Module 6: Marts: Facts and Dimensions Slides
Slide walkthrough for Module 6 of Production Analytics Engineering with dbt: Metrics, Semantic Layers & Lineage: Create the business-facing layer: facts,...
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
- Marts: Facts and Dimensions - Create the business-facing layer: facts, dimensions, and star schemas.
- Learning Objectives - 3 outcomes for this module
- Why This Module Matters - Marts are the tables business users and dashboards should trust. Facts store measurable events; dimensions store descrip
- 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
- Design simple fact and dimension tables
- Understand star schema basics
- Choose the right mart grain for reporting
Why This Module Matters
Marts are the tables business users and dashboards should trust. Facts store measurable events; dimensions store descriptive context.
Production Notes
- Name marts based on business concepts, not source systems. Business users do not care which app emitted the raw table.
Common Mistakes
- Creating one giant flat table for every question
- Putting measures in dimensions
- Ignoring slowly changing attributes
Key Takeaways
- Marts should be easy and safe for downstream consumers
- Facts and dimensions make reporting grain explicit
- Star schemas remain useful even in modern warehouses
Inline Exercises
-
Design an Ecommerce Mart
Create a simple star schema with fct_orders, dim_customers, and dim_products.
30-45 minutes - Beginner to Intermediate
- List each table grain
- Choose primary keys
- Choose foreign keys
- Choose three measures on fct_orders
- Choose five descriptive columns for dimensions
Inline lab: complete the exercise directly in the course page.