Day 20 of 45 · Pro lesson

Athena + Glue

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

Teaches: S3 + Glue + Athena flow, Glue Data Catalog, external tables & SerDes, Athena scan pricing ($5/TB), partition pruning, schema evolution

Build: Serverless query engine: Glue catalog discovery → partition pruning benchmarks → pre-warehouse data quality validation.

•Complete 15 drills on Glue catalog schemas, Hive SerDes, scan pricing formulas, and schema drift.
•Execute 6 Python modules simulating Athena query orchestration and schema evolution.
•Simulate 5 production failures: runaway $18k scan invoice, silent type drift, and full scan timeouts.
•Defend 10 senior serverless query questions and pass the blank-page challenge.

Athena is serverless compute charging by data scanned. Pair Hive-partitioned Parquet with column pruning to cut 99% of costs.

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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