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Module 13: Lineage with dbt Artifacts Slides

Slide walkthrough for Module 13 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Trace impact from source columns to...

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. Lineage with dbt Artifacts - Trace impact from source columns to models, metrics, dashboards, and AI answers.
  2. Learning Objectives - 3 outcomes for this module
  3. Why This Module Matters - Lineage is the map of how data flows. It helps you debug wrong numbers, assess change impact, and explain how a metric w
  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

  • Explain table, column, metric, and operational lineage
  • Know what dbt manifest, run_results, and catalog artifacts contain
  • Use lineage to reason about blast radius

Why This Module Matters

Lineage is the map of how data flows. It helps you debug wrong numbers, assess change impact, and explain how a metric was produced.

Production Notes

  • Use lineage during code review. Ask "what downstream object changes if this column changes meaning?" before merge.

Common Mistakes

  • Treating lineage as a pretty graph only
  • Ignoring dashboards and metrics as lineage endpoints
  • Not capturing run status and freshness alongside structural lineage

Key Takeaways

  • Lineage makes data changes safer
  • dbt artifacts already contain useful dependency metadata
  • Column and metric lineage are more useful than table lineage alone

Inline Exercises

  1. Trace the Blast Radius

    Follow one changed source column through models, metrics, and consumers.

    30-45 minutes - Intermediate

    • Start at raw_orders.amount
    • Map it to stg_orders.order_amount
    • Map it to fct_orders.gross_revenue
    • Map it to net_revenue
    • List impacted dashboards and owners

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

Read the full module | Back to course curriculum