I have been working on Power BI for a while now and I often get confused when I browse through help topics of it. They often refer to the functions and formulas being used as DAX functions or Power Query, but I am unable to tell the difference between these two. Please guide me.
5 Answers
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 (often called Power Query, which uses M). There's a tip about this in the title bar of the edit window that says "Power Query Editor" (but you would have to know that M and PowerQuery are essentially the same thing). Also 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 (e.g. 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
answered Jun 18, 2018 at 21:40
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://learn.microsoft.com/en-us/power-bi/power-bi-reports-performance
Let me illustrate with an example. Imagine you have data in a comma-separated txt file. You want to analyze and transform this data using Power BI. First, you load the data into Power BI, and then you use Power Query to make transformations using M language, like adding logical columns or removing unnecessary ones. After the transformations, Power Query updates the table in Power BI.In Power BI, let's say you have a table named "SalesTable" with columns like "CountryRegion" and "Sales" from Power Query. Now, suppose you want to calculate sales only for the "South" region. This is where DAX comes in. With DAX, you define the exact metrics you need, such as calculating total sales only for the "South" region:
TotalSalesSouthRegion = CALCULATE(SUM(SalesTable[Sales]), SalesTable[CountryRegion] = "South")
You use this DAX formula to create custom visuals in Power BI. I hope this revised example accurately reflects the process in Power BI. Good luck!
