Power Query is the data preparation tool built into Power BI and Excel. It connects to data sources, then cleans and reshapes the raw data before analysis. Every step is recorded and re-runs automatically on refresh, so the same preparation never has to be redone by hand.
What Power Query is
Power Query is a data preparation engine. Microsoft's overview of Power Query describes it as "a data transformation and data preparation engine" that "comes with a graphical interface for getting data from sources and a Power Query editor for applying transformations".
In plain terms, it's the step that happens before any chart is built. Raw data is rarely tidy: dates are in the wrong format, columns are mislabelled, two spreadsheets need joining. Power Query is where you sort all of that out, so the data going into Power BI or Excel is clean and ready to use.
It isn't a separate product you buy. Power Query is a feature inside tools you already have, and it started life in Excel before becoming a core part of Power BI.
What Power Query does
Power Query handles the three jobs that come before analysis, often shortened to ETL: extract, transform and load. Microsoft notes that business users "spend up to 80% of their time on data preparation", and this is the work Power Query speeds up.
- Connect: it links to data wherever it lives, from Excel files and CSVs to databases, web pages and cloud services. Microsoft lists connectivity to hundreds of different sources.
- Clean and reshape: it removes columns, filters rows, fixes data types, splits or merges fields and joins tables together. There are over 350 built-in transformations.
- Load: it sends the finished, tidy data into the report or workbook, ready for charts and calculations.
The real benefit is that this work is reusable. You define the steps once; next month, you refresh, and Power Query repeats every step on the new data automatically.
How Power Query works: the editor and applied steps
You do the work in the Power Query Editor, a separate window with its own ribbons and menus. Microsoft calls it "the primary data preparation experience". You point it at your data, then pick transformations from the menus rather than writing code.
Each change you make is saved as a step in an Applied Steps list. That list is the recording of your whole process, from first connection to finished table. When the underlying data changes, you don't repeat the work: Microsoft's guide to Power Query in Excel, where it's labelled Get & Transform on the Data tab, explains that on a refresh "each step runs automatically".
If you ever need to change something, you click the step where it went wrong and adjust it; everything after it re-runs. That's why a query built once keeps paying off.
The M language behind it
Behind the menus, Power Query writes code in a language called M, or the Power Query M formula language. Every click in the editor generates M, so the language is always doing the work even when you never see it.
For most people that's the whole story: you don't need to learn M to get real value from Power Query. Microsoft confirms the editor "automatically creates the M code required" so "you don't need to write any code". M only becomes useful when a transformation goes beyond what the menus offer, and plenty of users never reach that point.
M is also a different language from DAX, the formula language used for Power BI calculations. The two are easy to confuse because both appear in Power BI, but they do separate jobs at separate stages, covered next.
Power Query vs DAX: where each fits
In Power BI, Power Query and DAX are the two halves of working with data, and they run in order. Power Query goes first: it connects to the raw data and shapes it as the report's model is built.
DAX comes second. Once the data is loaded and tidy, DAX, the formula language behind Power BI calculations, works out new figures from it while someone views the report. Those new figures include totals, growth rates and running averages. A simple way to remember it: Power Query prepares the data; DAX analyses it.
You'll meet both when learning Power BI properly. Power Query is usually the gentler starting point, because clicking through the editor feels closer to working with a spreadsheet.
Clean data is where good reports begin Power Query is approachable, but knowing which transformations to use - and how to build a query that stays reliable - is a skill worth investing in. Our two-day, hands-on Power BI Masterclass teaches Power Query the practical way, shaping real data rather than memorising menus. If you are still comparing courses, our Power BI training buyer's guide sets out how to choose a UK course.
