Data & AI Training
Dimensional Modelling (Star Schema)
Designing data models optimised for analytics, reporting and business insight.
Overview
Dimensional modelling is the foundation of effective analytics and BI. Without it, reporting becomes slow, inconsistent and difficult for business users to understand.
This session introduces dimensional modelling concepts such as facts, dimensions, grain and star schemas. Participants learn how to design models that are intuitive for users, performant for analytics, and resilient to change over time.
Who it is for
- BI developers and analytics engineers.
- Data analysts building reports and dashboards.
- Data architects designing analytical platforms.
- Product and domain leads responsible for insight delivery.
What you will learn
- The principles of dimensional modelling.
- How to define facts, dimensions and grain correctly.
- Star schemas vs snowflake schemas.
- How dimensional models support self-service analytics.
- Common pitfalls in analytical model design.
- How modelling choices affect performance and usability.
Agenda
- Why analytical models fail.
- Understanding grain and business processes.
- Designing fact tables.
- Designing dimensions and handling change.
- Star vs snowflake trade-offs.
- Practical examples and discussion.
Delivery and format
Delivered as a 1 hour interactive session. Can be extended into a hands-on dimensional modelling workshop.
- Format: Live virtual or on site.
- Group size: Up to 25 participants recommended.
- Optional: Follow-up hands-on dimensional modelling workshop.
Next steps
This course is commonly paired with: DQ2 · Data Quality Assessment & Profiling, DQ5 · Data Quality Monitoring & Reporting and DS4 · Connecting Strategy to Operations