Day 21 of 45 · Pro lesson

Redshift

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

Teaches: Redshift MPP architecture, DISTSTYLE (KEY, ALL, EVEN), Sort Keys & Zone Maps, columnar compression, COPY from S3, atomic staging merges

Build: Redshift MPP warehouse engine: parallel S3 COPY → staging deduplication → atomic idempotent merge transaction.

•Complete 15 drills on MPP slices, distribution styles, sort key pruning, and COPY options.
•Execute 6 Python modules simulating Redshift ingestion, distribution skew, and idempotent merges.
•Simulate 5 production failures: single-row INSERT lockups, duplicate PKs on retry, and slice data skew.
•Defend 10 senior data warehouse questions and pass the blank-page challenge.

Redshift doesn't enforce primary key constraints on write. Guarantee idempotency using staging tables and atomic transactions.

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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