AWS Transform FAQs

AWS Transform is an agentic AI service designed to support large-scale modernization of full-stack Windows workloads (including .NET and SQL Server), transformation of mainframe applications to modern languages and architectures of VMware workloads to Amazon EC2, and custom transformations for code, APIs, frameworks, and more.

You can access AWS Transform’s unified web experience tailored for large-scale modernization and team collaboration at https://console.aws.amazon.com/transform/home. For custom transformations for code, APIs, frameworks, and more, the service operates through both CLI and web interfaces. For select .NET applications requiring developer attention, developers can also use AWS Transform in Visual Studio IDE.

AWS Transform’s approach for migration and modernization differs from traditional tools on three fundamentals. First, AWS Transform offers specialized task agents for various tasks – ranging from network generation to extracting business rules from COBOL to porting .NET code. These agents combine specialized knowledge built on years of experience with enterprise-specific context. Second, the service uses agentic AI to orchestrate execution of these expert task agents, unique to each workload. Depending on the task, orchestration ranges from deterministic execution to goal driven, dynamic plans. The product focuses on getting the jobs done, integrating with humans in the loop or invoking coding agents. Third, learning capability is built-in at each level. The agents continually self-debug and improve outcomes and provide recommendations for next steps.

To get started, sign in to the AWS Transform web experience with your current enterprise credentials. If you are a new customer, you can use single sign-on (SSO) with AWS IAM Identity Center integration and connect it to an AWS account to get started. Alternatively, you can set up direct federation with Okta or Microsoft Entra. To learn more, see AWS Transform User Guide.

AWS Transform assessments analyzes your IT environment to simplify and optimize your cloud journey with intelligent, data-driven insights and actionable recommendations. Discover cost and performance optimization opportunities while getting detailed financial modeling to help you confidently plan your migration and maximize potential savings.

The workflow begins with uploading your existing server inventory to the AWS Transform platform. Once your data is in place, you have the opportunity to specify your target AWS Region. Next, you can then instruct AWS Transform to generate your business case. AWS Transform analyzes your server inventory and identifies the most suitable and cost-effective Amazon EC2 instances for each one. The resulting business case provides you with a clear, data-driven projection of how your current on-premises environment could map to AWS services, offering valuable insights for your migration planning and decision-making process.

AWS Transform supports a variety of data collection methods for x86 servers, whether virtual or physical, from on-premises environments. The service accepts server inventory data from several widely used assessment tools. These include exports from RVTools, data collected through AWS Transform discovery tool or the AWS Migration Evaluator agentless collector, and AWS Migration Portfolio Assessment (MPA) exports generated by tools like modelizeIT and Cloudamize.

After the completion of the assessment job, AWS Transform provides a summary of the assessment, an opportunity to ask questions about the cost and recommendations and the option to download a PDF version of the business case for offline review and sharing.

The business case includes key highlights from the server inventory, a summary of current infrastructure, and multiple total cost of ownership (TCO) scenarios with varying purchase commitments (on-demand and reserved instances), operating system licensing options (bring your own licenses and license-included) and tenancy options (dedicated and shared). The business case also includes actionable next step recommendations.

AWS Transform assessments provide directional estimates that approximate the cost of AWS services based on your current server configurations and assumed usage patterns. While these estimates are helpful for initial planning purposes, they should be viewed as guidance rather than exact figures. Actual AWS costs may vary depending on your specific implementation, resource optimization choices, and real-world usage patterns. It's important to note that these estimates are not quotes and should not be interpreted as guarantees of your final AWS service costs. For more precise cost planning, we recommend working with your AWS account team or an AWS Partner who can help analyze your specific requirements and usage patterns in detail.

