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 Connecting to IBM DB2 for z/OS. |
|
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 IBM Db2 LUW databases. |
| Microsoft Azure SQL Database |
Aurora MySQL, Aurora PostgreSQL, MySQL, PostgreSQL For more information, see Connecting to Azure SQL. |
|
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 SQL Server databases. |
|
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 Oracle databases. |
|
PostgreSQL (version 9.1 and higher) |
Aurora MySQL, Aurora PostgreSQL, MySQL, PostgreSQL For more information, see PostgreSQL databases. |
| 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 SAP databases. |
AWS SCT supports the following data warehouse conversions.
| Source data warehouse | Target data warehouse |
|---|---|
|
Amazon Redshift |
Amazon Redshift For more information, see Amazon Redshift. |
|
Azure Synapse Analytics |
Amazon Redshift For more information, see Azure Synapse Analytics as a source. |
|
BigQuery |
Amazon Redshift For more information, see BigQuery as a source. |
|
Greenplum Database (versions 4.3 and 6.21) |
Amazon Redshift For more information, see Greenplum databases. |
|
Microsoft SQL Server (version 2008 and higher) |
Amazon Redshift For more information, see SQL Server Data Warehouses. |
|
Netezza (version 7.0.3 and higher) |
Amazon Redshift For more information, see Netezza databases. |
|
Oracle (version 10.1 and higher) |
Amazon Redshift For more information, see Oracle data warehouse. |
|
Snowflake (version 3) |
Amazon Redshift For more information, see Snowflake. |
|
Teradata (version 13 and higher) |
Amazon Redshift For more information, see Teradata databases. |
|
Vertica (version 7.2.2 and higher) |
Amazon Redshift For more information, see Vertica databases. |
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 Connecting to Apache Cassandra. |
AWS SCT supports conversions of the following extract, transform, and load (ETL) processes. For more information, see Converting Data Using ETL.
| 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 and Configuring the AWS Schema Conversion Tool.
For an introduction to the AWS SCT user interface, see Navigating the user interface of the AWS SCT.
For information on the conversion process, see Converting database schemas in AWS Schema Conversion Tool.
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 on-premises data warehouse to Amazon Redshift with AWS Schema Conversion Tool.
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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 Integrating AWS Database Migration Service with AWS Schema Conversion Tool.
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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 extension packs with AWS Schema Conversion Tool.
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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 Converting data from Amazon Redshift using AWS Schema Conversion Tool.
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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.
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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.
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You can use AWS SCT to migrate extraction, transformation, and load (ETL) processes. For more information, see Converting Data Using 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
-
Start the AWS Schema Conversion Tool.
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Open the Help menu and then choose Leave Feedback. The Leave Feedback dialog box appears.
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For Area, choose Information, Bug report, or Feature request.
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For Source database, choose your source database. Choose Any if your feedback is not specific to a particular database.
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For Target database, choose your target database. Choose Any if your feedback is not specific to a particular database.
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For Title, type a title for your feedback.
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For Message, type your feedback.
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Choose Send to submit your feedback.