Analytics Engineering with Microsoft Fabric and Power BI

Part IV. Maintaining an Analytics Solution

A Note for Early Release Readers

With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles.

This will be Part 4 of the final book. Please note that the GitHub repo will be made active later on.

If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at [e-mail].

Building an analytics solution is only half the story. The other half begins when people start using it. That is usually when the questions change. Not “Can we build this?” but “Who should have access?” “Can this data leave the organization?” “What changed between yesterday and today?” “Why did production break?” “Can we deploy this safely without clicking around in ten different places and hoping for the best?”

Welcome to Part IV. By this point in the book, you have worked through the foundations of analytics engineering, explored Microsoft Fabric, and built Power BI semantic models that can support real analytical workloads. But production analytics is not just about data movement, modeling, DAX, or performance. A solution that is fast but insecure is not ready. A solution that works but cannot be governed is not ready. A solution that depends on manual changes in production is definitely not ready, even if everyone promises to be careful this time.

In