Data & AI Training
Relational Modelling
Designing robust relational data models that support consistency, integrity and change.
Overview
Relational models underpin most operational systems and many analytical platforms. Poor relational design leads to duplication, inconsistency and fragile systems that are hard to change.
This session focuses on practical relational modelling using Entity Relationship Diagrams (ERDs). Participants learn how to identify entities, define attributes, model relationships and apply normalisation in a way that supports real-world systems and data quality requirements.
Who it is for
- Data analysts and engineers designing relational schemas.
- Solution and data architects.
- Developers working with transactional databases.
- Governance and quality practitioners concerned with consistency and integrity.
What you will learn
- How to identify entities and attributes from business processes.
- How to model relationships and cardinality correctly.
- The purpose of primary and foreign keys.
- How normalisation supports data integrity and reuse.
- When and why to denormalise.
- How relational models interact with governance and quality controls.
Agenda
- From business process to entities.
- Attributes, keys and relationships.
- Cardinality and optionality.
- Normalisation: what it solves and what it doesn’t.
- Common modelling anti-patterns.
- Examples of effective relational models.
- Q&A and Discussion.
Delivery and format
Delivered as a 1 hour interactive session. Can be extended into a practical ERD modelling workshop.
- Format: Live virtual or on site.
- Group size: Up to 25 participants recommended.
- Optional: Follow-up hands-on ERD modelling workshop.
Next steps
This course is commonly paired with: DM3 · Dimensional Modelling (Star Schema), DQ2 · Data Quality Assessment & Profiling and DG4 · Metadata Management