Day 22 of 45 · Pro lesson

AWS Pipeline

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

Teaches: complete AWS platform flow, high-watermark extraction, S3 lakehouse staging, Glue catalog registration, Redshift COPY & merge, reconciliation

Build: End-to-end AWS data platform: PostgreSQL incremental extraction → S3 raw/curated → Athena validation → Redshift merge.

•Complete 15 drills on watermark tracking, S3 layout, external tables, and warehouse reconciliation.
•Execute 6 Python modules simulating the complete AWS pipeline and discrepancy debugger.
•Simulate 5 production failures: premature watermark advance, duplicate retries, and missing dimensions.
•Defend 10 senior cloud pipeline interview questions and pass the blank-page exam.

Decouple Postgres from Redshift using S3. Advance watermarks only after warehouse commits, and use LEFT JOIN fallbacks.

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())

Already on Pro? Go to your account. Need a pass? See pricing.