For heterogenous migrations, the source and target database engines are different, such as in the case of Oracle to Aurora, Oracle to PostgreSQL, and Microsoft SQL Server to MySQL migrations. Heterogeneous migrations are a two-step process. As the schema structure, data types, and database code of source and target databases can be quite different, the first step is to convert the source schema and code to match that of the target database. The second step is to migrate data from the source database to the target database using AWS DMS. You can choose between AWS DMS replication instances or AWS DMS Serverless, which automates the time-consuming tasks of provisioning, monitoring, and scaling migration resources. All required data types will be automatically converted during the migration.
For schema conversion, AWS DMS offers two schema conversion solutions that can save weeks to months of effort. You can choose to either sign in to the AWS DMS console to initiate the AWS DMS Schema Conversion (AWS DMS SC) workflow for a fully managed experience or download the AWS Schema Conversion Tool (AWS SCT) software to perform a similar assessment and conversion on your local system.
Both options will automatically assess and convert the source database schema and most of the database code objects, including views, stored procedures, and functions, to a format compatible with the target database. In a few steps, you can generate an assessment report that shows the schema conversion complexity. This report provides prescriptive guidance on how to resolve any incompatibilities between the source and target database engines. Any objects that cannot be automatically converted are clearly marked as action items with prescriptive instructions on how to convert so that they can be manually converted to complete the migration. Once schema conversion is complete, AWS DMS can migrate data from source to target.
AWS DMS SC uses generative AI in combination with a traditional rule-based approach to further reduce the number of database objects that require manual conversion. Using generative AI recommendations, you can simplify and accelerate your database migration projects, particularly when converting complex code objects such as stored procedures, functions, or triggers. AWS DMS Schema Conversion with generative AI accelerates migration by providing reviewable code recommendations, reducing time and effort for complex conversions and enabling faster, more reliable database migrations. The feature is available for schema conversions from commercial engines, such as Microsoft SQL Server, to Amazon Aurora PostgreSQL-Compatible Edition and Amazon Relational Database Service (Amazon RDS) for PostgreSQL. You can learn more about AWS DMS SC in our documentation and getting started guide. Check out the documentation on AWS DMS SC supported database conversions and AWS SCT conversions.
For converting embedded SQL statements in your application, Amazon Q Developer can scan your Java application source code and convert the code from Oracle to Amazon Aurora PostgreSQL or Amazon RDS for PostgreSQL.