Were the data sources known? Was the data loaded? Which scheduling orchestration did you use, as mentioned Airflow?

In a typical data pipeline, the first step is to identify and catalog all data sources, whether they are relational databases, APIs, file stores, or streaming services. Once the sources are known, data is extracted using connectors or custom scripts and staged in a landing zone. Airflow is used to orchestrate the entire workflow: DAGs define tasks such as extraction, transformation, and loading; scheduling is handled via cron expressions or event triggers; dependencies are expressed through task dependencies; and retries, alerts, and logging are managed by Airflow’s built‑in mechanisms. For example, a DAG might extract data from a MySQL database, run a Spark job for transformation, and then load the results into a Snowflake warehouse, all scheduled to run nightly. Airflow’s monitoring UI and alerting help ensure that any failures are quickly identified and addressed.