Microsoft Fabric Data Engineering Project: Wide World Importers

An end-to-end sales analytics solution built on Microsoft Fabric with the Wide World Importers (WWI) sample dataset. It ingests data from SQL Server and SharePoint into a Lakehouse, models it as a star schema and delivers a Power BI sales dashboard.

Stack: Microsoft Fabric (Data Pipelines, Dataflows Gen2, Lakehouse) | SQL Server | SharePoint | Power BI | DAX

What this project shows

• Ingesting from two different sources (SQL Server and SharePoint files) into one Fabric workspace
• Organising data in a Lakehouse with Bronze, Silver and Gold layers
• Building a star schema semantic model with fact and dimension tables
• Delivering a business-facing Power BI dashboard on top of the Gold layer

How it works

1. Ingestion

Data Pipelines and Dataflows Gen2 bring raw data from SQL Server and SharePoint CSVs into the Lakehouse.

2. Lakehouse

Ingested data is stored as Lakehouse tables and transformed into fact and dimension tables. Key tables include Fact_Order_Line, wwi_Sales_Orders, Dim_Customer, Dim_Countries, Dim_Cities and Dim_Date. The table list is in lakehouse/WWILakehouse-definition.md.

3. Semantic model

Relationships between the fact and dimension tables form a star schema, with DAX measures for total sales, order count and average tax rate.

4. Power BI dashboard

The report covers sales trend over time, revenue by geography, orders by product size and top customers by revenue. More detail is in the dashboard README.

Repository structure

.
|-- Architecture Diagram WWI Fabric DE.png
|-- Data Ingestion/        pipeline export, source CSVs and pipeline screenshot
|-- lakehouse/             Lakehouse definition and table screenshot
|-- Power BI Dashboard/    .pbix file, dashboard and semantic model screenshots
`-- README.md

Run it yourself

1. Create a Microsoft Fabric workspace and a Lakehouse.
2. Import the pipeline from Data Ingestion/ and point it at your own SQL Server and SharePoint sources.
3. Load the tables into the Lakehouse and build the relationships shown in the semantic model.
4. Open Power BI Dashboard/WWI Sales Power BI Dashboard.pbix in Power BI Desktop and connect it to your Lakehouse.

Author

Hammad Akram, Data Analytics Engineer | Portfolio | LinkedIn