Scheduling vs Triggering

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?”

1. 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

2. 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.

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