AWS Transform Assessment and AWS Migration Evaluator are both valuable tools for planning cloud migrations. Assessments is a fast, self-service capability of AWS Transform, designed specifically for organizations looking to migrate x86 servers from on-premises environments to AWS. It utilizes existing server inventory data to provide targeted recommendations for Amazon EC2 instances and generate quick TCO estimates. This streamlined approach is ideal for companies seeking a rapid, focused assessment of their migration options. AWS Migration Evaluator offers a more comprehensive, expert-led assessment service. Guided by AWS Solutions Architects, this in-depth evaluation encompasses a broader range of analyses, including detailed data collection, storage assessment, sustainability evaluation, and Microsoft SQL Server analysis. Migration Evaluator is best suited for organizations that require thorough migration planning and desire expert guidance throughout the process.

AWS Transform has a built-in AI chat capability so you can ask for more details or clarification about instance mapping, licensing and tenancy suggestions, and next step recommendations. For further support or additional analysis for other workload types, engage with your account team or partner or contact us.

AWS Transform for Windows includes two main components: transforming .NET Framework applications to cross-platform .NET and migrating Microsoft SQL Server to Aurora PostgreSQL databases along with the dependent .NET application. 

AWS Transform for .NET accelerates modernization of Windows-based .NET Framework applications to cross-platform .NET for Linux environments. It connects to your source code repositories in GitHub, GitLab, Azure Repos, or Bitbucket and performs a comprehensive analysis focused on three key areas: repository dependencies, required private packages and third-party libraries, as well as identifying supported project types. Based on this analysis, it generates a transformation plan for these repositories and highlights any missing dependencies that you can resolve by uploading packages yourself. During the transformation process, AWS Transform for .NET converts application code, builds the output, runs unit tests, and commits results to a new branch in your repository. You can then deploy the transformed application as a container on Amazon EC2 or Amazon ECS.

AWS Transform for SQL Server modernization accelerates migration of your Microsoft SQL Server databases and applications to Aurora PostgreSQL. It connects to your SQL Server databases running on Amazon EC2 or Amazon RDS to discover the schemas and the stored procedures in your databases. It then performs a detailed analysis of databases and applications to create waves of applications, and databases that can be transformed together based on dependency relationships. It then transforms SQL Server schemas to Aurora PostgreSQL and migrates databases to new or existing Aurora PostgreSQL target clusters. For .NET applications transformation, the service updates database connections in the source code and modifies ORM code in Entity Framework and ADO.NET to be compatible with Aurora PostgreSQL — all in a unified workflow with human supervision.

In both workflows, AWS Transform provides a comprehensive transformation summary, including modified files, test outcomes, and suggested fixes for any remaining work. Your teams can track transformation status through its interactive chat or worklogs. Additionally, your teams receive email notifications with links to transformed .NET code in your repositories. For workloads that need further processing, your developers can continue using the Visual Studio extension in AWS Transform.

AWS Transform for Windows discovers the repositories in your account and identifies supported project types in each repo. It supports porting console applications, class libraries, Web APIs, WCF Services, Model View Controller (MVC), Single Page Application (SPA), and unit test projects (xUnit, NUnit, MSTest frameworks) to cross-platform .NET (full list available here). In addition, AWS Transform for Windows also ports MVC Razor views UI projects to ASP.NET Core Razor views, UI porting of ASP.NET Web Forms to Blazor on ASP.NET Core, porting Entity Framework and ADO.NET ORM code for Aurora PostgreSQL compatibility, porting of WinForms, WPF, and Xamarin projects to cross-platform .NET, and support for VB.NET language projects.

After identifying project types, it analyzes these projects for dependencies on other projects, private packages, and third-party libraries. Based on the dependency analysis, AWS Transform for Windows recommends a transformation plan that orders repositories according to their last modification dates, dependency relationships, and private package requirements.

You can download the analysis report to evaluate the recommended plan and review it with your team. You also have the option to customize the recommended plan by editing the selection in the console or by uploading a modified file with your preferred selection. Administrators and approvers can review and approve the plan before proceeding with the transformation process.

