linkmerce-airflow-change

To safely update Airflow DAG scheduling and dependencies, you must read the DAG alongside called modules to validate trigger, credential, extract, transform, and load alignment, keeping orchestration logic separate from business code to prevent broken ETL pipelines.

Airflow DAG documentation drifts from actual ETL workflow behavior when doc_md is not updated alongside implementation changes, causing operators to run incorrect workflows; keeping the DAG focused on orchestration and updating doc_md prevents this inconsistency.

The best way to change credential wiring and load strategies in an Airflow DAG is to read the DAG with its called modules together, verify end-to-end alignment of credentials and load paths, and ensure the modifications touch only orchestration responsibilities.

Yes, you can adjust Airflow DAG triggers and schedules for DuckDB load operations independently by keeping the DAG focused purely on orchestration, verifying that trigger and load strategy changes do not alter underlying extract or transform business logic.

You should add or update tests when making schedule adjustments to Airflow DAGs only if the DAG behavior is realistically testable within the repository, ensuring the orchestration changes remain validated without unnecessary test overhead.

When modifying ETL orchestration in Airflow DAG files, the main limitation is keeping changes strictly within airflow/dags orchestration scope; avoid embedding business logic and always validate dependency, trigger, credential, and load alignment against called modules.