The pattern that matters
Power BI's market share sits at 28%, per 2024 Gartner data. What separates fast implementations from slow ones? Data modeling decisions made early - specifically, whether you build around star or snowflake schemas.
Star schema puts quantitative measures in a central fact table, surrounded by dimension tables holding descriptive attributes (time, product, customer). Think sales transactions at the center, with spokes connecting to date, product catalog, and customer details. Snowflake schema normalizes those dimensions further - product dimension splits into product, category, and subcategory tables.
Microsoft's internal benchmarks show star schema delivers 5-10x query performance improvements over normalized models. The reason: fewer joins. Power BI's engine handles one-to-many relationships from dimensions to facts efficiently. Add extra normalization layers and you're forcing the engine to work harder for the same result.
When the rules change
Star schema is the default recommendation, but context matters. Single "big table" approaches work for quick prototypes or one-off analyses. Snowflake makes sense when you need complex hierarchies - say, product classifications that change frequently across multiple levels.
The challenge: over-normalization creates query complexity that cancels performance gains from reduced storage. You trade disk space for speed, and in enterprise BI, speed usually wins.
Recent tutorials from Pragmatic Works (October 2024) and Chandoo reinforce star schema as the baseline approach. Power BI auto-detects relationships, but manual setup via drag-and-drop or DAX ensures the model scales as datasets grow.
What this means in practice
For teams building new semantic models: start with star schema unless you have specific reasons not to. For existing implementations running slow: check if over-normalization is the culprit. The pattern is proven, the tooling supports it, and the performance difference is measurable.
Notably absent: major vendor announcements on schema tooling in the past seven days. The fundamentals haven't changed, which is useful information in itself. Sometimes the boring answer is the right one.