The Azure Retail Data Pipeline was built to demonstrate the full path from structured source data to business intelligence. The modeled dataset spans customers, products, orders, stores, suppliers, categories, and promotions, so the work is as much about relationships and orchestration as it is about loading rows.
One flow, clear responsibilities
- ›Source data provides the operational retail entities
- ›Azure Data Factory orchestrates ingestion and movement
- ›Python, pandas, SQLAlchemy, and SQL support transformation and validation
- ›Azure SQL Database holds the relational analytics model
- ›Analytical views create a reusable reporting layer
- ›Power BI and DAX turn the model into KPI and trend reporting
The analytical contract
The views vSalesByMonth, vTopProducts, vCustomerCohorts, and vStorePerformance provide a stable boundary between relational storage and reporting. Power BI can focus on revenue, order volume, average order value, margin, store performance, and customer trends without rebuilding the same SQL logic in every visual.
Why scale matters here
At 300,000 orders, 50,000 customers, and 10,000 products, load order, referential integrity, and reusable aggregation are visible design concerns. The scale is large enough to make pipeline and model decisions meaningful while remaining understandable as a portfolio system.
💡 Keeping KPI logic in SQL views lets Power BI reuse one definition instead of recreating calculations in each visual.