What's the difference between DAX and Power Query (or M)?

Question

M and DAX are two completely different languages.

M is used in Power Query (a.k.a. Get & Transform in Excel 2016) and the query tool for Power BI Desktop. Its functions and syntax are very different from Excel worksheet functions. M is a mashup query language used to query a multitude of data sources. It contains commands to transform data and can return the results of the query and transformations to either an Excel table or the Excel or Power BI data model.

More information about M can be found here and using your favourite search engine.

DAX stands for Data Analysis eXpressions. DAX is the formula language used in Power Pivot and Power BI Desktop. DAX uses functions to work on data that is stored in tables. Some DAX functions are identical to Excel worksheet functions, but DAX has many more functions to summarize, slice and dice complex data scenarios.

There are many tutorials and learning resources for DAX if you know how to use a search engine. Or start here.

In essence: First you use Power Query (M) to query data sources, clean and load data. Then you use DAX to analyze the data in Power Pivot. Finally, you build pivot tables (Excel) or data visualisations with Power BI.

• M is the first step of the process, getting data into the model.

(In PowerBI,) when you right-click on a dataset and select Edit Query, you're working in M (also called Power Query). There's a tip about this in the title bar of the edit window that says Power Query Editor. (but you have to know that M and PowerQuery are essentially the same thing). Also (obviously?) when you click the get data button, this generates M code for you.

• DAX is used in the report pane of PowerBI desktop, and predominantly used to aggregate (slice and dice) the data, add measures etc.

There is a lot of cross over between the two languages (eg you can add columns and merge tables in both) - Some discussion on when to choose which is here and here

Think of Power Query / M as the ETL language that will be used to format and store your physical tables in Power BI and/or Excel. Then think of DAX as the language you will use after data is queried from the source, which you will then use to calculate totals, perform analysis, and do other functions.

• M (Power Query): Query-Time Transformations to shape the data while you are extracting it
• DAX: In-Memory Transformations to analyze data after you've extracted it

One other thing worth mentioning re performance optimisation is that you should "prune" your datatset (remove rows / remove columns) as far "upstream" - of the data processing sequence - as possible; this means such operations are better done in Power Query than DAX; some further advice from MS here: https://docs.microsoft.com/en-us/power-bi/power-bi-reports-performance