Data Modeling Best Practices for Analytics
Star schemas, dimensional modeling, and design patterns that scale for enterprise analytics.
A well-designed data model is the foundation of performant analytics. Star schemas and dimensional modeling have stood the test of time—here's how to apply them effectively in modern data platforms.
Why Dimensional Modeling Still Matters
Despite the rise of lakehouses and flexible schemas, dimensional models remain ideal for analytics. They're optimized for queries, intuitive for business users, and work seamlessly with tools like Power BI and Tableau.
Star Schema Essentials
Fact Tables
Fact tables store measurable events (transactions, orders, clicks). Keep them narrow: foreign keys to dimensions plus numeric measures. Avoid descriptive columns—those belong in dimensions.
Dimension Tables
Dimensions describe the "who, what, when, where" of your facts. Use surrogate keys for joins. Include slowly changing dimension (SCD) logic when attributes change over time.
Design Patterns
Conformed Dimensions
Use the same dimension definition across multiple fact tables. Date, Customer, and Product dimensions should be shared—not duplicated—for consistent reporting.
Role-Playing Dimensions
When one dimension relates to a fact multiple ways (e.g., Order Date vs Ship Date), create separate views or aliases rather than duplicating the table.
Conclusion
Good data modeling is about clarity and reuse. Start with star schemas for core analytics; add complexity only when needed. Your future self—and your report users—will thank you.
