What are the main differences between DAX and M Language?
Power BI supports M Language and DAX to manage, manipulate, filter, and analyze the data. However, they are distinct from one another, are not interdependent, and each has a unique syntax, structure, and logic.
| DAX | M Language |
|---|---|
| Each of the roughly 250 functions used in Data Analysis Expression (DAX) formulas is covered in full in the DAX function reference, which also includes syntax, arguments, return values, and examples. | Each of the roughly 700 functions is covered in an article in the Power Query M function reference. These citations are created automatically by the in-product assistance. |
| Calculation operators can be classified as either arithmetic, comparison, text concatenation, or logical. | There are several common operators that can be used on null, including logical, number, time, date, datetime zone, duration, text, and binary. |
| Use DAX if you want to build a calculated column in Power BI. | On the other hand, you must utilize M language in Power Query Editor if you need to build a Custom Column. |
Power BI – Explain the ‘M language’
A robust “get data” experience with many options is offered by Microsoft Power Query. The ability to filter and mix, or “mash-up,” data from one or more of the many supported data sources, is a fundamental feature of Power Query. Using the Power Query Formula Language, such data mashups are expressed (informally known as “M”). Excel, Power BI, Analysis Services, and Data-verse are just a few of the Microsoft tools that Power Query integrates M documents into to enable repeatable data mashup.
Following DimDate sample Dataset have been used to perform M Formula Language Queries.
Dataset