Day 14 of 45 · Pro lesson

Data Warehousing: Star Schema, Snowflake, Keys, and Grain

The curriculum for this day stays visible. The lesson, code, and workspace unlock with Pro.

Teaches: OLTP vs OLAP, fact tables, dimension tables, star vs snowflake, surrogate vs natural keys, declaring grain

Build: Kimball Star Schema in SQLite (orders, customers, products, dates) with grain fan-out debugger.

•Complete 15 drills on OLTP vs OLAP profiling, additive measures, grain statements, and surrogate keys.
•Execute Star Schema and Snowflake modules in Pyodide with multi-hop join profiling.
•Simulate 5 failure modes: join fan-out inflation, missing -1 dimension members, and grain pollution.
•Defend 10 senior interview questions and complete the data modeling blank-page exam.

Declare the grain in plain English first: grain dictates primary keys, valid metrics, and prevents join fan-out.

This section walks through the idea with a short example, then the trade-offs you should mention in an interview.

In practice you start from the raw rows, apply the transform step by step, and check the shape of the result before you move on.

A common mistake is to jump straight to the final query without naming the grain or the join keys that keep the result correct.

Once the core path works, you harden it for nulls, duplicates, and late data so the pipeline stays reliable under load.

The Pro write-up covers the full explanation, worked examples, and the code you can run in the studio.

# Locked example
result = transform(frame)
print(result.head())

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