A growing retailer had transactional data in SQL Server but no analytical model. Every Power BI report required complex joins across 20+ tables. A purpose-built star schema data warehouse transformed reporting performance.
The retail database was a normalized OLTP schema optimized for transactions, not analytics. Power BI reports required joining 20+ tables, causing 45-second load times and frequent timeouts. The business team couldn't build their own reports without developer help.
Designed a Kimball-style star schema with a central Sales fact table and conformed dimensions for Date, Product, Store, Customer, and Promotion — optimized for Power BI's in-memory engine.
Created slowly changing dimension (SCD Type 2) logic for Product and Customer dimensions to preserve historical context, enabling accurate period-over-period comparisons in Power BI.
Built SSIS packages to extract from the OLTP system nightly, apply business transformations, and load the star schema — with full error handling, logging, and email alerting on failures.
Applied columnstore indexes to all fact tables, implemented table partitioning by month, and used Copilot for Azure Data to generate optimized T-SQL views for the Power BI semantic layer.
Published a certified Power BI semantic model on top of the warehouse with row-level security by region, enabling business users to build their own reports without IT involvement.
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