Understand DAX Context in Power BI: CALCULATE, Filter Context & Row Context (Part 2)

Video by: Reid Havens

The single biggest concept separating DAX beginners from confident report builders is context. This interactive guide breaks down how Power BI evaluates every formula you write: what filter context is, how CALCULATE manipulates it, the difference between row context and filter context, how context transition bridges the two, and why iterator functions like SUMX create their own row context. If CALCULATE has ever confused you, start here.

Topics covered:

• The mental model shift: why DAX doesn't think like Excel

• Query context: how rows and columns shape evaluation

• Filter context: slicers, relationships, and implicit filters

• CALCULATE: overriding, replacing, and adding filter arguments

• Row context: calculated columns, iterators, and the current row

• Context transition: what happens when CALCULATE meets row context

• Iterator functions: SUMX, AVERAGEX, and row-by-row evaluation

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[DAX] Lib - From Zero to Community Hero (with Jake Duddy)

LIVESTREAM DATE/TIME 📅

MAY 15TH 2026 (Pacific Time)

DESCRIPTION 📄

User-defined functions landed in DAX last September, and SQLBI launched DAXLib alongside them as a community repo for sharing libraries. There are already 30+ packages people can pull straight into their models. If you've built a UDF that made your own life easier, odds are someone else is working around the same problem.

This stream is about closing that gap. Jake Duddy joins me to walk through what it actually takes to turn a working UDF into a published DAXLib package that other people can install and use. Jake recently contributed EvaluationContext.Colour (a HEX manipulation library for SVG and conditional formatting work), so he's been through the workflow recently and has the bruises to show for it.

What we'll cover:

• DAXLib: what it is and why SQLBI built it

• Model-independent vs model-dependent functions, and which belong in a library

• Packaging a UDF for publication

• Submitting through GitHub

• What separates a useful contribution from noise

• Q&A throughout

GUEST BIO (Jake Duddy)👤

Jake Duddy is a Microsoft Data Platform MVP and Power BI SME based in Birmingham, UK. He runs the Evaluation Context blog covering Power BI, Fabric, DAX, and SSAS, and speaks regularly at SQLBits, the Power BI & Fabric Summit, and the Microsoft Data Platform Group Birmingham. Links to DAXLib, Jake's blog, and his EvaluationContext.Colour library are in the comments. #PowerBI #DAX #MicrosoftFabric #DAXLib #UDF

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Master DAX Foundations in Power BI: Calculations, Data Models & Filter Flow (Part 1)

Video by: Reid Havens

Start thinking in DAX instead of Excel. This interactive guide walks through everything you need to build a mental model for Power BI calculations: what DAX actually is, how measures differ from calculated columns, table and column references, star schema basics, and how filters flow from dimension to fact tables. Part 1 of a 3 part DAX Fundamentals series.

Topics covered:

• Excel formulas vs DAX: what changes and why

• Measures vs calculated columns: when to use each

• DAX syntax: table references, column references, and measure calls

• Star schema: fact tables, dimension tables, and relationships

• Filter flow: how slicers and relationships shape your results

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Power BI Report-Scoped Measures: 6 Patterns for Better Reports

Video by: Reid Havens

Report-scoped measures (aka report-specific measures) are one of the easiest ways to level up a Power BI report without turning your semantic model into a junk drawer.

In this video, I walk through my Visual & Report-Scoped Measures guide and show the art of the possible. No deep DAX theory, no data engineering detours, just practical patterns you can steal immediately.

We’ll cover:

• What “report-scoped” measures are (and when you should use them)

• Six practical patterns for enhancing report UX with measures

• Dynamic titles that respond to context

• Conditional formatting driven by measures (so visuals explain themselves)

• How to keep your model clean while still making reports more interactive and readable

If you build reports for humans (not just for your own amusement), this is a strong set of patterns to have in your toolkit.