AWS Schema Conversion Tool (SCT)
The AWS Schema Conversion Tool (AWS SCT) is a downloadable, project-based desktop application from Amazon Web Services that converts a source database’s schema and code — tables, views, stored procedures, functions, and even the SQL embedded in application code — into an equivalent form for a different target database engine. The core exam division of labour: AWS SCT converts the schema and code; AWS Database Migration Service (DMS) then moves the data.
Key points
- Schema/code vs. data. AWS SCT handles everything about structure — DDL, stored procedures, functions, views, and application SQL in languages such as C++, C#, and Java. It does not move rows. AWS DMS handles the actual data movement, typically running continuously with change data capture while SCT’s conversion work happens up front (or, for some source/target pairs, SCT can hand off directly to DMS by generating DMS endpoints and tasks).
- Only needed for heterogeneous migrations. When source and target use the same database engine (a homogeneous migration, e.g. on-premises PostgreSQL → Amazon RDS for PostgreSQL), the schema is already compatible and DMS can migrate the data on its own — no SCT step required. SCT is for heterogeneous migrations, where the source and target engines differ (e.g. Oracle → Amazon Aurora PostgreSQL) and the schema and procedural code must first be translated.
- Two places to do schema conversion today. The original standalone AWS SCT desktop application still exists and supports the broadest range of source/target engine combinations. AWS has since added DMS Schema Conversion, a newer browser-based experience built directly into the DMS console (no separate install) that covers the most common source/target pairs. Use DMS Schema Conversion for a common path; fall back to the standalone AWS SCT desktop app for an engine pair it doesn’t yet cover.
- Assessment report. Before converting anything, AWS SCT can generate a database migration assessment report that estimates the overall effort of the migration, summarizing which schema objects will convert automatically and which will need manual conversion — used for planning and effort estimation before committing to a migration.
- Data extraction agents. For data-warehouse migrations into Amazon Redshift (from sources such as Teradata, Oracle, Greenplum, Netezza, Vertica, Azure Synapse, or BigQuery), AWS SCT manages data extraction agents that pull data out of the source warehouse and land it in Amazon S3 or directly move it toward Redshift — a separate mechanism from the OLTP schema-conversion workflow.
- Also converts ETL jobs (e.g. Microsoft SQL Server Integration Services packages to AWS Glue, Teradata BTEQ/FastLoad/FastExport scripts to Redshift RSQL) and can help migrate big data frameworks such as Apache Hive and HDFS toward Amazon EMR and Amazon S3.
- Where SCT can’t automatically convert a database feature, its extension pack installs AWS Lambda functions and Python libraries in the target environment to emulate the source feature’s behavior.
- Converted schema targets include Amazon RDS (MySQL, MariaDB, Oracle, SQL Server, PostgreSQL), Amazon Aurora, and Amazon Redshift; it can also produce schema destined for a self-managed database on Amazon EC2 or stored as data in Amazon S3.