Efficient Data Modeling with the Star Schema in Power BI

Basics of data modeling

• Data modeling in OLTP databases such as relational SQL databases.
• Data modeling in OLAP databases such as Power BI.
• The strengths and weaknesses of the flat table as a data model.

The Star Schema as the Standard for High-Performance Data Models

• Improved evaluation options.  
• Simplifying DAX commands.
• Optimizing memory usage and performance.

Importing Data with Power Query

• Creation of dimension and fact tables.
• Clean and transform data, for example, using the unpivot feature.
• Differentiation between joins in Power Query and relationships in Power Pivot.

Building Data Models Using the Star Schema

• Understand and apply relationships in the Power BI data model (1:1, 1:n, and n:m).
• Active and inactive connections, e.g. for date tables.
• Multi-fact scheme for complex target/actual comparisons.

Best practices for data modeling

• Best practices for maintainable and high-performance data models.
• Identify and avoid common modeling errors.

AI Support with Microsoft Copilot and Anthropic Claude

• Copilot and Claude in Power BI and Excel: Use a prompt to have them generate and explain the Power Query steps for data modeling.
• Copilot and Claude as sparring partners for modeling decisions: building the star schema, naming dimension and fact tables, and documenting the data model.
• Prompting Basics: Distinguishing Between Good and Poor AI Outputs and Critically Evaluating Suggestions.
• Data Privacy and the Limits of AI Use in Data Modeling.

Practical exercises and case studies

• Develop data models using real-world examples.
• Discussion of selected solutions and exchange of experiences.
• Open Q&A session.

Please bring your own laptop to the training . You can find more detailed installation instructions under Methods.