Prepare to become an expert in Data Analysis eXpressions, or DAX. The book reframes DAX not as a static formula language, but as a context-sensitive analytical language. The core promise is practical.
Readers will learn how to see, trace, control, and intentionally shape the filter context surrounding a measure. A formula may look unchanged, but a KPI card, matrix row, subtotal, grand total, chart axis point, slicer selection, or cross-highlight can cause it to evaluate under a different query context. This is why the same measure can appear to return different results across visuals. The book turns that behavior from a source of frustration into the central mental model for mastering DAX.
The AI angle is deliberately grounded. This is not a book about generic AI prompting. It explains why Copilot, data agents, natural-language Q&A, and LLM-based assistants depend on trusted semantic models and well-designed DAX measures. Readers learn how to validate AI-generated DAX, provide prompts with model and filter context, and understand how RAG-style retrieval, semantic metadata, and intelligent agents can support analytics without replacing DAX expertise.
Readers should choose this book because it fills a gap between beginner tutorials and dense reference manuals. It is not organized around memorizing every function. It is organized around the way DAX is used in professional Power BI and Microsoft Fabric environments: as reusable, governed business logic that must behave predictably under real user interaction.
What You Will Learn:
Who This Book is For:
This book is for Power BI developers, data analysts, BI engineers, report authors, semantic model developers, SQL-oriented data professionals, and Microsoft Fabric teams who want DAX measures to behave predictably in real reports.
Frank Banin
Power BI Interactive DAX Filter Context Semantic Model Microsoft Fabric Implicit Filters DAX Patterns DAX Optimization Copilot for Power BI Data Agents DAX