Pages
376
Published
2019
Advanced DAX techniques and data modeling patterns for Power BI professionals
Move beyond basic measures and build Power BI models that handle real-world complexity with precision and speed.
Pro DAX with Power BI by Philip Seamark and Thomas Martens is a practical reference for analysts and BI developers who already know their way around Power BI and want to write DAX that actually performs. The book works through evaluation contexts, filter propagation, and advanced calculation patterns with concrete examples drawn from realistic data models, giving you the vocabulary and technique to solve problems you currently work around.
Most Power BI users hit a wall. Basic measures work fine until the business asks for something more nuanced: time intelligence across non-standard calendars, ratios that survive slicer selections, running totals that respect filter context. That is where DAX stops feeling intuitive and starts demanding real understanding.
Pro DAX with Power BI was written by two practitioners who spend their professional lives building and auditing Power BI models. Philip Seamark and Thomas Martens do not walk you through the ribbon or explain what a slicer is. They start where most books stop: evaluation context, filter propagation, and the precise mental model you need to predict what a DAX expression will return before you run it.
The book is organized around the concepts that trip up experienced users most often. You will work through row context and filter context in depth, understand how CALCULATE reshapes the filter environment, and learn why two expressions that look equivalent can return completely different results under different query conditions. From there you move into iterator functions, context transition, and the patterns that underpin virtually every advanced calculation you will ever write.
Data modeling decisions are treated as a first-class concern throughout. The authors show how table relationships, cardinality choices, and schema design directly affect what your DAX can and cannot express efficiently. A poorly structured model cannot be rescued by clever DAX, and this book makes that relationship explicit.
If you already know how to build a report in Power BI and want to stop guessing why your measures return unexpected numbers, this book gives you the systematic understanding to write DAX with confidence and defend every expression you ship.
Establishes the mental model for how DAX interacts with the tabular engine, covering tables, columns, measures, and the relationship between schema design and calculation behavior.
Breaks down row context and filter context from first principles, using step-by-step worked examples to show exactly how DAX determines which rows participate in any calculation.
Dissects the CALCULATE function argument by argument, showing how each filter argument adds, removes, or replaces filters and why this matters for every non-trivial measure you write.
Explains how context transition converts row context into filter context, then applies that understanding to SUMX, AVERAGEX, RANKX, and other iterator functions in realistic scenarios.
Covers ALLSELECTED, KEEPFILTERS, and filter table arguments, showing how to build measures that respect or ignore slicer selections in a controlled and predictable way.
Implements standard time intelligence functions for year-to-date, moving averages, and period-over-period comparisons, then extends the patterns to non-standard and fiscal calendars.
Examines how cardinality, cross-filter direction, and bidirectional relationships affect DAX results, with guidance on when each modeling choice helps or creates hidden problems.
Works through many-to-many relationship patterns and semi-additive calculations such as last-balance measures, with clear explanations of why standard aggregation functions fail in these scenarios.
Introduces DAX query plans and the storage engine versus formula engine distinction, then demonstrates how to use DAX Studio to profile slow measures and restructure expressions for speed.
Assembles the techniques from previous chapters into a set of complete, production-ready calculation patterns covering basket analysis, dynamic segmentation, and conditional ranking.
Yes. The authors assume you already write basic measures and calculated columns in Power BI. If you have never used SUM or IF in DAX, start with an introductory resource first.
The focus is on DAX and data modeling, which applies to Power BI Desktop and Analysis Services tabular models. Service-specific features such as deployment pipelines are not the subject of the book.
Apress titles typically include downloadable companion files accessible through the publisher's website. Check the book's page on apress.com for the source code or sample model downloads.
The core DAX language and evaluation context mechanics covered here have not changed in ways that invalidate the content. Some newer functions introduced after publication will not appear, but the fundamental patterns remain accurate.
The examples are framed around Power BI, but DAX evaluation context is identical in Power Pivot and Analysis Services tabular, so most of the content transfers directly to those environments.
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