Day 19 of 45 · Pro lesson

S3 + Data Lake

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

Teaches: S3 prefixes vs directories, Medallion zones (Raw/Silver/Gold), Hive partitioning, partition pruning, lifecycle tiering, compaction

Build: S3 Medallion Lake engine: raw ingestion → schema validation → Hive partitioned Parquet → curated sales mart.

•Complete 15 drills on Medallion contracts, Hive paths, Athena scan pruning, and lifecycle decay.
•Execute 6 Python modules simulating lake ingestion, partition pruning, and Delete Marker recovery.
•Simulate 5 production failures: small files meltdown, duplicate retries, and unpartitioned scans.
•Defend 10 senior storage interview questions and pass the blank-page challenge.

Partition by query access patterns, keep immutable raw data for replay, and automate lifecycle tiering to Glacier.

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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