Why is data validation important during database migration, and what techniques are used?

• A. To speed up migration by skipping data validation.
• B. To verify data types after migration.
• C. To ensure data integrity; use checksums, record counts, and validation runs; DMS replication validation.
• D. To encrypt data in transit during transfer.

Answer: (C)

Data validation is essential during migration because it ensures data integrity and completeness from source to target. When moving data, things like corruption, truncation, or missed records can occur due to transfer glitches, schema conversions, or other issues, and validation gives you confidence that the migrated dataset truly matches the original and that applications will function correctly after switchover. The techniques used to achieve this include checksums, record counts, and validation runs. Checksums compare a hash of data on the source with the hash on the target for corresponding chunks or tables, so matching hashes indicate data content matches while mismatches flag discrepancies. Record counts verify that the number of rows is the same on both sides, helping catch missing or extra rows. Validation runs perform a thorough comparison of data between source and target—either by scanning every row or by validating a representative sample of values—to confirm exact data equality. In AWS DMS, replication validation extends this idea to ongoing replication, automatically verifying that changes delivered to the target stay in sync with the source and providing reports on any differences. Security measures like encryption in transit are important, but they serve a different purpose and do not validate data integrity.