After performing transformation, do you check whether the destination target has all data present?

Yes, validating data after transformation is a critical step in any ETL pipeline. First, compare row counts between source and target to ensure no rows were lost. Next, use checksums or hash totals on key columns to detect data corruption. Perform sample data checks to verify that transformations applied correctly. Automated data quality tests can flag anomalies such as nulls in mandatory fields or out-of-range values. Finally, maintain audit logs and alerts so that any discrepancies trigger immediate investigation. This systematic validation ensures data integrity and builds trust in the warehouse.