Analytics Engineering with Microsoft Fabric and Power BI

Part I. Introduction to Analytics Engineering

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 1 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].

Before we touch Microsoft Fabric, Power BI, semantic models, notebooks, pipelines, or any of the shiny tools that make modern analytics work feel exciting, we need to deal with something less glamorous and far more important: the fundamentals. Because here is the truth that might make a lot of you feel uncomfortable: tools don’t fix unclear thinking.

You can build a beautiful dashboard on top of a terrible model. You can automate a pipeline that moves the wrong data perfectly every morning. You can create an elegant lakehouse architecture that nobody understands, nobody trusts, and nobody wants to use. Technology can make good decisions scale, but it can also make bad decisions expensive. That is why Part I starts with analytics engineering as a discipline, not as a vendor feature or a job title printed on a LinkedIn profile.

We begin in Chapter 1 by asking a deceptively simple question: what does an analytics engineer actually do? The answer is not “write ...