The Mental Model
The capstone combines every course idea into one trusted analytics layer. You will design the models, tests, metric specs, semantic objects, and lineage map.
This is not about memorizing commands. It is about showing you can reason from raw records to trusted business answers.
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: What should be the final proof that your analytics layer is trustworthy?
Reveal the answer
You should be able to explain the grain, tests, metric definitions, owners, freshness expectations, and lineage from source to consumer.
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?