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BigData / Apache Airflow Interview Questions

What is an Airflow Dataset and how does data-driven scheduling work?

Datasets (introduced in Airflow 2.4) are logical references to data assets identified by a URI. A DAG can produce a Dataset via an outlet, and another DAG can be scheduled to run automatically when that Dataset is updated.

from airflow.datasets import Dataset

my_dataset = Dataset('s3://my-bucket/output/daily.csv')

# Producer DAG
@dag(schedule='@daily', ...)
def producer():
    @task(outlets=[my_dataset])
    def write_data():
        ...  # write to S3

# Consumer DAG — triggered whenever my_dataset is updated
@dag(schedule=[my_dataset], ...)
def consumer():
    ...

This replaces fragile time-based scheduling with event-driven, data-dependency-aware scheduling.

How is a consumer DAG triggered in data-driven scheduling?

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