◆ Apache Airflow

Troubleshoot Airflow state mismatches

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 taskThe 'daily_etl_process' DAG shows 'success' but the downstream reporting system is complaining…+
The 'daily_etl_process' DAG shows 'success' but the downstream reporting system is complaining about missing data. Manually mark the 'load_to_warehouse' task as 'failed' so it triggers a retry.
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 I manually mark the 'load_to_warehouse' task as 'failed', can you quickly check the data…+
Before I manually mark the 'load_to_warehouse' task as 'failed', can you quickly check the data integrity in the target warehouse for yesterday's run? I want to confirm the data truly didn't make it, and not just that the reporting system is looking in the wrong place.
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' DAG shows 'success', but our CRM team says their data hasn't updated…+
The 'customer_data_sync' DAG shows 'success', but our CRM team says their data hasn't updated.
The 'customer_data_sync' DAG shows 'success', but our CRM team says their data hasn't updated since yesterday. This is impacting sales outreach. I'm afraid it's a silent failure where the task completed without actually pushing data, or maybe a caching issue on the CRM side. I can't tell if the problem is in my pipeline or their system. What's the fastest way to diagnose where the disconnect is and get the data flowing?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI often face situations where DAGs report success, but downstream systems show missing or stale…+
I often face situations where DAGs report success, but downstream systems show missing or stale data.
I often face situations where DAGs report success, but downstream systems show missing or stale data, leading to urgent investigations. This erodes trust in our data pipelines. It feels like I'm always reacting to external complaints. What habit should I change to proactively detect these state mismatches and data discrepancies before they impact our users?
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.