Data Modelling and Design — Lesson 3: Physical Relational Design, Datatypes, Constraints, Indexes, and Scale

Learning Objectives

• Explain the architecture, workflow, or business purpose behind table and column design, datatypes, defaults, and naming standards.
• Apply primary, foreign, unique, check, and not-null constraints and index candidates, access patterns, partition awareness, and denormalization in a controlled scenario.
• Validate outcomes and troubleshoot risks involving storage, volume, history, audit, privacy, and migration implications.

Detailed Topics

• table and column design, datatypes, defaults, and naming standards
• primary, foreign, unique, check, and not-null constraints
• index candidates, access patterns, partition awareness, and denormalization
• storage, volume, history, audit, privacy, and migration implications

Instructor Demonstration

Demonstrate table and column design, datatypes, defaults, and naming standards within a customer-and-order platform redesigned to eliminate ambiguity, duplication, inconsistent keys, and reporting gaps, then capture the exact configuration, command, transaction, report, model, query, code, or process evidence that proves the result for lesson 3.

Hands-On Exercise

Implement a controlled exercise covering primary, foreign, unique, check, and not-null constraints and index candidates, access patterns, partition awareness, and denormalization, then introduce and resolve an exception involving storage, volume, history, audit, privacy, and migration implications while preserving before-and-after evidence.

1. Confirm the environment, identities, inputs, dependencies, risks, and expected outcome.
2. Complete the course-specific configuration, development, analysis, or process workflow.
3. Capture settings, commands, data, outputs, logs, reports, screenshots, calculations, or test results.
4. Compare the observed result with acceptance criteria and document corrective action.

Scenario Analysis

• Identify the technical or business decision that must be made.
• Compare valid approaches and their security, performance, reliability, cost, compliance, and support trade-offs.
• Define rollback, recovery, ownership, communication, and evidence requirements.

Troubleshooting Guidance

Diagnose issues across table and column design, datatypes, defaults, and naming standards, primary, foreign, unique, check, and not-null constraints, index candidates, access patterns, partition awareness, and denormalization, storage, volume, history, audit, privacy, and migration implications without making an unverified production change. Separate symptoms from causes, gather evidence, test the smallest reversible correction, rerun validation, and record preventive action.

Validation Checklist

• The requirement, scope, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by course-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Physical Relational Design, Datatypes, Constraints, Indexes, and Scale in a realistic Data Modelling and Design scenario and validate the result with documented evidence.