Power BI is highly regarded within the data community for its user-friendly interface and powerful features, which enable in-depth analysis of datasets. One of its greatest strengths is its accessibility: it caters not only to technical teams, but also to business users, making it a versatile tool for organisations of all kinds.

Developed by Microsoft in 2015, Power BI incorporates a variety of tools into its architecture that enable data teams to process the entire data lifecycle, from ingestion and transformation to visualisation.

DAX and Power Query are both Power BI internal assets used for data processing, but they serve different purposes. Employees often confuse them, but this article explains the differences so that you will no longer be confused by either language.

Power Query for Smarter Data Preparation in Power BI?

Power Query is a tool that plays a big part in the data analysis workflow, it is used for data preparation and transformation.

Indeed, when analysing data in Power BI or any other tool, the first step is to clean the data. For example, if you fail to remove duplicates, your graphs will produce inaccurate analyses and lead to poor business decisions due to the poor quality of your data.

Microsoft created Power Query to solve this problem. It enables you to remove duplicates, as well as merge and shape data. You can also combine data from multiple sources to build a comprehensive analysis.

Power Query provides a graphical interface with tab options at the top of the screen, similar to those used for designing a PowerPoint presentation. You can also benefit from advanced functions such as creating a pivot table.

Mastering Data Analysis with DAX in Power BI

DAX (Data Analysis Expression) is a formula language used for calculations inside Power BI. DAX allows you to perform more in-depth analytics by creating calculated columns.

For example, imagine a dataset containing a list of employees and their names and dates of birth. Using DAX, you could create a calculated column to compute each employee’s age by comparing their date of birth with the current date. This adds a new field to the table that stores the age of each employee.

In contrast, a measure does not create or store new data in the table. Instead, it calculates results dynamically based on the context of your report. For instance, you could create a measure called 'Total Employees' that counts the number of employees.

This value would automatically adjust depending on the filters applied — for example, showing the number of employees by age group.

When to Use Power Query and DAX in Your Workflow?

In summary, Power Query is used for data transformation and cleaning, while DAX is used for performing advanced analytics based on existing collections.

In fact, Power Query is intended for data preparation, and optimising this process can reduce refresh times. DAX is not designed for data transformation and has not been built for that purpose.

My best advice is to experiment with both tools so that you can combine their strengths in real-world projects.