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Module 15: CI/CD for Analytics Engineering Slides

Slide walkthrough for Module 15 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Prevent broken models and metric...

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. CI/CD for Analytics Engineering - Prevent broken models and metric changes from reaching production silently.
  2. Learning Objectives - 3 outcomes for this module
  3. Why This Module Matters - CI/CD for analytics engineering applies software delivery discipline to data models: compile, test, document, review, an
  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

  • Understand analytics CI checks
  • Use slim CI thinking for changed models
  • Design review rules for metric and semantic changes

Why This Module Matters

CI/CD for analytics engineering applies software delivery discipline to data models: compile, test, document, review, and deploy with clear gates.

Production Notes

  • A fast CI path increases adoption. If checks take too long, teams route around them.

Common Mistakes

  • Running no tests in pull requests
  • Running the entire warehouse for every change
  • Allowing semantic layer changes without business owner review

Key Takeaways

  • Analytics code needs CI because it affects production decisions
  • Run the smallest safe set of changed and downstream models
  • Metric changes deserve extra review

Inline Exercises

  1. Design a Safe PR Gate

    Choose the checks that should block a risky analytics pull request.

    30-45 minutes - Intermediate

    • List compile checks
    • List model tests
    • List changed model selection
    • Add docs or contract checks
    • Add reviewer rules for metric changes

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

Read the full module | Back to course curriculum