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:

• How Power BI report definitions and interactions create implicit filter context, and how those hidden filters change measure behavior across cards, charts, matrix rows, subtotals, and grand totals.
• How semantic models, relationships, expanded tables, column lineage, and table expressions define what data is in scope for DAX evaluation.
• How to use CALCULATE, CALCULATETABLE, REMOVEFILTERS, ALL, KEEPFILTERS, ALLSELECTED, TREATAS, USERELATIONSHIP, CROSSFILTER, and related functions to shape context intentionally.
• How to solve recurring business reporting problems with reusable patterns for percentages, ratios, growth rates, hierarchy-aware totals, time intelligence, disconnected selectors, multi-fact models, and scalable enterprise metrics.
• How to design, validate, and document DAX measures so they remain trustworthy for dashboards, semantic models, Copilot explanations, data agents, and AI-assisted analytics workflows.

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 is a senior data and analytics professional with more than 18 years of experience designing scalable, enterprise-grade business intelligence solutions. He holds an MSc in Predictive Analytics from Northwestern University and advanced Microsoft certifications in Microsoft Fabric, Azure Data Engineering, and Generative AI for Business.

Frank has used and taught analytical technologies such as MDX and DAX since 2010, with a specialized focus on interactive Power BI reporting since 2015. He has led enterprise BI projects in full-time and consulting roles, including at Bloomberg, Microsoft, and Merrill Lynch, delivering high-impact solutions such as investment dashboards, governed semantic models, and embedded analytics platforms.

He brings a practical blend of technical depth and business fluency across semantic modeling, dimensional modeling, DAX optimization, performance tuning, and analytics delivery. His writing combines hands-on engineering experience with approachable teaching, helping data professionals understand not only how to write code, but how analytical systems behave in real reports.