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Applied Curriculum — Chapters 1–8

Chapter 1 — Data Management

Applied objective: Inventory Meridian data assets; classify value and risk; distinguish business/data/technology problems; map a data lifecycle; draft a small Data Management charter/strategy/roadmap; establish the starting baseline.

  • Primary lab mode: Orientation / enterprise diagnosis
  • Produces: spreadsheet inventory, lifecycle map, issue register, baseline roadmap
  • Technology / medium: primarily management + light SQL inspection
  • DMBOK gate: read and blueprint Chapter 1 before final lab writing
  • Carry-forward: reuse baseline artifacts/data later rather than resetting Meridian

Chapter 2 — Data Handling Ethics

Applied objective: Analyze proposed customer profiling, employee analytics, third-party sharing, secondary use, and online-data scenarios. Identify stakeholders, harms, missing context, ethical concerns, and governance responses.

  • Primary lab mode: Decision / ethical dilemma
  • Produces: decision memos, ethical issue log, stakeholder analysis
  • Technology / medium: no forced coding; optional SQL only to inspect what data exists
  • DMBOK gate: read and blueprint Chapter 2 first
  • Boundary: programming is not the point of this chapter

Chapter 3 — Data Governance

Applied objective: Create governance roles, council structure, stewardship assignments, issue escalation, policy/standard examples, and governance measures. Resolve a live definition dispute between Sales and Finance.

  • Primary lab mode: Operating model / decision rights
  • Produces: governance charter, role/RACI map, issue workflow, policy/standard artifacts
  • Technology / medium: Docs/Sheets + database metadata inspection
  • DMBOK gate: read and blueprint Chapter 3 first
  • Cross-chapter potential: deliberately reactivate earlier concepts where useful without creating artificial all-chapter exercises

Chapter 4 — Data Architecture

Applied objective: Map Meridian source systems, stores, and data flows; draw current-state and target-state architecture; trace Customer/Product/Order data; identify duplication, coupling, ownership, and integration risks.

  • Primary lab mode: Architecture / current-to-target
  • Produces: current/target diagrams, source-system matrix, architecture decisions
  • Technology / medium: diagrams.net + DBeaver + SQL metadata
  • DMBOK gate: read and blueprint Chapter 4 first

Chapter 5 — Data Modeling and Design

Applied objective: Move from conceptual to logical to physical model for core commerce entities. Implement tables, relationships, keys, constraints, and naming choices; test what breaks when integrity rules are absent.

  • Primary lab mode: Model → build
  • Produces: ERDs, PostgreSQL DDL, model decision log, integrity tests
  • Technology / medium: SQL + PostgreSQL + DBeaver
  • DMBOK gate: read and blueprint Chapter 5 first
  • Cross-chapter potential: strong interleaving chapter; reactivate selected governance/metadata/quality concepts where genuinely useful

Chapter 6 — Data Storage and Operations

Applied objective: Work with schemas, tables, views, indexes at awareness/practical depth, transactions, backup/restore in a lab-safe way, change validation, environment separation, and operational evidence.

  • Primary lab mode: Operate / recover
  • Produces: operations runbook, validation queries, backup/rebuild evidence
  • Technology / medium: PostgreSQL + DBeaver + PowerShell
  • DMBOK gate: read and blueprint Chapter 6 first
  • Boundary: practical Data Management operations, not production DBA specialization

Chapter 7 — Data Security

Applied objective: Classify columns; create lab users/roles; grant/revoke access; build a CRUD/access matrix; expose only necessary columns via views; test least privilege; record access-control evidence.

  • Primary lab mode: Classify / restrict / test
  • Produces: classification register, access matrix, SQL role scripts, access-test evidence
  • Technology / medium: PostgreSQL roles/views + DBeaver
  • DMBOK gate: read and blueprint Chapter 7 first
  • Boundary: Data Security for Data Management, not a broad cybersecurity program

Chapter 8 — Data Integration and Interoperability

Applied objective: Load CRM CSV, OMS relational data, and service JSON into staging; standardize formats; transform/join; document source-to-target mapping; build lineage; break a source format and repair the pipeline.

  • Primary lab mode: Ingest / transform / trace
  • Produces: staging tables, SQL/Python transforms, mapping spec, lineage evidence
  • Technology / medium: SQL + Python/pandas + PostgreSQL
  • DMBOK gate: read and blueprint Chapter 8 first
  • Cross-chapter potential: strong interleaving chapter
  • Boundary: enough integration work to understand and govern the flow, not a production Data Engineering track

Shared carry-forward rule

All eight chapters should reuse earlier Meridian artifacts/data when relevant. The company accumulates decisions, defects, schemas, evidence, and governance history over time.