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Module 16: Capstone: Build a Trusted Analytics Layer Slides

Slide walkthrough for Module 16 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Design the full flow from raw ecommerce...

This slide page is the visual review companion for the full course module. Use it to recap the architecture, examples, exercises, production warnings, and takeaways after reading the lesson.

Slide Outline

  1. Capstone: Build a Trusted Analytics Layer - Design the full flow from raw ecommerce tables to governed metrics and lineage.
  2. Learning Objectives - 3 outcomes for this module
  3. Why This Module Matters - The capstone combines every course idea into one trusted analytics layer. You will design the models, tests, metric spec
  4. The Mental Model - Lesson section from the full module
  5. Tiny Example - Lesson section from the full module
  6. Interactive Check - Lesson section from the full module
  7. Inline Practice Lab - Lesson section from the full module
  8. Self-Check Quiz - Lesson section from the full module
  9. Real-World Use Cases - Reliable executive dashboards that do not disagree across teams, AI analytics agents that query governed metrics instead of guessing SQL
  10. Common Mistakes to Avoid - 3 mistakes covered
  11. Production Notes - 1 practical notes
  12. Inline Exercises - 1 inline exercise
  13. Key Takeaways - 3 points to remember

Learning Objectives

  • Design an end-to-end analytics layer
  • Apply dbt, tests, metrics, semantic modeling, and lineage together
  • Produce a portfolio-ready architecture explanation

Why This Module Matters

The capstone combines every course idea into one trusted analytics layer. You will design the models, tests, metric specs, semantic objects, and lineage map.

Production Notes

  • Use the capstone as a reusable interview story: problem, model design, quality gates, metric governance, lineage, and tradeoffs.

Common Mistakes

  • Submitting only SQL without explaining grain or trust
  • Skipping metric ownership
  • Treating lineage as optional decoration

Key Takeaways

  • Trusted analytics requires modeling, quality, semantics, and lineage together
  • A strong fresher portfolio shows reasoning, not just SQL snippets
  • The same governed layer can serve BI, embedded analytics, and AI tools

Inline Exercises

  1. Trusted Analytics Layer Design

    Create a complete design worksheet for ecommerce analytics.

    30-45 minutes - Intermediate

    • Define sources and staging models
    • Design marts with facts and dimensions
    • Add tests and freshness checks
    • Define three governed metrics
    • Draw lineage from raw source to dashboard and AI consumer

    Inline lab: complete the exercise directly in the course page.

Read the full module | Back to course curriculum