The AWS Schema Conversion Tool makes heterogeneous database migrations predictable by automatically converting the source database schema and a majority of the database code objects, including views, stored procedures, and functions, to a format compatible with the target database. Any objects that cannot be automatically converted are clearly marked so that they can be manually converted to complete the migration. SCT can also scan your application source code for embedded SQL statements and convert them as part of a database schema conversion project. During this process, SCT performs cloud native code optimization by converting legacy Oracle and SQL Server functions to their equivalent AWS service thus helping you modernize the applications at the same time of database migration. Once schema conversion is complete, SCT can help migrate data from a range of data warehouses to Amazon Redshift using built-in data migration agents.
The AWS Schema Conversion Tool supports the following conversions -
| Source Database | Target Database on Amazon RDS |
|---|---|
| Oracle Database |
Amazon Aurora, MySQL, PostgreSQL, Oracle |
| Oracle Data Warehouse | Amazon Redshift |
| Azure SQL | Amazon Aurora, MySQL, PostgreSQL |
| Microsoft SQL Server | Amazon Aurora, Amazon Redshift, MySQL, PostgreSQL |
| Teradata | Amazon Redshift |
| IBM Netezza | Amazon Redshift |
| Greenplum | Amazon Redshift |
| HPE Vertica | Amazon Redshift |
| MySQL and MariaDB | PostgreSQL |
| PostgreSQL | Amazon Aurora, MySQL |
| Amazon Aurora | PostgreSQL |
| IBM DB2 LUW | Amazon Aurora, MySQL, PostgreSQL |
| Apache Cassandra | Amazon DynamoDB |
| SAP ASE | RDS for MySQL, Aurora MySQL, RDS for PostgreSQL, and Aurora PostgreSQL |
You can download AWS Schema Conversion Tool for your platform of choice from the links below:
In addition to SCT, the Workload Qualification Framework (WQF) helps you assess and plan your database migrations to AWS databases. WQF uses AWS Schema Conversion Tool (AWS SCT) to collect information to model existing Oracle and Microsoft SQL Server database workloads and provides instructions to convert them to an AWS database. It identifies the complexity of the migration by analyzing database schemas and code objects, application code, dependencies, performance characteristics among other inputs. WQF can conduct a fleet-wide analysis of your entire database portfolio and help categorize migrations based on complexity and workload, so you're fully informed about the potential effort to migrate them into AWS.
WQF automatically generates the following reports:
- Workload assessment based on the complexity, size, and technology used
- Recommendations on migration strategies to migrate to Amazon RDS or Amazon Aurora
- Actionable feedback and step-by-step instructions for migrations
- Assessment of the migration effort required based on team size and member roles
