In this course, you'll learn how to convert raw, often non-optimized data into a well-designed star model with facts and dimension tables. You'll see how to use Power Query, Power BI, and Microsoft Fabric (Warehouse and Lakehouse) to set up a robust semantic layer that makes reporting easier, faster, and more reliable. The emphasis is on practical modeling: from source data to a scalable star model that can be used directly for reporting, data science and self-service BI.

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global program

Part 1:

Introduction to dimensional modelling.

Facts, dimensions, and the importance of the star model for Power BI and Fabric.

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Part 2

From source data to star model.

Analysis of source structures, pitfalls in relational and operational models

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Part 3

Transform with Power Query.

Step by step, convert raw data into fact and dimension tables.

Part 4

Star models in Power BI and Fabric.

Semantic model in Power BI versus modeling in Fabric Warehouse and Lakehouse.

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Part 5

Quality, performance and DAX.

Impact of a good star model on DAX complexity, performance and maintenance

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Part 6

Gold datasets and best practices.

Designing reusable datasets for data engineers and data scientists, patterns and Q&A.

Data engineers who want to design star models in Fabric (Warehouse, Lakehouse) or other platforms.
Data scientists who need reliable, well-structured datasets for analyses and models.
BI specialists and Power BI developers who are now working on non-dimensional sources.
Data analysts who manage gold datasets or semantic models.

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No specific prior knowledge required; affinity with data is desirable.

Experience with SQL or building Power BI reports makes it easier to follow the course.