AWS Schema Conversion Tool (SCT) is a powerful database migration utility that enables organizations to convert database schemas from one database engine to another, facilitating heterogeneous database migrations to AWS. This tool plays a crucial role in workload migration and modernization strateg…AWS Schema Conversion Tool (SCT) is a powerful database migration utility that enables organizations to convert database schemas from one database engine to another, facilitating heterogeneous database migrations to AWS. This tool plays a crucial role in workload migration and modernization strategies by automating the complex process of schema transformation.SCT supports conversions from various source databases including Oracle, Microsoft SQL Server, MySQL, PostgreSQL, and others to AWS database services such as Amazon Aurora, Amazon RDS, Amazon Redshift, and Amazon DynamoDB. The tool analyzes source database schemas, stored procedures, functions, and other database objects to generate equivalent code for the target platform.Key features include automated assessment reports that identify conversion complexity and highlight areas requiring manual intervention. SCT provides detailed migration assessments showing the percentage of code that can be automatically converted versus portions needing developer attention. This helps architects estimate migration effort and plan resources effectively.For data warehouse migrations, SCT can convert schemas from Teradata, Oracle, Netezza, and Greenplum to Amazon Redshift, enabling organizations to modernize their analytics infrastructure. The tool also supports application code conversion for embedded SQL in languages like C++, Java, and C#.SCT integrates seamlessly with AWS Database Migration Service (DMS) to provide end-to-end migration capabilities. While SCT handles schema and code conversion, DMS manages the actual data transfer with minimal downtime through continuous replication.Best practices include running SCT assessments early in migration planning to understand complexity, using the built-in action items to track manual conversion tasks, and leveraging extension packs that provide additional functionality for complex conversions. Organizations should also test converted schemas thoroughly in development environments before production deployment to ensure functional equivalence and performance optimization.
AWS Schema Conversion Tool (SCT) - Complete Guide
Why AWS Schema Conversion Tool is Important
AWS Schema Conversion Tool (SCT) is a critical component in database migration strategies, particularly for heterogeneous migrations where you need to move from one database engine to another. In enterprise environments, organizations often need to migrate from commercial databases like Oracle or Microsoft SQL Server to open-source alternatives like PostgreSQL or MySQL, or to AWS-native databases like Amazon Aurora. SCT automates the complex process of converting database schemas, stored procedures, and application code, significantly reducing migration time and effort.
What is AWS Schema Conversion Tool?
AWS SCT is a free, downloadable application that automatically converts source database schemas and most custom code to a format compatible with your target database. It supports conversions between various database engines including:
- Oracle to Amazon Aurora, PostgreSQL, MySQL, or Amazon Redshift - Microsoft SQL Server to Amazon Aurora, PostgreSQL, MySQL, or Amazon Redshift - Teradata to Amazon Redshift - Netezza to Amazon Redshift - Greenplum to Amazon Redshift - Vertica to Amazon Redshift - MySQL to PostgreSQL - PostgreSQL to MySQL
SCT also converts data warehouse schemas when migrating to Amazon Redshift and can help with application SQL code embedded in C++, Java, and other programming languages.
How AWS Schema Conversion Tool Works
Step 1: Assessment SCT first connects to your source database and analyzes the schema, stored procedures, functions, triggers, and other database objects. It generates an assessment report that identifies conversion complexity and highlights items requiring manual intervention.
Step 2: Schema Conversion The tool automatically converts compatible schema elements to the target database format. Objects that cannot be automatically converted are flagged with action items, and SCT provides guidance on how to manually complete these conversions.
Step 3: Code Conversion SCT converts stored procedures, functions, triggers, and views. It also includes application code conversion capabilities through extension packs that can convert embedded SQL in application source code.
Step 4: Migration Report A detailed migration assessment report is generated showing the percentage of code that can be automatically converted versus what requires manual effort. This helps in project planning and resource allocation.
Step 5: Apply to Target Once conversions are complete and reviewed, SCT can apply the converted schema to the target database. The tool works alongside AWS Database Migration Service (DMS) for actual data migration.
Key Features
- Extension Packs: Additional components that emulate source database functions not natively available in the target database - Data Extraction Agents: For large-scale data warehouse migrations, SCT uses agents to extract data and migrate it to Amazon Redshift - Virtual Partitioning: Helps manage large table migrations by creating virtual partitions - Optimization Recommendations: Provides suggestions for optimizing target database performance
Exam Tips: Answering Questions on AWS Schema Conversion Tool (SCT)
Tip 1: Understand When to Use SCT vs DMS Remember that SCT is for schema and code conversion in heterogeneous migrations, while DMS handles actual data migration. When questions mention converting stored procedures or schema objects between different database engines, SCT is the answer.
Tip 2: Know Homogeneous vs Heterogeneous Migrations SCT is primarily needed for heterogeneous migrations (different database engines). For homogeneous migrations (same engine), native database tools or DMS alone may suffice. Exam questions often test this distinction.
Tip 3: Remember SCT Assessment Reports Questions about planning migrations or estimating effort often point to SCT assessment reports. These reports quantify automatic versus manual conversion requirements.
Tip 4: Data Warehouse Migration Scenarios When you see scenarios involving Teradata, Netezza, Greenplum, or Vertica migrating to Amazon Redshift, SCT with data extraction agents is typically the correct approach.
Tip 5: Application Code Conversion SCT can convert embedded SQL in application code. If a question mentions converting Java or C++ applications with embedded Oracle SQL to work with Aurora PostgreSQL, SCT is relevant.
Tip 6: SCT is a Client Application Remember that SCT is installed locally on a workstation - it is not a managed AWS service running in the cloud. It requires network connectivity to both source and target databases.
Tip 7: Extension Packs for Compatibility When questions mention functions or features from the source database that do not exist in the target, extension packs provide emulation capabilities to bridge these gaps.