Introduction

Power Query is how data gets into Power BI, and how it gets cleaned up before anyone builds a report on top of it.

Source Data
     |
     | Power Query
     |
Clean, Modeled Tables
     |
     | loaded into
     |
Power BI Data Model

Every Import or Dual-mode table in a Power BI model passes through Power Query first.

What Power Query Does

Power Query handles three jobs:

Power Query
   |
   +-- Connect to data sources
   |
   +-- Transform and clean data
   |
   +-- Load the result into the model

It connects to databases, files, APIs, and dozens of other source types, then reshapes that raw data into the tables a Power BI model actually needs.

The Power Query Editor

Transformations are built visually in the Power Query Editor, opened from Home > Transform Data in Power BI Desktop.

Source Data (preview)
       |
       | apply a step
       |
Transformed Preview
       |
       | apply another step
       |
Final Table

Every transformation is recorded as a step, shown in the Applied Steps pane, and can be reordered, edited, or removed.

See Power Query Editor for a closer look at the interface itself.

Under the Hood: M Language

Every action in the Editor generates code in M, Power Query's underlying formula language.

Click "Remove Columns" in the UI
        |
        | generates
        |
Table.RemoveColumns(Source, {"Column1"})

Most work happens through the visual editor, but M can also be written or edited directly in the Advanced Editor for transformations the UI doesn't expose. See M Language for more detail.

Queries

Each data source loaded into a model becomes a query — a named sequence of steps that produces one table.

Query: "Sales"
   |
   +-- Source
   +-- Filtered Rows
   +-- Renamed Columns
   +-- Changed Type
   |
   Result: FactSales table

A single Power BI model typically has one query per table, visible in the Queries pane on the left side of the Editor.

Applied Steps Are Sequential

Steps run in order, each one operating on the result of the step before it.

Source
   |
   v
Step 1: Filter Rows
   |
   v
Step 2: Rename Columns
   |
   v
Step 3: Change Types
   |
   v
Final Table

Reordering steps can change the result, or break later steps that depended on the earlier order — a common source of Power Query errors.

Query Dependencies

Queries can reference other queries, letting transformation logic be reused instead of duplicated.

Query: RawSales
   |
   | referenced by
   |
Query: CleanedSales
   |
   | referenced by
   |
Query: FinalSales

This is useful for staging raw, unfiltered data in one query, then building cleaned or filtered versions from it without re-fetching the source each time.

Where Power Query Fits

Data Source
     |
     | Power Query (extract, transform)
     |
Power BI Model
     |
     | DAX (calculate, aggregate)
     |
Report Visuals

Power Query shapes the data. DAX calculates on top of the shape Power Query produced. Getting the Power Query layer right makes the DAX layer significantly simpler.

Best Practices

• Do as much cleaning and shaping as possible in Power Query, rather than compensating for messy data with complex DAX.
• Name queries and steps clearly — "Changed Type1" tells the next person nothing.
• Reference queries to reuse logic instead of copy-pasting the same steps across multiple queries.
• Remove unnecessary columns early, since later steps process less data as a result.

Common Mistakes

Doing Too Much in DAX Instead of Power Query

Reshaping or cleaning data with DAX measures, when Power Query could have produced the correct shape at load time, adds unnecessary complexity to the model.

Ignoring Step Order

Reordering steps without understanding their dependencies can silently break a query — a later step referencing a column name that an earlier step renamed, for example.

Not Using Query References

Duplicating the same set of cleaning steps across multiple queries, instead of building one shared query and referencing it, means every future fix has to be repeated in every copy.

Introduction Checklist

Before considering a Power Query setup finished:

• Queries and steps have clear, descriptive names.
• Shared logic is built once and referenced, not duplicated.
• Unnecessary columns and rows are removed as early as possible.
• The result loads into the model as clean, well-typed tables — not raw, unprocessed data.

Next Steps

Continue learning Power Query:

• M Function Reference
• Power Query Editor
• Transformations
• M Language

See it applied end to end: Build a Complete Sales Analysis Report cleans a real messy export from scratch, then builds a full model and report on top of it.