I'm new to Power BI and I'm confused about where to perform my data cleaning. Should I be creating new columns using DAX formulas, or is it better to handle everything within the Power Query editor before loading the data? What are the performance implications for the data model when dealing with millions of rows in a production environment?
3 answers
The general rule of thumb in the Power BI community is "transform as far upstream as possible." This means if you can do it in the source database, do it there. If not, Power Query (M language) is your next best bet. Power Query transformations are performed during the data refresh and are highly compressed by the VertiPaq engine. DAX calculated columns, however, are computed at row level and stored in the model, which can bloat file size. Reserve DAX for "Measures" that need to respond to user filters and slicers dynamically during report interaction.
That is a great explanation, but wouldn't using Power Query for complex conditional logic make the refresh time significantly longer for very large datasets compared to a simple DAX measure?
Use Power Query for "shaping" the data and DAX for "analyzing" the data. If you need a new category, use Power Query. If you need a Year-over-Year growth percentage, use DAX.
Exactly, Jessica. Keeping those roles separate makes the data model much easier to maintain as your dashboard grows in complexity.
It's a trade-off, Michael. While Power Query might increase the refresh time, it keeps the report's "snappiness" high for the end-user. A heavy DAX calculated column has to be recalculated frequently and consumes RAM. For a better user experience, a longer backend refresh is usually preferred over a laggy report. If refresh time becomes a massive bottleneck, that's when you should look into Incremental Refresh settings.