The Mental Model
Marts are the tables business users and dashboards should trust. Facts store measurable events; dimensions store descriptive context.
If staging is clean ingredients and intermediate is prep work, marts are the served plates. This is the layer people actually consume.
Tiny Example
We will use a small ecommerce dataset throughout the course. Think of these as the only tables in your first warehouse:
| Table | Grain | Example columns |
|---|---|---|
raw_orders | one row per order event | order_id, customer_id, amount, status, created_at |
raw_order_items | one row per item inside an order | order_id, product_id, quantity, item_price |
raw_customers | one row per customer | customer_id, email, country, created_at |
Interactive Check
Question: Should customer country live in fct_orders or dim_customers?
Reveal the answer
Usually dim_customers owns customer country. fct_orders can join to it for analysis, but the descriptive customer attributes belong in the customer dimension.
Inline Practice Lab
This lab is intentionally small. You can solve it by reading the table, writing the SQL/YAML mentally, or pasting the snippet into any SQL scratchpad later.
-- Example starter table
select
order_id,
customer_id,
amount,
status,
created_at
from raw_orders;The goal is not tooling setup. The goal is learning the production habit: state the grain, clean one thing, test one assumption, and explain the downstream impact.
Self-Check Quiz
- What is the grain of the table you are building?
- Which downstream metric or dashboard would be wrong if this model broke?
- What test would catch the most likely beginner mistake here?