How have you used Airflow to orchestrate end‑to‑end data pipelines, for example, extracting data from DMS and loading it into downstream systems?

I designed a DAG that starts with a custom operator to poll AWS DMS for replication logs. The DAG uses a Sensor to wait until a new snapshot is available, then triggers a BashOperator that runs a dbt model to transform the raw data into a staging schema in S3. After the transformation, a PythonOperator reads the S3 files, performs validation, and writes the cleaned data to a Redshift table. Finally, a PostgresOperator triggers downstream analytics jobs. Key decisions included:

1. Modular operators – each step is isolated, making debugging easier.
2. Retries and alerts – sensors and tasks have retry logic and Slack notifications.
3. Dynamic task mapping – for large numbers of tables, I used Airflow 2.3’s task mapping to generate tasks on the fly.
4. XComs for metadata – the DAG passes file paths and row counts between tasks.

This architecture ensures that data moves from DMS to the warehouse reliably, with minimal manual intervention.