Build Production-Ready Airflow DAGs

Python and Bash operators implement tasks; Airflow Variables and connections provide configuration. Airflow DAG Builder guides the creation, optimization, and troubleshooting of Apache Airflow workflows. Its design principles emphasize idempotent tasks, autonomous units that fail fast, historical-data handling, observable execution, and explicit resource limits. The aim is a DAG whose scheduling, dependencies, failure behavior, and data handling are understandable together.

The DAG example establishes ownership, a start date, retry delays, failure email behavior, and execution timeouts. It uses a daily schedule, disables catchup, and limits active runs. TaskFlow examples then connect extraction, transformation, and loading through returned dictionaries and automatic XCom handling. Those examples simulate processing; their record counts and quality scores are sample values rather than measured pipeline results.

Advanced dependency examples include file readiness, a PostgreSQL quality query, and a success email. Custom validation raises an error for insufficient records, while branching selects high-volume or standard processing from the data volume. Monitoring records pipeline identity, execution date, duration, processed records, and quality metrics, with a Slack failure notification triggered when a task fails.

Use this skill for scheduled Python data workflows that need reliable retries, bounded concurrency, historical-data processing, and clear operational signals. It is relevant when troubleshooting dependencies or tuning sensors, task groups, dynamic task generation, and execution limits.

Treat the provided processing functions as patterns to adapt to a real source and destination. The extraction and loading examples explicitly simulate their work, so they do not constitute an implemented connection to a production data system. Resource settings and validation thresholds need to reflect the workflow rather than inherit sample values without review.

Inputs include the pipeline's schedule, task dependencies, date partitions, record characteristics, connection identifiers, configuration values, and notification destinations. Outputs are DAG and task definitions, dependency relationships, validation results, branch selections, and logged metrics. A file sensor can wait for the expected dated input:

file_sensor = FileSensor(
    task_id='wait_for_source_file',
    filepath='/data/input/{{ ds }}/source.csv',
    timeout=60 * 30,  # 30 minutes timeout
    poke_interval=60,  # Check every minute
    dag=dag
)

Configuration examples obtain batch size, source endpoint, and notification emails through Airflow Variables. Connection retrieval uses BaseHook instead of embedding connection details in task logic. Recommended controls include pools, active-task and active-run limits, sensor intervals and timeouts, execution timeouts, and appropriate logging levels. Dependency-on-past behavior should be enabled only when required. DagBag examples check importability, task presence, and the extraction-to-transformation relationship.