During transformation, the selected source code repositories from your approved plan are securely fetched into a network-isolated execution environment for transformation to cross-platform .NET. AWS Transform for .NET supports transforming applications written using .NET Framework versions 3.5+, .NET Core 3.1, .NET 5, .NET 6, and .NET 7 to cross-platform .NET 8 (LTS), and .NET 10, and database access frameworks Entity Framework, and ADO.NET.

After porting, AWS Transform runs a full .NET build to identify any build errors and runs an AI-led evaluation loop to auto-remediate issues. This process is repeated across all supported projects within the repositories. After the transformation job is completed, the transformed code is committed back to your source code repository in your chosen target branch for review.

For .NET source-code repositories that have successfully completed transformations with zero build errors, AWS Transform executes unit test projects, if present, and provides those execution results for your review. For repositories that have partially transformed projects, unit test projects are ported but are not run. You can resolve remaining issues yourself before running the unit tests.

AWS Transform also supports deploying the transformed applications to a target environment for customers to validate the transformed applications.

AWS Transform for Windows first discovers the databases that are running in your AWS account. It then identifies the databases running the Servers, schemas, and stored procedures associated with the databases. It also analyzes the source code repositories to identify database dependencies in the repos, embedded SQL queries, and database access code written in Entity Framework, and ADO.NET. Based on the analysis, it then creates customizable wave plans for databases and application transformation so they can be transformed together.

You can download the analysis report to review the modernization recommendations, complexity of the transformations, and the databases, and source code repository invoking the databases.

AWS Transform for Windows transforms SQL Server in 3 steps: 1) Schema conversion, 2) Data migration, and 3) Code transformation.

During database schema conversion, the schemas from the selected databases are converted from Microsoft SQL Server schemas to Aurora PostgreSQL-compatible schemas. If there are issues in the schema conversion, AWS Transform for Windows will automatically run an AI-led evaluation loop to auto-remediate the issues. The process is repeated across all the schemas in the databases in the wave. Similarly, if there are stored procedures in the SQL Server databases, they are ported to be compatible with Aurora PostgreSQL databases as well. Once the schemas are successfully converted, they will be applied to the target Aurora PostgreSQL databases.

After the schemas are fully transformed for your target PostgreSQL database, you have the option to migrate your data from your SQL Server databases to Aurora PostgreSQL databases. During this stage, AWS Transform for Windows migrates your data to the transformed PostgreSQL databases. If there are any issues during the migration process, you will be informed of the migration issues and a migration report to troubleshoot the failures.

Finally, the source code repositories are updated to match the PostgreSQL target database created. The connection strings are updated to match the PostgreSQL database, embedded SQL code is ported to be compatible with PostgreSQL, and Entity Framework and ADO.NET are updated to match the new database. After the transformation is completed, the updates are committed to a new source code repository branch that you had provided. You can review a detailed transformation summary of the updates that AWS Transform performed during this step.

For .NET code transformations, you can track all modification actions through detailed transformation reports provided for each repository in natural language. These reports outline the files, APIs, and private NuGet packages that were modified, moved, or updated during the process. When repositories are partially transformed, the summary report includes specific details about build errors and schema transformation failures, along with recommendations for resolving these issues. All transformed source code is committed to a new target branch that you specify during the job, allowing you to check out the branch and review the code changes performed by AWS Transform.

For SQL Server modernization, you can monitor schema conversion and data migration actions through reports available after the transformation steps are completed. These reports are accessible both immediately after transformation and through the Migration Project page in the AWS Data Migration Service (AWS DMS) Console. Similar to .NET transformations, you can track source code changes from the feature branch. Additionally, you can validate the transformation results by examining the deployed database schemas and stored procedures in your target PostgreSQL database.

In the web experience, you can monitor transformation progress in real time through two main methods. The interactive chat provides dynamic updates and responses based on the current job plan and context of your questions, accessing a comprehensive knowledge base about ongoing jobs and actions. The worklogs offer detailed documentation of all actions performed by AWS Transform for Windows on your source code and databases, including user approvals and audit trails.

