◆ Apache Airflow

Automate maintenance for Airflow

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

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskSet up a daily job to clean out logs older than 30 days from the data pipeline's metadata…+
Set up a daily job to clean out logs older than 30 days from the data pipeline's metadata database, starting Monday morning at 2 AM, to keep performance stable.
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 we launch the new customer analytics pipeline, create a maintenance schedule that…+
Before we launch the new customer analytics pipeline, create a maintenance schedule that proactively checks for DAG run failures, alerts the team if an issue is found, and retries the last successful state, to prevent data staleness and ensure reports are always fresh.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe database is approaching its storage limit again, and I'm getting pressure from leadership…+
The database is filling up, and I'm getting blamed for slow dashboards.
The database is approaching its storage limit again, and I'm getting pressure from leadership about dashboard load times. I've tried manually clearing old logs, but it's not sustainable. I'm worried about hitting a hard stop and crashing our reporting. What's the fastest way to automate cleanup without disrupting active DAGs?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI spend too much time firefighting data pipeline database performance issues, often after a…+
I'm constantly reacting to database performance issues instead of preventing them.
I spend too much time firefighting data pipeline database performance issues, often after a dashboard owner complains. This reactive approach is burning me out and eroding trust. What habit can I change to proactively manage database health and keep our data pipelines running smoothly without constant 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.