Day 30 of 45 · Pro lesson

Production Airflow & Reliability

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

Teaches: Logical date vs Execution date vs Data interval, catchup=False vs catchup=True, CLI backfills & idempotent rerun safety, Sensor poke vs reschedule mode, Worker slot starvation prevention, Retries with exponential backoff & jitter, Avoiding top-level Variable.get() / Connection.get() DB blasts

Build: Production Airflow simulator: logical date intervals, sensor slot starvation remediation, exponential retry jitter, and execution_timeout circuit breakers.

•Complete 15 drills on logical date calculations, sensor modes, backoff jitter, and metadata DB protection.
•Run 6 Python modules simulating worker slot contention, backfill reconciliation, and variable caching.
•Simulate 5 production failures: Celery worker starvation, duplicate date execution, thundering herd retries, and scheduler DB connection pool collapse.
•Defend 10 senior production Airflow questions and pass the blank-page challenge.

Never use sensor poke mode for long waits (>5 min): it locks worker slots and starves all other pipelines. Always use mode='reschedule'.

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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