A refresher pls - which is more efficient (Power Query or DAX) for data source calls? I'll explain.

Hi， D_PBI 

It depends on your actual situation.

Please refer to Marco Russo's insight on Compare dax calculated column with power query calculated column.

When to use Power Query computed columns
You should use Power Query computed columns whenever the expression only depends on other columns of the same row of the table where you create the new column. This is the case for the Price Range column used in the example of this article.

If the data source is a relational database and you merge tables that can be joined in the data source efficiently, then it is a good idea to denormalize a table using Power Query functions – rather than importing multiple tables into the data model and then creating DAX calculated columns retrieving corresponding data from other tables using RELATED.

You should be careful when deciding whether to use Power Query computed columns, if you need to aggregate rows from other tables. The Power Query computed columns executes the aggregation on the data source, and this might take a long execution time. If this happens, you should consider a DAX calculated column to avoid a long processing times.

When to use DAX calculated columns
You should use DAX calculated columns whenever you cannot implement the same calculation in a Power Query computed column. The typical case is when aggregating data from other tables in the model. Another case would be the denormalization of tables coming from different data sources, because this would not be optimized using the query folding technique in Power Query.

---

Best Regards,
Community Support Team _ Eason