Engineering Work & Projects

Learning DAX requires developing an intuition for the evaluation context (row context vs. filter context vs. context transition). Passive video lectures provide zero feedback on subtle calculation mistakes (such as implicit context transitions in iterators or incorrect ALL/REMOVEFILTERS usage). Existing tools require firing up full Power BI Desktop with local tabular instances, creating massive friction.

We analyzed the cognitive load of debugging DAX. We identified that learners need three immediate pillars in one cockpit: a responsive formula bar with syntax checking, a live visual card simulating the rendered visual scalar, and an active filter context inspector displaying applied slicers and cross-filters.

We built DAXQuest as a Next.js application with a custom client-side DAX tokenizer and challenge validation engine. We integrated Monaco Editor with Power BI DAX autocompletion, context-aware column suggestions, and interactive parameter hovers. We implemented 47 progressive engineering challenges spanning Aggregations, CALCULATE context transition, Iterators, Time Intelligence, and Capstone Sales Analytics.

A zero-install interactive platform validating DAX expressions against realistic star schemas in under 50 milliseconds. Deployed to production with 47 verified challenges, 20 lessons, an automated diagnostic assessment, and a comprehensive DAX reference guide.

For enterprise teams with 5+ BI engineers, the binary .pbix file is a bottleneck. Merging two branches is impossible; files overwrite each other; measures break silently in production; and code reviews happen post-deployment. Teams waste hours manually synchronizing measure definitions.

With Microsoft's release of the PBIP format and Tabular Model Definition Language (TMDL), semantic models became plain-text files. However, tooling to enforce formatting standards, measure documentation, and automated deployment checks was missing.

We designed BIKit as an engineering CLI and GitHub Action runner that inspects TMDL files. It validates DAX formatting standards, flags undocumented measures, detects orphan columns, and verifies foreign-key integrity prior to merging pull requests into the production branch.

Teams utilizing PBIP + BIKit operate with true Git pull request workflows. Merge conflicts are resolved as plain text, measure regressions are caught in CI, and deployments to Fabric workspaces occur automatically.

Organizations frequently run out of Power BI Premium capacity memory without knowing why. Developers often leave transaction IDs, system GUIDs, and high-precision timestamps in the model, unknowingly consuming critical capacity RAM while visuals experience calculation lag.

Profiling enterprise models showed that the majority of memory bloat typically comes from a small set of high-cardinality columns that no user visual ever queries. Furthermore, bidirectional relationships between dimensions created costly storage engine callback loops.

We built diagnostic heuristics that inspect semantic model metadata (BIM, TMDL, and VertiPaq Analyzer metrics). The tool automatically scores models across 10 engineering criteria: column cardinality, relationship ambiguity, DAX iterator usage, auto date/time table overhead, and Fabric Direct Lake readiness.

Automated diagnostic reports that pinpoint high-cardinality columns, identify uncompressed dictionaries, and flag non-folding M steps before deployment to production capacities.