Day 23 of 45 · Pro lesson

Spark Fundamentals

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

Teaches: horizontal scaling, driver vs executor, Catalyst DAG, transformations vs actions, narrow vs wide stages, fault tolerance lineage

Build: Distributed execution simulator: driver scheduling, narrow transformations, shuffle partitions, and worker failure recovery.

•Complete 15 drills on driver vs executor roles, narrow vs wide transforms, and shuffle boundaries.
•Run 6 Python modules simulating execution stages, partition byte-splits, and lineage recovery.
•Simulate 5 production outages: driver OOM via collect(), 10k small files, and shuffle fetch failures.
•Defend 10 senior Spark architecture questions and pass the blank-page challenge.

Spark coordinates distributed compute. Never run df.collect() on large data, and remember files in S3 are not partitions.

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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