As a data engineer, how would you retrieve complete data?

To retrieve complete data reliably, I would design a robust ETL pipeline that incorporates data discovery, validation, and incremental loading. First, I would catalog the source schema and data types using a metadata store or data catalog. Next, I would partition the source data (by date, shard key, or hash) to enable parallel extraction and reduce lock contention. For the extraction step, I would use a tool like Apache NiFi or Airflow to orchestrate the job, ensuring that each partition is processed independently. I would implement idempotent writes to the target by using upsert logic or a staging table that merges on a unique key. Validation checks (row counts, checksum, sample record comparison) would run after each load to confirm completeness. Finally, I would schedule incremental loads based on change data capture (CDC) or timestamp columns, so only new or updated rows are processed, keeping the pipeline efficient while guaranteeing that the target dataset is always complete.