Day 27 of 45 · Pro lesson

PySpark Production Pipeline

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

Teaches: data contracts & early validation, string & enum sanitation, windowed latest-record deduplication, join cardinality diagnostics, pre-write circuit breakers, idempotent writes

Build: End-to-end PySpark pipeline: raw ingestion → contract validation → window deduplication → broadcast join → pre-write assertions.

•Complete 15 drills on schema contracts, window deduplication, join cardinality checks, and idempotency.
•Run 6 Python modules validating pipeline stages, cardinality audits, and pre-write assertions.
•Simulate 5 production outages: revenue explosion from dimension duplicates, partial write appends, and empty outputs.
•Defend 10 senior pipeline engineering scenarios and complete the architecture test.

Verify dimension uniqueness before joining to prevent row explosion, and write with mode('overwrite') to ensure rerun safety.

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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