Question

When should I use Power Query vs DAX for transforming data?
Choosing between Power Query and DAX is critical for optimizing performance and data model efficiency. This question examines scenarios where using Power Query (M language) for pre-load transformations is more effective versus using DAX for in-model, post-load calculations—especially in large-scale datasets where transformation timing and memory usage matter.

Answer 1

Before the data is loaded into the model, use Power Query to clean, shape, and perform pre-load transformations, such as deleting columns, combining tables, or filtering rows. It enhances performance while decreasing the size of the model.

After the data has been loaded, use DAX to perform calculations, such as generating measures, calculated columns, and dynamic aggregations in response to user input.

To put it briefly:

• Pre-loading Power Query improves performance and reusability.
• For user-driven computations and graphics, DAX = post-load is ideal.