What is the AWS Schema Conversion Tool?

You can use the AWS Schema Conversion Tool (AWS SCT) to convert your existing database schema from one database engine to another. You can convert relational OLTP schema, or data warehouse schema. Your converted schema is suitable for an Amazon Relational Database Service (Amazon RDS) MySQL, MariaDB, Oracle, SQL Server, PostgreSQL DB, an Amazon Aurora DB cluster, or an Amazon Redshift cluster. The converted schema can also be used with a database on an Amazon EC2 instance or stored as data on an Amazon S3 bucket.

AWS SCT supports several industry standards, including Federal Information Processing Standards (FIPS), for connections to an Amazon S3 bucket or another AWS resource. AWS SCT is also compliant with Federal Risk and Authorization Management Program (FedRAMP). For details about AWS and compliance efforts, see AWS services in scope by compliance program.

AWS SCT supports the following OLTP conversions.

Source database	Target database
IBM Db2 for z/OS (version 12)	Amazon Aurora MySQL-Compatible Edition (Aurora MySQL), Amazon Aurora PostgreSQL-Compatible Edition (Aurora PostgreSQL), MySQL, PostgreSQL For more information, see Using IBM Db2 for z/OS as a source.
IBM Db2 LUW (versions 9.1, 9.5, 9.7, 10.5, 11.1, and 11.5)	Aurora MySQL, Aurora PostgreSQL, MariaDB, MySQL, PostgreSQL For more information, see Using IBM Db2 LUW as a source.
Microsoft Azure SQL Database	Aurora MySQL, Aurora PostgreSQL, MySQL, PostgreSQL For more information, see Using Azure SQL Database as a source.
Microsoft SQL Server (version 2008 R2, 2012, 2014, 2016, 2017, 2019, and 2022)	Aurora MySQL, Aurora PostgreSQL, Babelfish for Aurora PostgreSQL (only for assessment reports), MariaDB, Microsoft SQL Server, MySQL, PostgreSQL For more information, see Using SQL Server as a source.
MySQL (version 5.5 and higher)	Aurora PostgreSQL, MySQL, PostgreSQL For more information, see Using MySQL as a source. You can migrate schema and data from MySQL to an Aurora MySQL DB cluster without using AWS SCT. For more information, see Migrating data to an Amazon Aurora DB cluster.
Oracle (version 10.1 and higher)	Aurora MySQL, Aurora PostgreSQL, MariaDB, MySQL, Oracle, PostgreSQL For more information, see Using Oracle Database as a source.
PostgreSQL (version 9.1 and higher)	Aurora MySQL, Aurora PostgreSQL, MySQL, PostgreSQL For more information, see Using PostgreSQL as a source.
SAP ASE (versions 12.5.4, 15.0.2, 15.5, 15.7, and 16.0)	Aurora MySQL, Aurora PostgreSQL, MariaDB, MySQL, PostgreSQL For more information, see Using SAP ASE (Sybase ASE) as a source.

AWS SCT supports the following data warehouse conversions.

Source data warehouse	Target data warehouse
Amazon Redshift	Amazon Redshift For more information, see Using Amazon Redshift as a source.
Azure Synapse Analytics	Amazon Redshift For more information, see Using Azure Synapse Analytics as a source.
BigQuery	Amazon Redshift For more information, see Using BigQuery as a source.
Greenplum Database (versions 4.3 and 6.21)	Amazon Redshift For more information, see Using Greenplum Database as a source.
Microsoft SQL Server (version 2008 and higher)	Amazon Redshift For more information, see Using SQL Server Data Warehouse as a source.
Netezza (version 7.0.3 and higher)	Amazon Redshift For more information, see Using Netezza as a source.
Oracle (version 10.1 and higher)	Amazon Redshift For more information, see Using Oracle Data Warehouse as a source.
Snowflake (version 3)	Amazon Redshift For more information, see Using Snowflake as a source.
Teradata (version 13 and higher)	Amazon Redshift For more information, see Using Teradata as a source.
Vertica (version 7.2.2 and higher)	Amazon Redshift For more information, see Using Vertica as a source.

