How would you design a robust data ingestion pipeline that recursively grabs files and handles errors gracefully, using tools like DBT, Redshift, and Power BI?

To make a recursive file ingestion robust, you would use a workflow orchestrator like Airflow or Prefect to schedule the ingestion job. The job would scan the source directory, detect new files, and push them to a staging area (e.g., S3). Each file would be processed in a separate task so that a failure in one file does not bring down the entire run. You would enable retries, set a back‑off strategy, and use idempotent writes to Redshift (e.g., staging tables with unique constraints). DBT would then transform the staged data into the warehouse schema. For error handling, you would capture exceptions, send alerts, and maintain a log table that records file status. If the ingestion fails, you can re‑run only the failed files. Additionally, you can use a lightweight monitoring dashboard (e.g., Power BI) to visualize ingestion health. This approach ensures that errors in individual files do not break the whole pipeline and that the system can recover automatically.