Graphed LogoGraphed
Graphed LogoGraphed
How It WorksAgentsIntegrationsPricing

What Code Does Power BI Use?

Cody Schneider••8 min read

If you're starting to use Power BI, it's natural to wonder what's happening behind the clicks. You might ask, "Do I need to be a coder to use this? What code does Power BI even use?" The short answer is that Power BI has two of its own specialized languages, but you don't need to be an expert developer to get massive value from the tool. This article breaks down exactly what those languages are, what they do, and how much you really need to know about them.

GraphedGraphed

Build AI Agents for Marketing

Build virtual employees that run your go to market. Connect your data sources, deploy autonomous agents, and grow your company.

Watch Graphed demo video

The Two Core Languages of Power BI: DAX and M

Think of Power BI as a house. The work you do in the Power Query Editor - cleaning up your messy data - is like the plumbing and electrical work. It happens behind the walls, and it has to be solid for everything else to work. The language that powers this is called M. Once the foundation is set, you start arranging the furniture and decorating. This is the analysis and visualization you do in the main Power BI report view, and it's powered by a formula language called DAX.

  • M Code is used in the Power Query Editor to connect to and transform your raw data.
  • DAX (Data Analysis Expressions) is used in the report view to create calculations, measures, and custom columns to analyze your data model.

Let's look at each one so you can understand its role and feel more comfortable working with it.

Meet M: The Language of Power Query

Every Power BI project starts with data, and that data is rarely perfect. It might have extra blank rows, columns you don't need, or formatting that needs to be fixed. The Power Query Editor is your workshop for cleaning and preparing this data, and M is the language working silently in the background.

What is M and Why Do You Need It?

M stands for "Mashup," and it's what's known as a data transformation language. The great news is that for 90% of your work, you won't be writing M code from scratch. Instead, every time you click a button in the Power Query interface, Power BI writes the corresponding M code for you.

Here are common tasks you might do where M is being generated for you:

  • Removing Columns: Deleting irrelevant columns from a large spreadsheet.
  • Splitting Columns: Taking a "Full Name" column and splitting it into "First Name" and "Last Name".
  • Filtering Rows: Removing rows that have empty values or don't meet certain criteria (e.g., keeping only sales from the "USA" region).
  • Changing Data Types: Converting a column mistakenly formatted as text into a number or date.
  • Unpivoting Data: Restructuring your table from a wide format (like months spread across columns) into a tall format that's easier for analysis.

Free PDF · the crash course

AI Agents for Marketing Crash Course

Learn how to deploy AI marketing agents across your go-to-market — the best tools, prompts, and workflows to turn your data into autonomous execution without writing code.

Where Do You Find M Code?

While Power BI writes the code for you, you can always peek behind the curtain to see it. Inside the Power Query Editor, go to the View tab and click on the Advanced Editor.

You'll see a window with the M code that records every step you've taken to clean your data. It might look something like this for a simple process of loading an Excel file and changing a data type:

let
    Source = Excel.Workbook(File.Contents("C:\Users\YourName\Documents\SalesData.xlsx"), null, true),
    Sales_Sheet = Source{[Item="Sales",Kind="Sheet"]}[Data],
    #"Promoted Headers" = Table.PromoteHeaders(Sales_Sheet, [PromoteAllScalars=true]),
    #"Changed Type" = Table.TransformColumnTypes(#"Promoted Headers",{{"OrderID", Int64.Type}, {"OrderDate", type date}, {"Region", type text}, {"Amount", type number}})
in
    #"Changed Type"

It can look intimidating, but just know that this is a repeatable script. The key takeaway is that you almost never have to write M yourself. You just need to know it’s there, powering the easy-to-use interface.

Understanding DAX: The Brains Behind Your Visuals

Once your data is clean and loaded into the main Power BI model, it's time to analyze it. This is where DAX comes in. DAX, or Data Analysis Expressions, is a formula language that lets you perform calculations and pull insights from your data.

What is DAX and How is it Different from Excel?

If you've used formulas in Excel, DAX will feel somewhat familiar, but it's far more powerful. The biggest difference is that Excel formulas operate on individual cells (like A1 + B1), while DAX formulas operate on entire columns and tables (like calculating the sum of every value in the 'Revenue' column).

