◆ Apache Airflow

Set up monitoring and alerting 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
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 taskSet up an alert for our 'customer_data_ingestion' DAG so that if it fails or runs longer than…+
Set up an alert for our 'customer_data_ingestion' DAG so that if it fails or runs longer than 30 minutes, the data engineering team's Slack channel gets notified immediately.
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 finalize the monitoring for the new marketing attribution pipeline, make sure we're…+
Before we finalize the monitoring for the new marketing attribution pipeline, make sure we're not just alerting on failures. Flag any DAGs that consistently run late or have high task retry rates, as those often signal deeper issues that impact downstream reports for the marketing team.
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 'sales_reporting_sync' DAG failed silently for two hours this morning, delaying critical…+
The sales data pipeline failed silently for two hours, and nobody noticed.
The 'sales_reporting_sync' DAG failed silently for two hours this morning, delaying critical dashboards for the sales team, and nobody knew until a user complained. We have basic failure alerts, but this one just hung. What kind of monitoring did I miss that would have caught this, and what's the best immediate action to prevent this specific type of silent failure from happening again before end of day?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternIt feels like our data team is constantly reacting to user complaints about stale or missing…+
We always find out about data issues from users, not our monitoring.
It feels like our data team is constantly reacting to user complaints about stale or missing data, instead of our monitoring catching issues proactively. We're losing credibility with the business. What habit should I change to shift us from reactive firefighting to truly proactive data quality and pipeline health alerting?
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.