DAX (Data Analysis Expressions) and M language are both used to manipulate data and perform calculations in Microsoft Power BI and Excel Power Pivot. Here are the main differences between both languages:
1- Purpose: DAX is a formula language used to create calculations and aggregations in Power BI and Excel. M is a functional programming language used to query and transform data in Power Query.
2- Data manipulation: DAX is used to manipulate data within tables or columns, such as performing calculations, aggregating data, or creating calculated columns or measures. M is used to manipulate data before it is loaded into the data model, such as filtering, merging, transforming, or reshaping data from different sources.
3- Syntax: DAX has a syntax similar to Excel formulas, using functions and operators to perform calculations.
Total Sales= SUM(Sales[Amount])
M uses a functional programming syntax, using functions and expressions to transform and query data.
Example- Table.SelectRows, Table.AddColumn, Table.TransformColumns, and Text.Split etc.
4- Data sources: DAX works with data models created in Power BI or Excel, while M works with a variety of data sources, such as Excel files, databases, or web services.
5- Complexity: DAX is designed for complex calculations and aggregations, and can be easy for non-programmers to learn and use. M is designed for more complex data transformations and may require more programming skills.
6- Performance: DAX is optimized for performance within the data model, and its calculations and aggregations are done in memory. M is optimized for performance during data transformation and loading, and can perform operations such as filtering, merging, and grouping more efficiently than DAX.
7- Functions: DAX has a large library of functions that can be used for calculation and aggregation, including mathematical, statistical, and time intelligence functions. M also has a library of functions that can be used for data transformation and cleaning, such as filtering, grouping, and partitioning functions.
8- Error Handling: DAX has a built-in error handling mechanism that can detect and handle errors during calculation and aggregation operations. M also has an error handling mechanism that can handle errors during data transformation and loading.
9- Interoperability: DAX is primarily used in Power BI and Excel, whereas M can be used in other tools such as Power Query and SQL Server Integration Services.
10- Learning Curve: DAX is generally easier to learn and use than M, as it uses a syntax similar to Excel formulas and requires less programming knowledge. M requires more programming knowledge and may have a steeper learning curve for non-programmers.
In short, DAX is used to create calculations and aggregations within the data model, whereas M is used to query and transform data from various sources before loading them into the data model.
DAX is easier for non-programmers to learn and use, while M is more suited to complex data transformations and requires more programming skills.