SQL Server modernization
AWS Transform for SQL Server Modernization is an AI-powered service that automates the full-stack modernization of Microsoft SQL Server databases and their associated .NET applications to Amazon Aurora PostgreSQL. AWS Transform now converts SQL Server storage objects, powered by AWS DMS, and code objects (stored procedures) using an agentic, interactive experience. The service orchestrates the entire migration journey from schema conversion, data migration and modifying application code to match the new target PostgreSQL, making your teams more productive by automating complex and labor-intensive tasks.
Supported regions
AWS Transform for SQL Server is available in US East (N. Virginia) - us-east-1
Cross-Region Usage: For databases in unsupported regions, you can clone the database to a supported region for transformation, then deploy the results back to your target region.
Capabilities and key features
Database transformation
Schema conversion: Automatically converts SQL Server schemas to Aurora PostgreSQL, including tables, views, indexes, constraints, and relationships
Stored procedure transformation: Converts T-SQL stored procedures to PL/pgSQL with AI-enhanced accuracy
Data migration: Migrates data with integrity validation using AWS Database Migration Service (DMS)
Database objects: Supports triggers, functions, views, computed columns, and identity columns
Validation: Automated data integrity verification and referential integrity checks
Application transformation
Entity Framework transformation: Updates Entity Framework 6.3-6.5 and EF Core 1.0-10.0 configurations for PostgreSQL
ADO.NET transformation: Converts ADO.NET data access code from SQL Server to PostgreSQL providers.
Connection string updates: Automatically updates all database connection strings to the new target PostgreSQL database
Database provider changes: Replaces SQL Server providers with Npgsql (PostgreSQL provider)
ORM configuration updates: Modifies data type mappings, identity columns, and database-specific configurations
Orchestration & validation
Wave-based modernization: Organizes large estates into logical migration phases
Dependency mapping: Identifies relationships between applications and databases
Human-in-the-loop (HITL) checkpoints: Provides review and approval gates at critical stages
Automated validation: Tests schema compatibility, data integrity, and application functionality
CI/CD integration: Integrates with existing development pipelines
Deployment
Amazon ECS and Amazon EC2 deployment: Automated containerized deployment with auto-scaling support
Infrastructure-as-code generation: Creates CloudFormation or AWS CDK templates
Automated deployment validation: Verifies successful deployment with health checks
Rollback capabilities: Supports rollback procedures if issues arise
Supported versions and project types
SQL Server versions
AWS Transform supports the following SQL Server versions:
| SQL Server Version | Support Status |
|---|---|
| SQL Server 2022 | Supported |
| SQL Server 2019 | Supported |
| SQL Server 2017 | Supported |
| SQL Server 2016 | Supported |
| SQL Server 2014 | Supported |
| SQL Server 2012 | Supported |
| SQL Server 2008 R2 | Supported |
Note
All SQL Server editions are supported (Express, Standard, Enterprise). SQL Server can be hosted on AWS (Amazon RDS for SQL Server or SQL Server on Amazon EC2) or hosted outside of AWS.
.NET versions
| .NET Version | Support Status |
|---|---|
| .NET 10 | Supported |
| .NET 8 | Supported |
| .NET 7 | Supported |
| .NET 6 (Core) | Supported |
| .NET Framework 4.x and earlier | Not Supported |
Important
Legacy .NET Framework 4.x and earlier versions are not supported. If your application uses .NET Framework, you must first upgrade to .NET Core 6+ using AWS Transform for .NET modernization before using SQL Server transformation capabilities.
Entity Framework versions
| Framework | Supported Versions |
|---|---|
| Entity Framework 6 | 6.3, 6.4, 6.5 |
| Entity Framework Core | 1.0 through 10.0 |
| ADO.NET | All versions (GA) |
Source code repositories
AWS Transform supports the following source code platforms:
GitHub and GitHub Enterprise Server
GitLab.com and GitLab Self-Managed
Bitbucket Cloud and Bitbucket Data Center
Azure DevOps and Azure DevOps Server
Amazon S3
Target database
AWS Transform targets Amazon Aurora PostgreSQL (PostgreSQL 15+ compatible) with support for the latest Aurora features and optimizations.
Technical requirements
Database requirements
Microsoft SQL Server version 2008 R2 through 2022
SQL Server hosted on AWS (Amazon RDS for SQL Server or SQL Server on Amazon EC2) or hosted outside of AWS
For AWS-hosted databases, the database and AWS Transform must be in the same AWS Region.
For databases hosted outside of AWS, network connectivity to the AWS Transform service is required.
Database user with VIEW DEFINITION and VIEW DATABASE STATE permissions
Database passwords using printable ASCII characters only (excluding '/', '@', '"', and spaces)
VPC containing the source SQL Server must have subnets in at least 2 different Availability Zones (required for DMS replication subnet groups)
Application requirements
.NET 6, 7, 8, or 10 applications
Entity Framework 6.3-6.5 or Entity Framework Core 1.0-10.0, or ADO.NET
Database connections discoverable in source code
Applications successfully build and run
Source code in supported repository platforms
AWS account requirements
AWS account with administrator access
IAM Identity Center enabled
Required service roles created (see setup instructions below)
VPC with appropriate network configuration
Data processing and storage
Processing location
Schema processing occurs in a DMS instance within your VPC
Data migration is optional and can be excluded if required
Transformation artifacts are stored in the AWS Transform service region
Stored artifacts
The following items are stored in the service region:
Agent logs
Assessment results
SQL schema files
DMS output artifacts
Important
Important for Data Residency: Even when data migration is opted out, metadata and processing artifacts are stored in the service region. This is important for organizations with strict data residency requirements.
Artifact management
Customer option for encryption using your own KMS keys
Defined TTL (time-to-live) period for all artifacts
Artifacts can be downloaded for offline storage