In Visual Studio IDE experience, when transforming .NET applications in Visual Studio, progress monitoring is available through the AWS Transform Hub. This interface displays the estimated remaining time, detailed transformation steps, and an activity worklog.

Additionally, you'll receive comprehensive transformation summary reports for each repository, detailing modified files, API changes, and updates to private NuGet packages.

Upon job completion, you'll receive an email notification containing deep links to review the transformed repositories. 

For .NET code transformations: AWS Transform provides a detailed transformation summary report including a next steps markdown file that outlines remaining tasks, such as Linux readiness issues and database access code updates. You can either use this information to initiate another transformation with AWS Transform or use it as guidance for an AI code companion.

For SQL schema conversion and data migration: The schema conversion report shows the percentage of successfully transformed schema and provides guidance for completing unfinished work. You can address remaining schema conversions using either the AWS Database Migration Service (AWS DMS) console's schema conversion page or IDEs like DBeaver. For data migration errors, you can review the data migration report to address the migration issues.

You are the owner of the code ported by AWS Transform for full-stack Windows modernization. Once the porting of source code is completed, the transformed code is committed to a branch of choice in your repository. AWS Transform does not store any copy of the transformed code after the code has been committed to the branch.

The same ownership principle applies to database schemas transformed using AWS Transform and AWS DMS. You own all converted schemas and can download, modify, and upload them to your target database. AWS Transform does not retain any schema information after job completion.

The AWS Transform .NET agent gets access to your source code through AWS CodeConnections service, which must be approved by an IT admin for your AWS account prior to accessing the source code. It then analyzes your code to identify inter-project dependencies and private packages used within the projects to recommend a transformation plan. The service is designed to securely and ephemerally clone your .NET solution, allowing you to use customer managed KMS keys  for encrypting your code in this environment. Customer managed KMS keys  allows you to have full control over keys, including managing policies, grants, tags, and aliases for accessing data.

Your source code processed by AWS Transform is stored only for the duration of the job and purged after the job is completed. Your trust, privacy, and security of your content are our highest priority. We implement appropriate controls, including encryption in transit, to prevent unauthorized access to or disclosure of your content and ensure that our use complies with our commitments to you.

AWS Transform securely analyzes your database schemas through a database connector, requiring explicit IT admin approval from your AWS account. Similarly, access to source code repositories is managed through AWS CodeConnections service, also requiring IT admin approval.

Database access is secured through AWS secret keys and user credentials that you provide to the AWS Transform agent. During schema conversion, the transformed schemas are deployed directly to your target Aurora PostgreSQL database within your specified AWS account, VPC, and subnet.

AWS Transform maintains strict security protocols throughout the process, never storing database information permanently. All database conversion information is deleted after job completion, and transformed code is committed only to your designated feature branch without any retention after the job is finished. This process ensures your database code and schemas remain secure throughout the transformation process while maintaining complete control within your AWS environment.

AWS Transform for mainframe is an agentic AI-powered service designed to accelerate the modernization of legacy mainframe applications. Customers can define high-level modernization goals and leverage a specialized AI agent to orchestrate the necessary tools and processes. The agent analyzes applications, generates documentation, extracts business logic, decomposes monolithic structures, transforms legacy code, automates testing, and manages modernization tasks, offering human-in-the-loop oversight where desired.

Key capabilities of AWS Transform include flexible, goal-driven planning, classification of application assets, planning and documentation generation with business logic extraction, comprehensive testing capabilities, automated refactoring that converts COBOL-based mainframe workloads into modern, cloud-optimized Java applications, and AI-powered reimagination capabilities.

AWS Transform empowers customers to modernize their critical mainframe applications faster, more cost-effectively, and with confidence that their business-critical logic will be preserved throughout the transformation process.

AWS Transform for mainframe supports both reimagine and refactor modernization patterns, offering flexible pathways to modernize legacy mainframe applications.

