Which approach will MOST effectively meet these requirements?
Use the AWS Schema Conversion Tool (AWS SCT) to convert source Oracle database schemas to the target Aurora DB cluster. Verify the datatype of the columns.
Use the table metrics of the AWS DMS task created for migrating the data to verify the statistics for the tables being migrated and to verify that the data definition language (DDL) statements are completed.
Enable the AWS Schema Conversion Tool (AWS SCT) premigration validation and review the premigration checklist to make sure there are no issues with the conversion.
Enable AWS DMS data validation on the task so the AWS DMS task compares the source and target records, and reports any mismatches.
Explanations:
The AWS Schema Conversion Tool (AWS SCT) is used to convert database schemas but does not handle data validation or compare the data between source and target databases. It focuses on schema conversion, not data migration validation.
While AWS DMS provides table metrics and progress reporting, these metrics alone do not validate that the data has been accurately migrated or compare source and target records. It helps monitor migration progress but does not directly validate data correctness.
Premigration validation in AWS SCT checks for potential conversion issues related to schema, not the actual data migration or validation of data correctness post-migration. This does not meet the requirement for data accuracy validation.
Enabling AWS DMS data validation allows for direct comparison of the data between the source and target databases, ensuring that the data has been migrated accurately. This solution is designed to minimize downtime and confirm the migration’s integrity before the cutover.
I evaluate that the answer is:
Enable AWS DMS data validation on the task so the AWS DMS task compares the source and target records, and reports any mismatches.