Build Sales Analytics Model – Star Schema, Semantic Model & Power BI | Microsoft Fabric Tutorial

Build Sales Analytics Model – Star Schema, Semantic Model & Power BI | Microsoft Fabric Tutorial

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

🎬 Watch the Full Tutorial

Blog post written with the help of ChatGPT.