Day 29 of 45 · Pro lesson

Airflow Fundamentals & DAGs

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

Teaches: DAGs as directed acyclic graphs, Scheduler vs Webserver vs Executor vs Worker, Task state lifecycle & cascades, Operators vs Sensors vs TaskFlow API, Bitshift dependency chaining (>>), XCom mechanics & metadata DB hazards

Build: Airflow execution engine: DAG registry, topological sort dependency resolution, state transition simulation, and 48KB XCom overflow protection.

•Complete 15 drills on DAG dependencies, cycle detection, state cascades, and XCom pointers.
•Run 6 Python modules simulating DAG dependency resolution, execution states, and XCom payload boundaries.
•Simulate 5 production failures: scheduler top-level parse death, 500MB XCom DB crash, cycle deadlock, and runaway backfill.
•Defend 10 senior Airflow architecture questions and complete the blank-page challenge.

Never pass dataframes or raw datasets through XCom. Use XCom strictly for lightweight metadata and remote pointers (S3/GCS paths).

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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