The following strategies are effective in optimizing the Premium capacity usage on Power BI for larger models. They include memory, dataset partitioning, and query optimization:
1. Memory Usage and Storage Optimization
Removes unnecessary columns, uses integer encoding instead of text, and aggregates wherever possible to reduce dataset size.
Hybrid Tables (DirectQuery + Import mode) load frequently accessed data into memory while keeping historical data in DirectQuery.
Enable Large Dataset Storage Format in Power BI Service for better memory management.
2. Dataset Partitioning and Incremental Refresh
Partition large tables for refresh performance improvement and reduced memory consumption.
Configure Incremental Refresh to load only new or changed data and thus save on refresh time and memory usage.
Save the partitions into Azure Data Lake Gen2 for optimal performance on large datasets.
3. Optimize Dax Queries and Model Relationships
Use optimized DAX measures (e.g., SUMX may be costly, but when possible, use SUM).
Do not create circular dependencies and inactive relationships that slow down calculations.
Use Composite Models to retain DirectQuery large fact tables while smaller dimensions are imported to optimize performance balance.
