Analytics Engineering with Microsoft Fabric and Power BI

Part II. Working with Microsoft Fabric

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 2 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].

Now that we’ve covered the fundamentals, it’s time to put them to work. Part I was deliberately tool-agnostic. We talked about the role of the analytics engineer, the shape of modern data architectures, and the modeling principles that make analytics systems trustworthy. That foundation matters because Microsoft Fabric is not just another tool you click around in. It is an entire analytics platform, and like every platform, it rewards people who understand what they are trying to build before they start building it.

This part of the book is where Fabric becomes real. We begin in Chapter 4 with the big picture: what Microsoft Fabric is, why it exists, and how its core concepts fit together. You’ll meet the ideas that show up everywhere else in the platform: tenants, capacities, workspaces, OneLake, workloads, pricing, and the software-as-a-service (SaaS) model that makes Fabric feel much closer to Power BI than to a traditional collection of cloud services. The ...