AWS SCT supports the following data NoSQL database conversions.

Source database	Target database
Apache Cassandra (versions 2.1.x, 2.2.16, and 3.11.x)	Amazon DynamoDB For more information, see Using Apache Cassandra as a source.

AWS SCT supports conversions of the following extract, transform, and load (ETL) processes. For more information, see Converting ETL processes.

Source	Target
Informatica ETL scripts	Informatica
Microsoft SQL Server Integration Services (SSIS) ETL packages	AWS Glue or AWS Glue Studio
Shell scripts with embedded commands from Teradata Basic Teradata Query (BTEQ)	Amazon Redshift RSQL
Teradata BTEQ ETL scripts	AWS Glue or Amazon Redshift RSQL
Teradata FastExport job scripts	Amazon Redshift RSQL
Teradata FastLoad job scripts	Amazon Redshift RSQL
Teradata MultiLoad job scripts	Amazon Redshift RSQL

AWS SCT supports the following big data framework migrations. For more information, see Migrating big data frameworks.

Source	Target
Apache Hive (version 0.13.0 and higher)	Hive on Amazon EMR
Apache HDFS	Amazon S3 or HDFS on Amazon EMR
Apache Oozie	AWS Step Functions

Schema conversion overview

AWS SCT provides a project-based user interface to automatically convert the database schema of your source database into a format compatible with your target Amazon RDS instance. If schema from your source database can't be converted automatically, AWS SCT provides guidance on how you can create equivalent schema in your target Amazon RDS database.

For information about how to install AWS SCT, see Installing, verifying, and updating AWS SCT.

For an introduction to the AWS SCT user interface, see Using the AWS SCT user interface.

For information on the conversion process, see Converting database schemas using AWS SCT.

In addition to converting your existing database schema from one database engine to another, AWS SCT has some additional features that help you move your data and applications to the AWS Cloud:

• You can use data extraction agents to extract data from your data warehouse to prepare to migrate it to Amazon Redshift. To manage the data extraction agents, you can use AWS SCT. For more information, see Migrating data from an on-premises data warehouse to Amazon Redshift.

• You can use AWS SCT to create AWS DMS endpoints and tasks. You can run and monitor these tasks from AWS SCT. For more information, see Using AWS SCT with AWS DMS.

• In some cases, database features can't be converted to equivalent Amazon RDS or Amazon Redshift features. The AWS SCT extension pack wizard can help you install AWS Lambda functions and Python libraries to emulate the features that can't be converted. For more information, see Using AWS SCT extension packs.

• You can use AWS SCT to optimize your existing Amazon Redshift database. AWS SCT recommends sort keys and distribution keys to optimize your database. For more information, see Optimizing Amazon Redshift by using AWS SCT.

• You can use AWS SCT to copy your existing on-premises database schema to an Amazon RDS DB instance running the same engine. You can use this feature to analyze potential cost savings of moving to the cloud and of changing your license type.

• You can use AWS SCT to convert SQL in your C++, C#, Java, or other application code. You can view, analyze, edit, and save the converted SQL code. For more information, see Converting application SQL using AWS SCT.

• You can use AWS SCT to migrate extraction, transformation, and load (ETL) processes. For more information, see Converting extract, transform, and load (ETL) processes with AWS Schema Conversion Tool.

Providing feedback

You can provide feedback about AWS SCT. You can file a bug report, submit a feature request, or provide general information.

To provide feedback about AWS SCT

1. Start the AWS Schema Conversion Tool.

2. Open the Help menu and then choose Leave Feedback. The Leave Feedback dialog box appears.

3. For Area, choose Information, Bug report, or Feature request.

4. For Source database, choose your source database. Choose Any if your feedback is not specific to a particular database.

5. For Target database, choose your target database. Choose Any if your feedback is not specific to a particular database.

6. For Title, type a title for your feedback.

7. For Message, type your feedback.

8. Choose Send to submit your feedback.