◆ Apache Airflow

Monitor Airflow workflows reliably

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 monitor for the daily ETL job that updates the customer database. If it fails, send an…+
Set up a monitor for the daily ETL job that updates the customer database. If it fails, send an email to the data operations team and me. Check the logs for specific error messages.
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 acceptI need to improve how we monitor the daily ETL jobs. If a job fails, I need to know not just…+
I need to improve how we monitor the daily ETL jobs. If a job fails, I need to know not just that it failed, but also which specific task within the workflow caused it, and ideally, a hint about the data quality issue. Prioritize alerts for critical jobs like the billing data sync.
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 customer data sync job just silently completed, but the downstream reports are showing…+
The customer data sync job just silently completed, but the downstream reports are showing stale data, and I'm getting calls from the marketing team.
The customer data sync job just silently completed, but the downstream reports are showing stale data, and I'm getting calls from the marketing team. I'm afraid we've pushed bad data to production again, but the monitoring says 'success'. I can't tell the marketing director that our monitoring lied. What just happened and what's the best next move to find the real issue?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternOur monitoring often tells us a job succeeded when it actually produced bad data, leading to…+
Our monitoring often tells us a job succeeded when it actually produced bad data, leading to downstream chaos.
Our monitoring often tells us a job succeeded when it actually produced bad data, leading to downstream chaos and frantic debugging. I'm constantly reacting to data quality issues that weren't caught by our alerts. What habit should I change to proactively catch these 'silent failures' before they impact the business?
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.