Retail Data Warehouse Design: Star Schema Patterns
Retail data warehouse design: star schema patterns for sales, inventory, customers, and supplier facts.
A clean data warehouse is the foundation of every retail analytics program. Star schemas remain the right pattern for most retail use cases — they are simple to query, well understood by BI tools, and scale well.
Core fact tables
Sales (transaction line items), Inventory (snapshot at intervals), Receipts (purchase order receipts), Returns (return line items), Traffic (store traffic at intervals).
Core dimensions
Date, Store, SKU/Product, Customer, Supplier, Employee, Channel. Each modeled as a slowly changing dimension (Type 2 where history matters).
Design patterns
Conformed dimensions across all fact tables. Date dimension is non-negotiable — build once, reuse everywhere. Use surrogate keys, not natural keys, for join performance.
Common pitfalls
Overly flat tables (the "one big table" anti-pattern), bi-directional relationships, and dimensions that should be facts (or vice versa).
Frequently Asked Questions
Should we use a data lake instead?+
For raw, unstructured data yes — but the BI layer still typically uses a star schema, often on top of the lake.
What tools support star schemas?+
Power BI, Tableau, Looker, MicroStrategy — all are designed around star schemas.
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