Power Query is a powerful tool for transforming and loading data, but poor practices can slow down your queries and impact overall performance. By understanding common bottlenecks and how to avoid them, you can improve the efficiency of your Power BI reports.

In this guide, we’ll explore the top causes of slowdowns in Power Query and provide actionable tips to optimize performance.

1. Avoid Loading Unnecessary Data

One of the most common causes of slow performance is importing too much data.

Best Practices:

✅ Filter out irrelevant rows at the data source level before loading data into Power Query.
✅ Remove unnecessary columns to reduce dataset size.
✅ Use query folding whenever possible to push transformations back to the source.

2. Optimize Data Types

Assigning the correct data types improves query performance and reduces memory usage.

Best Practices:

✅ Use appropriate data types for each column (e.g., integers for numeric values, text for strings).
✅ Avoid using complex data types like “Any” or mixed data types, which slow down processing.
✅ Perform data type conversion as early as possible in your query steps.

3. Minimize Steps in Query Editor

Each step in the Power Query Editor adds overhead to the query.

Best Practices:

✅ Combine multiple transformation steps when possible.
✅ Remove intermediate steps that don’t add value.
✅ Use advanced editor scripts to streamline complex operations.

4. Leverage Buffer Functions

Buffer functions temporarily store data in memory, speeding up repeated operations.

Best Practices:

✅ Use Table.Buffer() to load data into memory and reduce recalculations in later steps.
✅ Apply buffering to large tables that are reused multiple times within the same query.
✅ Be cautious with buffering on extremely large datasets to avoid memory overload.

5. Enable Query Folding

Query folding allows Power Query to delegate processing tasks back to the data source, improving performance.

Best Practices:

✅ Use query folding-friendly transformations, such as filtering, sorting, and aggregations.
✅ Connect to relational databases (like SQL Server) that support query folding.
✅ Avoid steps that break query folding, such as adding custom columns that aren’t supported by the source.

6. Manage Data Refresh Frequency

Frequent refreshes can strain Power BI resources, especially with large datasets.

Best Practices:

✅ Schedule refreshes during off-peak hours to minimize impact on performance.
✅ Use incremental data refresh when dealing with large datasets.
✅ Set refresh intervals based on business needs (e.g., hourly, daily).

Conclusion

Optimizing Power Query performance requires careful attention to data size, query steps, and transformations. By implementing these best practices, you can avoid common bottlenecks and create faster, more efficient Power BI reports.