How do you configure a full refresh for a data pipeline?

A full refresh means reloading the entire dataset into the target system each time the job runs. To configure it, you first identify the source tables and the target staging or fact tables. In the ETL tool (e.g., Informatica, Talend, or a custom script), set the extraction step to pull all rows from the source. In the transformation step, you typically clear the target table before inserting new data, which can be done with a TRUNCATE or DELETE statement. Then load the extracted rows into the target. Scheduling is usually handled by the orchestrator (e.g., Airflow, Control-M, or the tool’s built‑in scheduler) to run the job at a defined interval, such as nightly. You should also set up logging and error handling so that failures are captured and alerts are sent. Finally, test the job on a small dataset to ensure that the truncate and load steps work correctly and that data integrity is maintained.