The Mental Model
Grain means what one row represents. Most bad metrics come from joining tables with different grains and then aggregating without noticing the duplication.
Before writing any SQL, ask: one row per what? If you cannot answer, you are not ready to aggregate.
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: You join orders to order_items and then sum order amount. Why might revenue become too high?
Reveal the answer
Each order can have many items. The order amount repeats once per item after the join, so summing it counts the same order multiple times.
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?