Have you used Airflow? How would you orchestrate a Snowflake ELT pipeline using Airflow?

In Airflow, I would create a DAG that represents the ELT stages: extract, transform, and load. The extract step uses a SnowflakeHook to run a COPY INTO command that pulls data from external stages or other databases into a staging schema. The transform step can be a PythonOperator that runs a Snowflake SQL script via the SnowflakeHook or a SnowflakeOperator that executes a stored procedure performing data cleansing and aggregation. The load step uses a SnowflakeOperator to MERGE the transformed data into the target fact or dimension tables. I would set up connections in Airflow’s UI for Snowflake credentials, use XComs to pass intermediate results if needed, and configure retries, SLA, and alerting. For scheduling, I’d use a daily schedule with a backfill window. Monitoring is handled by Airflow’s UI and logs, while Snowflake’s query history provides performance metrics. This architecture keeps the orchestration logic in Airflow and the heavy lifting in Snowflake, leveraging Snowflake’s compute scalability.