Database Design & Modelling is a structured, practical course covering ERD, Normalisation, Schemas, Relationships. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
DatabasesTool stack
PostgreSQL
Technology marks for Database Design & Modelling, sourced from the CC0-licensed Simple Icons project.
Use ERD appropriately in a realistic, bounded task.
Use Normalisation appropriately in a realistic, bounded task.
Use Schemas appropriately in a realistic, bounded task.
Use Relationships appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
ERD: Data modelling
Apply ERD through entities, relationships, constraints.
Lesson
Key terms
Revision question
Entities with ERD
entities, ERD, databases
In Database Design & Modelling, which evidence best supports a entities result produced with ERD?
Relationships with Normalisation
relationships, Normalisation, databases
In Database Design & Modelling, which evidence best supports a relationships result produced with Normalisation?
Constraints with Schemas
constraints, Schemas, databases
In Database Design & Modelling, which evidence best supports a constraints result produced with Schemas?
Instructional figureNested structure
Entities: from context to evidence
Entities connects business rule and records to a valid data state in Database Design & Modelling.
1Business rule and records
Define the purpose, intended user and ERD constraints.
frames
2Entities
Derive entities from business rules and define stable identifiers.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Entities is credible only when the result can be traced back to its purpose, inputs and constraints. Entities is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
Normalisation: Querying
Apply Normalisation through selection, joins, aggregation.
Lesson
Key terms
Revision question
Selection with Relationships
selection, Relationships, databases
In Database Design & Modelling, which evidence best supports a selection result produced with Relationships?
Joins with ERD
joins, ERD, databases
In Database Design & Modelling, which evidence best supports a joins result produced with ERD?
Aggregation with Normalisation
aggregation, Normalisation, databases
In Database Design & Modelling, which evidence best supports a aggregation result produced with Normalisation?
Instructional figureContinuous cycle
Selection: from context to evidence
Selection connects business rule and records to a valid data state in Database Design & Modelling.
1Business rule and records
Define the purpose, intended user and Relationships constraints.
frames
2Selection
Translate conditions into predicates and test boundaries.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Selection is credible only when the result can be traced back to its purpose, inputs and constraints. Selection is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
Schemas: Reliability
Apply Schemas through transactions, indexes, backups.
Lesson
Key terms
Revision question
Transactions with Schemas
transactions, Schemas, databases
In Database Design & Modelling, which evidence best supports a transactions result produced with Schemas?
Indexes with Relationships
indexes, Relationships, databases
In Database Design & Modelling, which evidence best supports a indexes result produced with Relationships?
Backups with ERD
backups, ERD, databases
In Database Design & Modelling, which evidence best supports a backups result produced with ERD?
Instructional figureProcess flow
Transactions: from context to evidence
Transactions connects business rule and records to a valid data state in Database Design & Modelling.
1Business rule and records
Define the purpose, intended user and Schemas constraints.
frames
2Transactions
Choose boundaries that preserve invariants under failure.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Transactions is credible only when the result can be traced back to its purpose, inputs and constraints. Transactions is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Relationships: Production design
Apply Relationships through security, performance, operations.
Lesson
Key terms
Revision question
Security with Normalisation
security, Normalisation, databases
In Database Design & Modelling, which evidence best supports a security result produced with Normalisation?
Performance with Schemas
performance, Schemas, databases
In Database Design & Modelling, which evidence best supports a performance result produced with Schemas?
Operations with Relationships
operations, Relationships, databases
In Database Design & Modelling, which evidence best supports a operations result produced with Relationships?
Instructional figureContinuous cycle
Security: from context to evidence
Security connects business rule and records to a valid data state in Database Design & Modelling.
1Business rule and records
Define the purpose, intended user and Normalisation constraints.
frames
2Security
Grant roles by task and test prohibited operations.
produces evidence for
3Valid data state
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Security is credible only when the result can be traced back to its purpose, inputs and constraints. Security is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable Database Design & Modelling project using ERD, Normalisation, Schemas.
Expected output: A working Database Design & Modelling artefact plus an evidence-based self-review.
Tools: A suitable ERD environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using ERD.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the ERD scope.
Glossary
ERD
A core concept or tool used in Database Design & Modelling; its exact meaning is established in the relevant lesson.
Normalisation
A core concept or tool used in Database Design & Modelling; its exact meaning is established in the relevant lesson.
Schemas
A core concept or tool used in Database Design & Modelling; its exact meaning is established in the relevant lesson.
Relationships
A core concept or tool used in Database Design & Modelling; its exact meaning is established in the relevant lesson.
References
PostgreSQL Tutorial - PostgreSQL Global Development Group (accessed 2026-08-21)
SQL Language - PostgreSQL Global Development Group (accessed 2026-08-21)
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.