Star Schema Modeling in Practice for Data Analysts

In this course, you will learn how to transform raw, unoptimized source data into a well-designed star schema with fact and dimension tables. You will work with Power Query, Power BI, and Microsoft Fabric (Warehouse and Lakehouse) to build a robust semantic layer that makes reporting simpler, faster, and more reliable. The focus is entirely on practical modeling: from analyzing source structures to designing a scalable star model ready for reporting, analytics, and data science.

What you learn

• The fundamentals of dimensional modeling and the star schema.

• The purpose of fact and dimension tables and when to use them.

• Transforming raw source data into a star model using Power Query.

• Designing star models for Power BI semantic models and Fabric (Warehouse/Lakehouse).

• How a strong star model simplifies DAX, improves performance, and reduces errors.

• Guidelines for building transparent, traceable, and reliable datasets.

After this course you will be able to:

• Independently design a star schema based on source data.

• Model fact and dimension tables for Power BI and Fabric.

• Convert non-dimensional source data into a star schema using Power Query.

• Build Power BI reports and dashboards that are faster, simpler, and less error-prone.

• Create golden datasets in Fabric that are suitable for analytics as well as data science.

• Improve data traceability and reliability through consistent modeling.

For whom

• Data engineers designing star models in Fabric (Warehouse or Lakehouse) or similar platforms.

• Data scientists who need clean, structured datasets for analytics and modeling.

• BI specialists and Power BI developers working with non-dimensional source systems.

• Data analysts managing golden datasets or semantic models.

Prerequisites

• No specific prerequisites required; affinity with data is recommended.

• Experience with SQL or Power BI reporting makes the course easier to follow.

• Basic knowledge of data warehousing or modeling is helpful but not required.

Content (global program)

Part 1

• Introduction to dimensional modeling.

• Facts, dimensions, and why the star schema matters for Power BI and Fabric.

Part 2

• From raw data to star model.

• Evaluating source structures and identifying pitfalls in operational and relational models.

Part 3

• Transforming with Power Query.

• Step-by-step conversion of raw data into fact and dimension tables.

Part 4

• Star modeling in Power BI and Fabric.

• Building the semantic model in Power BI versus modeling in Fabric Warehouse or Lakehouse.

Part 5

• Quality, performance, and DAX.

• Impact of a strong star model on DAX complexity, performance, and maintainability.

Part 6

• Golden datasets and best practices.

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