In this example, you can update the source SQL schema and then run the AWS SCT tool. You will need to have a workaround for some of the limitations of AWS SCT.įor example, user-defined table types in the source schema are not appropriately recognized when used by procedures and functions in a different schema in the same database unless the Table Types are “fully qualified”. Conversion settings such as case sensitive option for source database object names.Customizing assessment report settings based on how detailed you will need comments.If you do not map, you may end up correcting the target schema script. This is required as data type conversions are not as expected from the AWS SCT tool. Specifying data type mappings (e.g., INT, INTEGER).You will need the below preparation steps for successful conversion Let us start reviewing steps for converting schema and objects for the target Aurora PostgreSQL database. You can use the tool to identify and convert SQL queries in the application code also. The tool will convert schemas and DB objects from source DB to Target DB. This is a powerful tool for homogeneous and heterogeneous database migrations, and it will support migration from MS SQL Server to Aurora PostgreSQL. Now let us do a deep dive on AWS tools for migrating MS SQL Server Databases to Aurora PostgreSQL. In the previous blog,” Motivations and Considerations for migration from MS SQL Server to AWS Aurora PostgreSQL”, we have mentioned that it is critical to have tools for a successful migration.
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