The Star Schema Standard
Power BI's optimal architecture centers on star schema: a fact table containing quantitative metrics (sales, inventory levels) connected one-to-many to dimension tables holding descriptive attributes (product names, dates, customer details). This structure minimizes query complexity and exploits Power BI's compression engine.
Fact tables define model granularity through foreign keys and numeric columns for aggregation. They grow over time. Dimension tables remain smaller, using unique keys (often surrogates for handling slowly changing dimensions) and supporting hierarchies for drill-down analysis.
Why Snowflake Schemas Fall Short
Snowflake schemas normalize dimension tables into sub-dimensions: splitting a product dimension into separate category, subcategory, and detail tables. This reduces source redundancy but creates performance problems in Power BI through longer filter propagation chains and additional joins.
Microsoft's guidance is direct: denormalize dimensions into single tables. The semantic model becomes simpler, queries run faster, and users navigate more easily. Storage savings rarely justify the performance hit when models scale to millions of rows.
Snowflake schemas have narrow use cases: heavily normalized source systems or genuine storage constraints. Most enterprise implementations should flatten the structure.
Relationship Design Matters
Single-direction, one-to-many relationships from dimension to fact tables remain the default. Bi-directional and many-to-many relationships increase logic complexity and slow queries, particularly with high-cardinality data.
Role-playing dimensions (multiple date types like order date, ship date) should use separate tables rather than inactive relationship chains. This keeps filter propagation predictable.
The Performance Trade-offs
Poor modeling creates bloated models, slow refresh times, and inaccurate results from inconsistent granularity. Flat tables or excessive snowflaking add redundancy or joins that hurt report interactivity.
Best practices: use Power Query for transformations, implement surrogate keys, hide fact table keys from users, include proper date tables, and partition large tables. Remove unnecessary columns. Limit calculated columns and complex DAX that reference multiple tables.
The pattern holds across enterprise deployments: star schema principles deliver faster, more reliable insights. This isn't academic, it's operational. Models that ignore these rules pay for it in refresh windows and user complaints.