You use DAX primarily to create two things:

  • Measures: These are on-the-fly calculations that respond to user interactions in a report. For example, a "Total Sales" measure will recalculate itself when you filter the report by a specific year or region. This is the most common use of DAX.
  • Calculated Columns: This adds a new column to your existing data table based on a formula. For example, you could create a "Profit Margin" column by calculating it from the 'Cost' and 'Revenue' columns. Unlike measures, these are calculated once when the data refreshes and stored in your model.
GraphedGraphed

Build AI Agents for Marketing

Build virtual employees that run your go to market. Connect your data sources, deploy autonomous agents, and grow your company.

Watch Graphed demo video

Practical DAX Examples for Marketers and Sales Teams

The best way to get a feel for DAX is to see it in action. Here are a few examples you might use in a business context.

Example 1: Calculating Total Revenue (A Simple Measure)

This is often the first measure anyone creates. It's a simple aggregation of a column.

Total Revenue = SUM(Sales[Revenue])

Here, SUM is the function, and Sales[Revenue] tells it to look at the 'Revenue' column inside the 'Sales' table.

Example 2: Calculating Year-to-Date Revenue (A More Complex Measure)

This is where DAX truly shines and goes beyond simple aggregations. Power BI has built-in "time intelligence" functions that make time-based analysis easy.

Revenue YTD = CALCULATE([Total Revenue], DATESYTD('Calendar'[Date]))

Let's break that down:

  • CALCULATE is one of the most powerful functions in DAX. It modifies the context in which a calculation is performed.
  • [Total Revenue] is the measure we created earlier.
  • DATESYTD('Calendar'[Date]) is a time intelligence function that creates a filter for all dates from the beginning of the year up to the latest date in the current context.

Example 3: Creating a Sales Tier (A Calculated Column)

Imagine you want to categorize deals as "Large" or "Small" based on their value. You can do this with a calculated column and an IF statement.

Deal Size Tier = IF(Sales[Revenue] > 5000, "Large Deal", "Small Deal")

This formula checks each row in the 'Sales' table. If the value in the 'Revenue' column is greater than 5000, it puts "Large Deal" in the new column for that row, otherwise, it puts "Small Deal."

A Quick Note on "Evaluation Context"

The concept of "Context" is the most fundamental principle in DAX. In simple terms, it means that the result of a single DAX measure can change based on how it's being viewed in a report. Your Total Revenue measure will show one big number in a card visual. But if you put it into a bar chart with 'Country' on the axis, the same measure automatically shows you the revenue for each country, because the "context" for each bar is a different country. Understanding this principle is the first big step to mastering DAX.

Free PDF · the crash course

AI Agents for Marketing Crash Course

Learn how to deploy AI marketing agents across your go-to-market — the best tools, prompts, and workflows to turn your data into autonomous execution without writing code.

The Supporting Cast: SQL, Python, and R in Power BI

While M and DAX are the native languages, Power BI also plays well with others, especially for grabbing data or performing advanced statistical analysis.

Using SQL to Grab Data

If your data is stored in a relational database like SQL Server, you can write a SQL query to directly pull the specific data you need into Power BI. Instead of pulling an entire massive table and then using the Power Query interface to filter it down, you can use a SQL statement to only bring in the columns and rows you need from the start. This happens at the very beginning of the data connection process.

Using Python and R for Advanced Analytics

For users who are more data-science oriented, Power BI allows you to run Python and R scripts. You can use these languages for:

  • More complex data transformations in Power Query than what the standard interface allows.
  • Performing advanced statistical analysis, like forecasting or clustering.
  • Building highly customized, unique visuals that aren't available out of the box.

For most marketers, salespeople, and business owners, these are not necessary. Think of them as power-ups available for highly specialized tasks, not everyday requirements.

Final Thoughts

Power BI relies on two core languages: M for preparing and cleaning your data behind the scenes, and DAX for enhancing your data model with custom calculations and measures. You'll primarily interact with M through the user-friendly Power Query Editor, and you'll typically start writing simple DAX formulas before moving on to more complex ones as your needs evolve.

Learning this new set of tools can involve a real learning curve, especially when you are just trying to get a quick answer to understand business performance. When we built Graphed our goal was to eliminate this complexity. We designed it so you could bypass the need to master Power Query transformations or DAX syntax. Instead of building reports by hand, you can just ask questions in plain English - like "Compare my website sessions from mobile vs. desktop last month" - and instantly get a live, interactive dashboard without writing a single line of code.

Related Articles