Your organization is planning to migrate several petabytes of unstructured training datasets from local NAS arrays to a public cloud object storage bucket. To ensure the integrity and quality of the dataset before the migration begins, which action is the most critical to perform first?

Data validation and cleaning is the primary prerequisite for any large-scale migration. Moving unstructured or structured data to a new cloud storage environment provides an ideal opportunity to audit the dataset. Migrating corrupted, duplicate, incomplete, or empty files leads to data pollution in the destination, which is harder to diagnose and costlier to clean at scale. Furthermore, storing redundant or corrupted files in cloud object storage incurs unnecessary ongoing storage and API call charges. While the other actions represent essential steps in a migration plan, establishing real-time ingestion monitoring and alerting pipelines for the transit phase is critical during the migration process but does not fix pre-existing data quality issues. Conducting hands-on training sessions for the data engineering team regarding new cloud identity and access management policies is an administrative security requirement but does not directly preserve the structural integrity of the data itself. Deploying automated backup schedules in the target cloud storage protects data after it has arrived, but if the source data is already corrupted, you will simply be backing up corrupted files. Therefore, validating, cleaning, and deduplicating the source datasets to eliminate corrupt records and empty files is the most critical first step.