The Real Performance Gap
Power BI's engine is optimized for star schemas - a central fact table connected directly to denormalized dimension tables. Microsoft recommends this over snowflake schemas (which normalize dimensions further) for a reason: faster queries, simpler relationships, and less ambiguous filtering.
The difference matters at scale. A star schema handles 60,000-participant survey datasets with 70 questions across multiple themes without performance degradation. The same data in a snowflake schema introduces additional table joins that compound query complexity.
What This Means in Practice
Star schema fundamentals:
- One central fact table (sales, transactions, events)
- Denormalized dimensions (Customer, Product, Date) connected directly
- One-to-many relationships, dimensions on the "one" side
- Single-direction filtering from dimensions to facts
The snowflake alternative:
- Normalized dimension tables broken into sub-dimensions (Product → Category → Subcategory)
- Reduced data redundancy
- More storage-efficient in theory
- Slower queries due to additional joins
For hierarchical data like product categories, snowflake schemas offer normalization benefits. But the performance cost is real, especially when DAX calculations traverse multiple relationship layers.
Design Patterns That Work
Enterprise implementations follow these patterns:
- Custom date tables for time intelligence (not auto-generated)
- Multiple fact tables supported, though one is optimal
- Handling different fact table granularities requires conformed dimensions
- Role-playing dimensions (same dimension used multiple times) need careful relationship management
The Storage Trade-Off
Star schemas consume more storage due to denormalization - dimension data repeats across rows. Snowflake schemas reduce this redundancy. The question for your team: does storage cost or query performance matter more?
History suggests most enterprise Power BI deployments choose query speed. The recent tutorial activity (Chandoo's October 2024 guide, Pragmatic Works' full course) reinforces star schema as the recommended approach.
Three Things to Watch
- Complex survey data - forums report scaling challenges for star schemas with highly normalized source data
- Multiple fact tables - design patterns stabilizing around conformed dimensions
- Slowly changing dimensions - implementation approaches vary across Type 1, 2, and 3 patterns
The pattern is clear: start with star schema, normalize only when specific requirements demand it. Your BI team will thank you when they're writing DAX.