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Joined April 2024
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CI/CD for Airflow Pipelines
Your Airflow DAG is code. So treat it like code.
📌Instead of changing production DAGs manually, you can have a workflow like:
Code
↓
Git
↓
Pull Request
↓
Tests
↓
Review
↓
Merge
↓
Deploy
“I can maintain Airflow pipelines in a real engineering environment.”
CI/CD is part of the second skill.
📌If another engineer had to maintain your DAG tomorrow, would they know how it's tested and deployed?
Data Engineering Community (DEC) retweeted
This sounds like a simple question. But there is a trap.
The interviewer already knows you know what these two are. That is not what they are testing.
They want to know if you understand the architecture behind each one.
Here🧵
◉ Concurrency needs to be managed
📌Why it matters for data engineers
A schedule tells your pipeline when to start. It doesn't automatically tell you whether the data is ready or whether another run is already in progress.
📌Take one of your pipelines and ask:
Retries Are Not a Magic Fix
Your pipeline fails.
Airflow says:
>>> Retrying...
Great. But should every failure be retried? Consider an API timeout. That's potentially a temporary problem, so retrying could make sense. Now consider invalid SQL.
◉ Tasks should ideally be safe to rerun
◉ Idempotency is an important part of reliable pipelines
📌Why it matters for data engineers
A reliable pipeline isn't one that simply retries everything. It's one that fails safely and recovers predictably.