Day 25 of 45 · Pro lesson

Spark Transformations

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

Teaches: partition boundaries, narrow vs wide transformations, shuffle write & fetch, repartition vs coalesce, coalesce(1) single-core hazard, partition sizing

Build: Multi-stage pipeline: narrow filtering → wide shuffle aggregation → hash repartitioning by customer key → coalesce(2) lake write.

•Complete 15 drills on narrow vs wide operations, physical execution plans, and shuffle costs.
•Run 6 Python modules simulating repartition vs coalesce mechanics and key co-location.
•Simulate 5 production outages: coalesce(1) bottleneck, 200 tiny files, and Cartesian join disk thrashing.
•Defend 10 senior Spark transformation scenarios and pass the blank-page exam.

Never use coalesce(1) before writing large datasets: it collapses upstream execution to a single core and crashes workers.

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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