Power BI: Data Modelling & Performance

You’ll learn best-practice techniques to reduce errors, improve speed, and lay the foundation for powerful reporting as your business grows.

Course Objectives

By the end of this course, you will:

• Understand the key principles of efficient data modelling in Power BI

• Design scalable data models using best-practice techniques

• Optimise relationships, star schemas, and table structures

• Use DAX intentionally for performance (not just results)

• Spot and avoid the common bottlenecks in Power BI reports

• Improve refresh times, load times, and model accuracy

Who Is This Course For?

This course is ideal for:

• Existing Power BI users looking to go deeper into model design

• Data professionals tasked with maintaining or scaling reports

• Anyone experiencing sluggish reports, calculation errors, or maintenance issues

Prerequisites: You should be confident building reports in Power BI and using basic DAX.

Course Outline

Power BI Data Model Foundations

• What makes a good data model?

• Understanding the VertiPaq engine

• Star schema vs snowflake schema

• Fact vs dimension tables

• Avoiding relationships with calculated columns

Data Shaping Best Practices

• M vs DAX: Where to shape your data?

• Combining and structuring source data in Power Query

• Data type alignment and transformation order

• Minimising columns and unnecessary data

Managing Relationships & Tables

• One-to-many and many-to-one relationships

• Creating proper keys and lookup tables

• Role-playing dimensions (e.g. multiple dates)

• Using inactive relationships with USERELATIONSHIP

Optimising DAX for Performance

• Common pitfalls: iterators, filter context, and complexity

• Aggregations and the CALCULATE function

• Pre-calculated columns vs calculated measures

• Performance tips for heavy models

Improving Report Responsiveness

• Using slicers, filters, and interactions smartly

• Limiting visuals per page

• Bookmarking and navigation to avoid slow load

• Managing visuals using visibility and layering

Refresh & Storage Efficiency

• Understanding Import vs DirectQuery vs Dual mode

• Data reduction strategies

• Reducing refresh times and query folding

• Incremental refresh (overview)

Testing & Troubleshooting

• DAX Studio and Performance Analyzer

• How to interpret bottlenecks

• Version control strategies and documentation

• End-user vs dev-user performance considerations

Reflections & Planning

• Recap the key principles of effective data modelling and performance optimisation

• Identify opportunities to apply best practices in shaping data, managing relationships, and writing DAX

• Plan how to avoid common pitfalls that impact efficiency, accuracy, and report responsiveness