Most people set schedule_interval ='@daily' and move on.
But in production, nothing is that simple.
Data arrives late.
APIs fail.
Files drop into S3 at random.
And your pipeline has to wait, trigger, or backfill — not just run on a timer.
Today, we’re digging into how scheduling really works — and how you should answer when interviewers ask:
“How do you schedule and trigger your Airflow DAGs?”
First: Understand the Two Types of Runs
Scheduled Runs
You tell Airflow to run a DAG every X time:Every hour, day, week, etc.
Use cases: batch ETL, daily reporting, metrics updates
Triggered Runs
Airflow runs the DAG when something happens:
A file lands in S3
An upstream DAG completes
An API returns a signal
Elite Bonus Drop Coming:
This Thursday, I’ll walk through Sensors, ExternalTask dependency patterns, and DAG chaining in a real multi-DAG system.
This is where your interview answers start sounding like a Staff Data Engineer.