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Module 14: Data Incidents and Debugging Slides

Slide walkthrough for Module 14 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Debug wrong revenue, stale data, broken...

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. Data Incidents and Debugging - Debug wrong revenue, stale data, broken joins, and schema drift like an engineer.
  2. Learning Objectives - 3 outcomes for this module
  3. Why This Module Matters - Data incidents are production incidents. A wrong dashboard can be as damaging as a down API when leaders use it to make
  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

  • Classify common data incidents
  • Use tests and lineage during debugging
  • Write a useful data incident review

Why This Module Matters

Data incidents are production incidents. A wrong dashboard can be as damaging as a down API when leaders use it to make decisions.

Production Notes

  • Maintain a data incident template: symptom, impact, first bad layer, detection gap, fix, prevention.

Common Mistakes

  • Fixing the dashboard instead of the model
  • Skipping incident review after numbers recover
  • Not notifying metric owners and consumers

Key Takeaways

  • Data debugging needs scope, lineage, and tests
  • Incidents should produce prevention work
  • Wrong data is a reliability problem

Inline Exercises

  1. Debug a Wrong Metric

    Use a fake incident timeline to identify the most likely failing model.

    30-45 minutes - Intermediate

    • Read the symptoms
    • List affected metrics
    • Trace upstream models
    • Pick the first layer where values diverge
    • Write one test that would have caught it

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

Read the full module | Back to course curriculum