validation.
- Create a logging table to document differences automatically.

This method ensures consistency, saves time, and provides a clear audit trail for every data quality check. Whether you’re a data engineer or analyst, this hands-on approach will empower you to handle even the most complex validation tasks with ease.