Build Sales Analytics Model – Star Schema, Semantic Model & Power BI
In this Microsoft Fabric tutorial, you’ll learn how to build an end-to-end Sales Analytics model using a star schema, semantic model, and Power BI integration with Direct Lake for blazing-fast performance.
🧠What is a Semantic Model in Microsoft Fabric?
A Semantic Model is a business-friendly layer on top of your raw data that enables easier analysis and reporting.
- Helps users understand and explore the data model intuitively
- Supports business measures using DAX (e.g.,
Total Sales = SUM(FactSales[TotalAmount])) - Defines relationships, KPIs, hierarchies, and security layers
- Optimized for tools like Power BI and Excel
🌟 Star Schema Design for Sales
In this model, we structure the data using a star schema:
- Fact Table:
FactSales - Dimension Tables:
DimCustomer,DimProduct,DimDate,DimRegion
All dimension tables are linked to the central FactSales table to enable fast and meaningful aggregations in reports.
⚡ Direct Lake Mode in Fabric
- Reads directly from Delta tables in OneLake
- Combines import-speed with real-time freshness
- No need for scheduled refreshes
- Fully integrated with Power BI
📊 Sample DAX Measures
Total Sales = SUM(FactSales[TotalAmount])
Total Orders = COUNT(FactSales[OrderID])
Sales Per Customer = DIVIDE([Total Sales], DISTINCTCOUNT(DimCustomer[CustomerID]))
📈 Visualizing in Power BI
- Connect to the semantic model using Direct Lake
- Create visuals: Sales Trend, Sales by Product, Customer Retention
- Apply filters, bookmarks, and slicers for interactivity
- Publish and share via Power BI Service