See how supported parts of your DAX behave.
Paste a measure and get a deterministic walkthrough of detected context and aggregation behavior. Unsupported or partially analyzed constructs are called out instead of silently guessed.
SQLBI explains it once. Then you have to apply it to your measure.
Marco and Alberto’s articles on row context, filter context, and context transition are the canonical DAX explainers. They’re visual, they’re honest about the cliff, and they’re written for the right audience — you.
The catch is they explain the concept with their measure, against their data model. When the measure that’s confusing you isn’t Marco’s, you have to translate.
DAX Solver applies its supported analyzer to your measure and labels the analysis complete, partial, or unsupported. Model-bound probes appear as evidence; symbolic sections stay visibly symbolic.
One textarea. No setup.
Drop a measure in. The page tells you immediately which of the six SQLBI concepts apply — starting filter context, row context, context transition, filter modification, aggregation, recap — before you commit.
Don’t have a model? Measure-only mode renders structural sections with symbolic values. Bind a built-in model or an eligible upload for bounded execution evidence where the analyzer supports it.
A consistent visual grammar for context changes.
The walkthrough uses pills, before/after context groups, removed-filter marks, and iterator disassembly so you can follow detected filter and row-context changes section by section.
The document is deterministic. Values backed by a model include execution evidence; otherwise they are labelled symbolic. Partial constructs and limitations remain visible in the saved walkthrough.
Five measures, five SQLBI concepts.
Each link below opens a real walkthrough — same renderer you’d get from pasting your own measure. The demos cover the canonical lessons from the SQLBI articles on row & filter context, context transition, and filter manipulation.
Slicer Builder — filter a report by a measure threshold.
A sibling tool built on the same SQLBI pedagogy — for the “I want a slicer to filter by sales amount” case Marco rebuilt in his article on filtering measures through slicers. Granularity-aware, calculation-group powered.
Ready to walk through your measure?
Public-model walkthroughs can be shared safely. Personal-model sharing follows source privacy rules, and every analysis preserves its support and evidence labels.