Introduction

Power Query enables users to extract, transform, and load (ETL) data efficiently, but poor query optimization, excessive memory usage, and failure to leverage query folding can lead to slow refresh times and high resource consumption. Common pitfalls include retrieving too much data from the source, applying transformations that break query folding, using inefficient joins, loading unnecessary columns, and not leveraging parameterized queries. These issues become particularly problematic in large datasets and enterprise-scale reports where refresh speed and performance are critical. This article explores Power Query performance bottlenecks, troubleshooting techniques, and best practices for optimizing data transformations.

Common Causes of Power Query Performance Issues

1. Breaking Query Folding, Resulting in Slow Performance

Query folding allows transformations to be pushed to the data source, but certain operations prevent this, leading to inefficient processing.

Problematic Scenario

let
    Source = Sql.Database("Server", "Database"),
    FilteredRows = Table.SelectRows(Source, each [Year] >= 2020),
    AddedColumn = Table.AddColumn(FilteredRows, "NewColumn", each Text.Upper([Name]))
in
    AddedColumn

Applying `Text.Upper()` on a column prevents query folding, forcing Power Query to load all data into memory before processing.

Solution: Perform Transformations at the Source

let
    Source = Sql.Database("Server", "Database", [Query="SELECT *, UPPER(Name) AS NewColumn FROM Table WHERE Year >= 2020"])
in
    Source

Pushing transformations to SQL ensures efficient execution.

2. Loading Unnecessary Columns and Rows Increasing Memory Usage

Retrieving all data instead of only necessary fields increases query execution time and memory consumption.

Problematic Scenario

let
    Source = Sql.Database("Server", "Database"),
    SelectedTable = Source{[Schema="dbo", Item="Sales"]}[Data]
in
    SelectedTable

Loading the entire table retrieves unnecessary data, slowing performance.

Solution: Load Only Required Columns and Rows

let
    Source = Sql.Database("Server", "Database", [Query="SELECT OrderID, Customer, Amount FROM Sales WHERE OrderDate >= '2022-01-01'"])
in
    Source

Filtering data at the source improves query efficiency.

3. Using Inefficient Joins Slowing Down Query Execution

Joining large datasets without proper indexing or pre-filtering can cause excessive processing time.

Problematic Scenario

let
    Sales = Sql.Database("Server", "Database", [Query="SELECT * FROM Sales"]),
    Customers = Sql.Database("Server", "Database", [Query="SELECT * FROM Customers"]),
    Merged = Table.NestedJoin(Sales, "CustomerID", Customers, "CustomerID", "NewTable", JoinKind.Inner)
in
    Merged

Joining full tables without filtering increases processing time.

Solution: Perform Joins at the Data Source

let
    Source = Sql.Database("Server", "Database", [Query="SELECT Sales.OrderID, Sales.Amount, Customers.CustomerName FROM Sales INNER JOIN Customers ON Sales.CustomerID = Customers.CustomerID WHERE Sales.OrderDate >= '2022-01-01'"])
in
    Source

Performing joins in SQL reduces Power Query processing overhead.

4. Excessive Use of Custom Columns Impacting Query Folding

Creating computed columns in Power Query instead of at the data source prevents query folding.

Problematic Scenario

let
    Source = Sql.Database("Server", "Database"),
    AddedColumn = Table.AddColumn(Source, "DiscountedPrice", each [Price] * 0.9)
in
    AddedColumn

Adding calculated fields within Power Query increases processing time.

Solution: Compute Values at the Source

let
    Source = Sql.Database("Server", "Database", [Query="SELECT *, Price * 0.9 AS DiscountedPrice FROM Products"])
in
    Source

Performing calculations in SQL ensures efficient processing.

5. Inefficient Refresh Strategy Leading to Unnecessary Data Reloads

Refreshing all queries every time slows down performance unnecessarily.

Problematic Scenario

let
    FullData = Sql.Database("Server", "Database", [Query="SELECT * FROM Transactions"])
in
    FullData

Retrieving the entire dataset on each refresh increases execution time.

Solution: Use Incremental Refresh for Large Datasets

let
    Source = Sql.Database("Server", "Database", [Query="SELECT * FROM Transactions WHERE TransactionDate >= DATEADD(DAY, -30, GETDATE())"])
in
    Source

Using incremental refresh reduces data reload times.

Best Practices for Optimizing Power Query Performance

1. Ensure Query Folding is Enabled

Push transformations to the source database for efficiency.

Example:

let
    Source = Sql.Database("Server", "Database", [Query="SELECT OrderID, Amount FROM Sales WHERE OrderDate >= '2022-01-01'"])
in
    Source

2. Load Only Necessary Data

Reduce memory usage by selecting only required columns and rows.

Example:

let
    Source = Sql.Database("Server", "Database", [Query="SELECT OrderID, Customer, Amount FROM Sales"])
in
    Source

3. Perform Joins at the Data Source

Prevent large in-memory joins by pre-processing in SQL.

Example:

let
    Source = Sql.Database("Server", "Database", [Query="SELECT Sales.OrderID, Customers.CustomerName FROM Sales INNER JOIN Customers ON Sales.CustomerID = Customers.CustomerID"])
in
    Source

4. Minimize Custom Columns in Power Query

Move calculations to the source system.

Example:

SELECT *, Price * 0.9 AS DiscountedPrice FROM Products

5. Implement Incremental Refresh

Limit data reloads to avoid unnecessary refresh times.

Example:

SELECT * FROM Transactions WHERE TransactionDate >= DATEADD(DAY, -30, GETDATE())

Conclusion

Power Query performance issues often result from inefficient query folding, excessive data loading, improper joins, unnecessary computed columns, and inefficient refresh strategies. By enabling query folding, filtering data at the source, optimizing joins, minimizing in-memory calculations, and implementing incremental refresh, developers can significantly improve Power Query execution speed. Regular monitoring using `Query Diagnostics` and `Performance Analyzer` helps detect and resolve inefficiencies before they impact reporting workflows.