Day 36 of 45 · Pro lesson

Project #1: Ingestion & Lake Staging

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Teaches: Transactional database CDC vs batch extraction, High-watermark state persistence in DynamoDB/PostgreSQL, Partitioned S3/GCS Bronze raw lake staging, Schema validation & payload checksum hashing, REST API event enrichment ingestion, Idempotent extraction chunking

Build: Platform Stage 1: PostgreSQL & Stripe API batch extractor → checksum validator → partitioned Hive Parquet S3 Bronze lake writer.

•Complete 15 drills on chunked extraction, watermark commits, schema hashing, and S3 staging paths.
•Run 6 Python modules testing extraction resilience, checksum verification, and network failure retries.
•Simulate 5 production failures: database replica timeout, partial chunk crash, schema column drop, and corrupted payload hash.
•Defend 10 senior ingestion architecture questions and pass the blank-page challenge.

Never perform transformations during the raw extraction phase. Write Bronze data in its raw, unmodified form to enable historical replay.

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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