◆ Apache Airflow

Clean up and maintain Airflow data

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts
See the prompt↓All Apache Airflow tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskClean up the old task logs and metadata for the 'archive_old_data_dag'. It's taking up too much…+
Clean up the old task logs and metadata for the 'archive_old_data_dag'. It's taking up too much space and slowing down the UI.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore the end of the quarter, review our entire environment and identify all the old,…+
Before the end of the quarter, review our entire environment and identify all the old, completed DAG runs, task logs, and XComs that are no longer needed. I want to implement a retention policy to automatically clean up this data after 90 days to keep our database performant and storage costs down.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentOur database is growing exponentially, and I'm seeing performance degradation in the UI and…+
Our database is growing uncontrollably, and I'm worried about performance.
Our database is growing exponentially, and I'm seeing performance degradation in the UI and longer DAG scheduling times. The finance team is asking about rising cloud costs, and I suspect it's due to unmanaged metadata. I don't know what data is safe to delete without breaking historical lineage or active DAGs. What's the best strategy to identify and safely purge old, unused data from the database without impacting ongoing operations or future audits?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternEvery few months, our database becomes sluggish, and I have to spend days manually identifying…+
My database always bloats over time, impacting performance and cost.
Every few months, our database becomes sluggish, and I have to spend days manually identifying and deleting old logs and metadata. This reactive cleanup is a huge time sink and often impacts our ability to review historical runs. What habit should I change in how I manage our environment to proactively control database growth and maintain optimal performance without constant manual intervention?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Apache Airflow's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.