The Trade-Off Is Clear
If you're building Power BI models at scale, the star schema vs snowflake debate isn't academic - it's about whether your reports will actually perform when executives need them.
Star schema wins on speed. The architecture is simple: one central fact table (sales, web events, transactions) surrounded by dimension tables (customer, product, date) in single-hop relationships. When a user filters on product category, Power BI makes one join. Real implementations report model compression from 800MB to 200MB - a 75% reduction - once denormalization is applied correctly.
Snowflake schema normalizes those dimensions into hierarchical sub-tables. Product becomes Product → Product Category → Product Subcategory. This reduces data redundancy, which sounds good until you realize Power BI's VertiPaq engine has to traverse multiple joins for every query. The storage savings rarely matter when you're dealing with cloud costs, but the query lag matters immediately.
What Actually Works
Successful implementations follow a pattern: star schema foundation, single-direction relationships (bidirectional creates circular dependency headaches), and proper date dimension tables built with CALENDARAUTO or custom DAX - not text fields masquerading as dates.
For complex scenarios - multiple fact tables at different granularities, role-playing dimensions, slowly changing dimensions - bridge tables and conformed dimensions keep things clean without abandoning the star approach. The key is maintaining that one-hop principle wherever possible.
The Fine Print
Snowflake schema has valid use cases: deeply hierarchical organizational structures, extreme data integrity requirements, or specific regulatory constraints. But most teams choose it because it looks "more normalized" without testing whether that matters for their workload.
Microsoft Fabric communities report ongoing optimization discussions, but the fundamentals haven't changed. Power BI Performance Analyzer will tell you quickly whether your schema choice is costing you seconds per query. Those seconds compound when you're serving hundreds of users.
The pattern is clear: start with star, prove you need snowflake's complexity before accepting its cost.