Module 1: What Analytics Engineering Actually Is Slides
Slide walkthrough for Module 1 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Understand the job: turn raw tables into...
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
- What Analytics Engineering Actually Is - Understand the job: turn raw tables into trusted business meaning.
- Learning Objectives - 3 outcomes for this module
- Why This Module Matters - Analytics engineering sits between raw data movement and business decision-making. The work is to make data clean, teste
- The Mental Model - Lesson section from the full module
- Tiny Example - Lesson section from the full module
- Interactive Check - Lesson section from the full module
- Inline Practice Lab - Lesson section from the full module
- Self-Check Quiz - Lesson section from the full module
- Real-World Use Cases - Reliable executive dashboards that do not disagree across teams, AI analytics agents that query governed metrics instead of guessing SQL
- Common Mistakes to Avoid - 3 mistakes covered
- Production Notes - 1 practical notes
- Inline Exercises - 1 inline exercise
- Key Takeaways - 3 points to remember
Learning Objectives
- Explain analytics engineering in beginner-friendly language
- Separate data engineering, analytics engineering, and BI work
- Understand why trust matters more than query cleverness
Why This Module Matters
Analytics engineering sits between raw data movement and business decision-making. The work is to make data clean, tested, documented, reusable, and understandable.
Production Notes
- Define ownership for every model early. Orphaned data models become silent liabilities.
Common Mistakes
- Thinking analytics engineering is only dashboard work
- Skipping documentation because the SQL seems obvious
- Letting every dashboard redefine core metrics
Key Takeaways
- Analytics engineering creates trusted business-ready data
- The core output is not a dashboard; it is reusable meaning
- dbt is one tool in a broader production data workflow
Inline Exercises
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Classify the Analytics Stack
Place raw tables, staging models, marts, metrics, semantic layer, dashboards, and AI tools in the correct order.
30-45 minutes - Beginner
- Read the seven components listed in the lesson
- Draw them as a left-to-right flow
- Mark which components are owned by analytics engineers
- Write one sentence describing why each layer exists
Inline lab: complete the exercise directly in the course page.