Day 31 of 45 · Pro lesson

Airflow + Spark + Cloud Lakehouse

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

Teaches: Separation of concerns: Airflow orchestrates, Spark computes, Why running PySpark code inside Airflow workers is a critical anti-pattern, SparkSubmitOperator vs EMR / Dataproc / Databricks operators, Deferrable operators & asynchronous triggers, S3/GCS staging handoff: Extract → Raw → Spark → Curated → Warehouse COPY/MERGE, Atomic partition replacement & staging cleanup

Build: Cloud lakehouse orchestrator: Airflow async trigger DAG → Dataproc/EMR Spark execution → S3 Parquet validation → atomic warehouse MERGE gate.

•Complete 15 drills on cloud operator patterns, async deferrable triggers, S3 partition handoffs, and warehouse ingestion gates.
•Run 6 Python modules simulating deferrable polling, partition manifest swaps, and staging garbage collection.
•Simulate 5 production failures: Airflow worker OOM from local PySpark, cluster timeout stalls, dirty partition loads, and zombie worker slots.
•Defend 10 senior cloud orchestration interview questions and complete the blank-page exam.

Never execute heavy compute on Airflow worker nodes. Treat Airflow strictly as the air traffic controller dispatching jobs to managed clusters.

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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