Data Modelling and Design — Lesson 4: Model Validation, Governance, Lineage, Versioning, and Handoff

Learning Objectives

• Explain the architecture, workflow, or business purpose behind CRUD and query-path validation against scenarios.
• Apply model reviews, quality checks, issue resolution, and sign-off and source-to-target lineage, metadata, ownership, and change impact in a controlled scenario.
• Validate outcomes and troubleshoot risks involving version control, DDL handoff, data dictionary, and implementation checklist.

Detailed Topics

• CRUD and query-path validation against scenarios
• model reviews, quality checks, issue resolution, and sign-off
• source-to-target lineage, metadata, ownership, and change impact
• version control, DDL handoff, data dictionary, and implementation checklist

Instructor Demonstration

Demonstrate CRUD and query-path validation against scenarios 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 4.

Hands-On Exercise

Implement a controlled exercise covering model reviews, quality checks, issue resolution, and sign-off and source-to-target lineage, metadata, ownership, and change impact, then introduce and resolve an exception involving version control, DDL handoff, data dictionary, and implementation checklist 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 CRUD and query-path validation against scenarios, model reviews, quality checks, issue resolution, and sign-off, source-to-target lineage, metadata, ownership, and change impact, version control, DDL handoff, data dictionary, and implementation checklist 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 Model Validation, Governance, Lineage, Versioning, and Handoff in a realistic Data Modelling and Design scenario and validate the result with documented evidence.