Question

A financial services company is migrating a suite of on-premises applications to AWS. One legacy component stores 40 TB of relational data in an IBM Db2 for z/OS database on a mainframe. The company wants to migrate this data into Amazon Aurora PostgreSQL with the least amount of custom coding for both schema conversion and ongoing replication, while keeping the source database available until cutover. Which approach best meets these requirements?

Accepted Answer

Use the AWS Schema Conversion Tool (SCT) to convert the Db2 schema and code objects to PostgreSQL, then use AWS DMS with a full load plus change data capture (CDC) task to migrate and continuously replicate the data until cutover.

Answer

Use AWS DataSync to transfer the Db2 data files directly to Amazon S3, then use AWS Glue crawlers to load the data into Aurora PostgreSQL.

Answer

Export the Db2 tables to CSV, ship them to AWS using a Snowball Edge device, and write custom Lambda functions to transform the schema and import records into Aurora PostgreSQL.

Answer

Use AWS DMS alone with a full load plus CDC task, relying on DMS to automatically convert all stored procedures, triggers, and schema objects during the migration.