Power BI Performance Optimization: A Practical Guide
Reduce refresh times and improve report responsiveness with proven DAX and data model techniques.
Slow Power BI reports frustrate users and undermine adoption. After optimizing dozens of enterprise deployments, I've learned that most performance issues stem from a few common patterns—and they're all fixable.
Data Model Optimization
1. Minimize Columns
Import only the columns you need. Each column consumes memory and slows down VertiPaq compression. Use Power Query to remove unnecessary columns before loading.
2. Choose the Right Data Types
Use the smallest data type that fits your data. Prefer Integer over Float when possible. Text columns should be kept short—consider using a separate lookup table for long descriptions.
DAX Best Practices
3. Avoid Iterator Functions on Large Tables
SUMX, FILTER, and EARLIER can be expensive. When possible, use aggregations like SUM, CALCULATE with filters, or pre-aggregated tables.
4. Use Variables for Readability and Performance
Variables are evaluated once and reused. They make complex DAX easier to read and can improve performance by avoiding repeated subqueries.
Refresh Optimization
5. Incremental Refresh
For large datasets, implement incremental refresh to load only new or changed data. This dramatically reduces refresh time and storage costs.
Conclusion
Performance optimization is iterative. Start with the data model, then tune DAX, and finally optimize refresh. Use Performance Analyzer to identify bottlenecks before and after changes.
