Power BI DAX VZ M :
A Beginner’s Guide 

Recently, I’ve been getting a lot of questions from students and Power BI learners about DAX and M , what they are, how they differ, and when to use each one. This confusion is completely understandable, especially since both languages are used to create calculations in Power BI but operate in very different ways.

To clear things up, I decided to answer all these questions in one place.

In this blog post, I’ll walk you through DAX and M from the ground up. We’ll look at what each language is designed for, explore their similarities and differences, discuss real-world use cases, and go through practical examples to show how the same problem can be solved using either DAX or M. By the end of this post, you’ll have a clear understanding of which language to use, when to use it, and why it matters for building efficient and high-performing Power BI reports.

DAX vs M Code in Power BI: Differences, Similarities, Use Cases, and Examples

Power BI is a powerful platform for transforming raw data into actionable insights. At the core of this process are two languages: M and DAX.

For many Power BI users, the distinction between these languages can be confusing at first, as they operate at different stages of the data and reporting workflow.

In practice, however, M and DAX play very different roles. Understanding when to use each one is a crucial skill that separates beginner Power BI users from confident, efficient report developers.

Key Differences Between DAX and M Code

Feature                                                      M                                                                                DAX

Used in                                                            Power Query                                                                   Data Model
Runs When                                                    Before data load                                                             After data load
Main Role                                                       Data preparation                                                            Data analysis
Interactivity                                                  Static                                                                                 Dynamic
Performance Impact                                  Reduces model size                                                        Affects query speed

Best For                                                          Cleaning & shaping                                                        Calculations & KPIs

Code Examples: M Code vs DAX

Let’s look at common scenarios to clearly see how M and DAX differ.

Example 1: Calculating Total Sales

Assume a Sales table with:

• Quantity
  
  

• Unit Price
  
  

Using M  (Power Query)

= Table.AddColumn(

    Source,

    "Total Sales",

    each [Quantity] * [Unit Price],

    type number

)

Explanation:

• A physical column is created during data load
  
  

• The calculation is static
  
  

• Values do not change with slicers or filters
  
  

Best When:

• The calculation is always the same
  
  

• You want better performance
  
  

• The value doesn’t need to be dynamic
  
  

Using DAX (Measure)

Total Sales =

SUMX(

    Sales,

    Sales[Quantity] * Sales[Unit Price]

)

Explanation:

• No column is stored in the model
  
  

• The calculation is evaluated dynamically
  
  

• Results change based on filters and slicers
  
  

Best When:

• You need interactive reporting
  
  

• Users filter by date, product, or region
  
  

• You want flexibility in visuals
  
  

Example 2: Filtering Data

Filtering Rows in M 

= Table.SelectRows(

    Source,

    each [Order Date] >= #date(2024, 1, 1)

)

Result:

• Only 2024 data is loaded
  
  

• Smaller model size
  
  

• Faster report performance
  
  

Filtering Data in DAX

Sales 2024 =

CALCULATE(

    [Total Sales],

    YEAR(Sales[Order Date]) = 2024

)

Result:

• Calculation is dynamic
  
  

• Can be combined with slicers
  
  

• Evaluated at query time
  
  

When to Use M 

Use M when:

• Data cleaning can be done once
  
  

• Transformations do not depend on user interaction
  
  

• You want to reduce model size
  
  

• Performance is a priority
  
  

Best practice : If it can be done in Power Query, do it in Power Query.

When to Use DAX

Use DAX when:

• Calculations must respond to filters or slicers
  
  

• You need KPIs, ratios, or time intelligence
  
  

• Results depend on report context
  
  

Best practice :  If it needs to be dynamic, use DAX.

A Common Mistake: Overusing DAX

Many Power BI users try to do everything in DAX , including data cleaning. This often leads to:

• Slower reports
  
  

• Complex formulas
  
  

• Hard-to-maintain models
  
  

A Better Approach

• Clean and shape data with M
  
  

• Analyze and calculate with DAX
  
  

This division keeps your model clean, fast, and scalable.

Now we have come to the end of this blog post.

DAX and M  aren’t competing technologies, they’re complementary tools that work best together inside Power BI.
M focuses on preparing and shaping your data before it ever reaches the model, while DAX takes that well-prepared data and turns it into meaningful insights through dynamic calculations.

One of the most valuable Power BI skills you can develop is knowing where a calculation belongs. Getting this right leads to cleaner models, better performance, and reports that are easier to maintain.

If you remember just one principle, remember this: Transform with M. Analyze with DAX.

When used together, M and DAX help you build faster, cleaner, and more powerful Power BI reports.

Feel free to connect with me if you have any questions, I’m always happy to help. Happy learning!