Why most data analysts struggle to move between DAX and SQL — and the bridge I wish I had 9 years ago

I work as a BI consultant, mostly between SQL Server and Power BI, for funded FinTech and telecom clients.

A pattern I've seen on basically every team: there's a Power BI dev who can write a 10-line DAX measure in their sleep, and a SQL dev who can tune a 100M-row query — and when the ETL team asks "give me this metric in T-SQL for the warehouse," neither one can do it cleanly.

It's not a skill gap. It's a thinking gap.

DAX thinks in filter context: you describe what you want, the engine works backward to evaluate it row-by-row. SQL thinks in set logic: you specify the join, the predicate, the grouping, and the engine builds a plan.

When you translate DAX to SQL, the dangerous move is going line-by-line. CALCULATE + FILTER does not always become a WHERE clause. SAMEPERIODLASTYEAR does not always become DATEADD. RANKX is not always ROW_NUMBER() — sometimes it's a window function, sometimes a self-join, sometimes a CROSS APPLY, depending on table size and what the optimizer can push down.

I put together a handbook of the 20 conversions that come up most often in real work — side-by-side DAX and T-SQL, plus a short performance note on each so you know which SQL pattern actually scales. Real examples from telecom CDRs and banking transactions, not toy datasets.

If you're a Power BI dev being asked to write SQL, or a SQL dev moving into Power BI, this is the bridge I wish someone had handed me in 2016.

DAX to SQL Conversion Handbook — $9, 27 pages, instant download.

https://growthwithshehroz.gumroad.com/l/dax-to-sql-handbook

Curious — anyone else in here straddling DAX and SQL daily? What's the conversion that trips you up most?