Data Modeling with Microsoft Power BI

A Power BI report can look polished and still rest on a model that is difficult to trust, maintain, or improve. Data Modeling with Microsoft Power BI focuses on the decisions beneath the visuals: how tables relate, how data is shaped, and how a model can support useful analysis without becoming a performance burden.

Markus Ehrenmueller-Jensen builds from core data-modeling principles toward practical work in Power BI, DAX, Power Query and M, and SQL. The result is a structured guide for readers who want to understand not just how to build a model, but why particular modeling choices matter.

Start with the structure of the data

The opening material establishes the building blocks: tables, relationships, keys, cardinality, joins, normal forms, dimensional modeling, granularity, and ETL. These foundations help make later Power BI techniques easier to reason about, especially when a model includes multiple tables and competing paths for analysis.

Work through real modeling decisions

From normalization and denormalization to dates, calculations, role-playing dimensions, and slowly changing dimensions, the book explores recurring design questions in analytical models. Its examples also address budgets, binning, multilingual models, key-value tables, and combining self-service work with enterprise BI.

See the same discipline through different tools

Five sections move from general modeling principles into Power BI, then examine modeling with DAX, Power Query and M, and SQL. That breadth makes it easier to compare where a task belongs and how different approaches affect the finished model.

Keep performance in the conversation

Performance tuning is part of the book’s structure, not an afterthought. Topics include storage modes, partitioning, pre-aggregation, composite models, and other ways to address the demands of a data model as it grows.

A useful reference for Power BI practitioners

Designed for beginner-to-intermediate readers, this book is relevant to analysts and developers who build Power BI models, as well as data professionals looking to revisit modeling fundamentals through practical examples. If your work involves turning source data into a dependable analytical structure, this guide offers a clear path from foundational concepts to more advanced implementation choices.