Refactoring with AWS Transform automates the transformation of COBOL-based mainframe applications into modern Java applications running on AWS, using agentic AI to analyze codebases, generate documentation, decompose monoliths, plan modernization waves, automate testing functions, and accelerate code refactoring while maintaining functional equivalence to the legacy stack.

Reimagining with AWS Transform enables transformation of mainframe applications to cloud-native architectures, leveraging automated analysis to convert monolithic applications into modern, agile solutions that can fully utilize cloud-native capabilities. Through a chat-centric, flexible agent experience, AWS Transform analyzes code and data, extracting information for technical and business documentation that drive the forward engineering of reimagined workloads.

A key feature of AWS Transform is its ability to break down monolithic mainframe applications into modular, business-aligned domains, and then generate comprehensive modernization waves. Using the business logic extraction in conjunction with the decomposition step helps break down monoliths into logical business domains.

Leveraging automated reasoning and planning capabilities, AWS Transform analyzes your codebase, identifies discrete functional areas, and organizes the application assets accordingly. It then creates detailed, prioritized modernization plans that consider factors like business priorities, technical complexity, and constraints. Through data and activity analysis, AWS Transform can also help identify application components with low utilization or minimal business value, enabling more informed decisions about target architecture.

This domain-driven decomposition and thoughtful planning allows you to tackle the modernization in manageable, iterative steps. By providing this visibility and structure up front, AWS Transform empowers you to focus your efforts, make informed decisions, and execute the modernization quicker.

AWS Transform for mainframe offers testing capabilities designed to reduce the time and effort required for mainframe modernization testing, which typically consumes over 50% of project duration. This includes automated test plan generation, test data collection scripts creation, and test case automation scripts creation. The service also includes a refactored functional test environment with tools for continuous regression testing, data migration, and results variation.

These agentic AI-powered capabilities work together to reduce dependency on scarce mainframe expertise, accelerate testing timelines, and improve accuracy through automation, helping customers modernize their mainframe applications with greater confidence and efficiency.

Yes, AWS Transform for mainframe is modular, allowing you to leverage its capabilities for as many or as few phases of the modernization journey as you choose. For example, when reimagining an application, you might initially focus on analysis across codebase, data structures, and activity, and later layer in documentation to inform the forward engineering of the reimagined application.

Inventory collection encompasses various mainframe components including COBOL programs, copybooks, Job Control Language (JCL), procedures and parameter cards, and DB2 definitions. If available, Customer Information Control System (CICS), Information Management System Transaction Manager (IMS TM), and CSD files should be loaded to determine entry points.

The extraction process begins by downloading source elements through text mode, converting each member into individual source files. Files should be organized in a structured directory system that reflects their origin, language, type, and application/sub-application relationships (for example, C:\Mainframe\APP1\Cobol\Program1.CBL or \Mainframe\APP1\JCL\JCL1.txt). If no file extension is provided, AWS Transform will determine the appropriate extension based on the file contents to classify the member.

The collected inventory is then compressed into a zip file and uploaded to an S3 bucket. The process might be iterative, with an initial upload followed by subsequent iterations of missing components until reaching satisfactory completeness.

After code transformation, you have the option to use pre-built Infrastructure as Code (IaC) templates to deploy your modernized applications. These templates are accessible through the AWS Transform chat interface, helping create the necessary compute resources, databases, storage, and security controls. Templates are available in AWS CloudFormation, AWS CDK, and Terraform formats.

You can also use the Reforge step to enhance your transformed Java code with improved readability and maintainability before deployment. To use this feature, provide your refactored code and java class list to specify which service classes to reforge. AWS Transform will generate downloadable files containing the reforged code.

AWS Transform provides the ability to specify files within your source code to generate documentation. You can choose summary overviews of file collections or detailed functional specifications for each file. The detailed specifications include logic flows, input/output processing, and other transactional details.

Once generated, you can access this documentation