Mainframe Migration to AWS With Java or C#

Moving a COBOL application to AWS means moving the work it does every day, from processing customer transactions to closing the books overnight. Converting the code to Java or C# is one part of that move. This guide explains how to choose which workloads to migrate first, design the AWS environment around how they run, and verify that they deliver the same business results before going live.

Mainframe migration to AWS can use automated refactoring to move supported COBOL workloads to Java or C#. Amdocs combines rule-based code and data conversion with application and data testing. The AWS design depends on how transactions, batch jobs and operational controls work together.

Modernizing on AWS can make sense when changes to a COBOL application depend on a shrinking pool of specialists, new services are hard to integrate, or mainframe operating costs are rising. Refactoring selected workloads to Java or C# can give teams a codebase they can maintain with existing skills and release through familiar delivery pipelines. Start with a workload where the benefit is clear, such as a billing application that needs to connect to a new customer portal, and verify that critical processes such as invoice calculations and overnight reconciliation still work as expected. 

Key Takeaways

• Choose the migration path by workload before selecting AWS services.
• Design compute, data, integration and batch scheduling together.
• Test business outputs, peak load, failure recovery and the cutover plan on the target.

Map the workload and choose the first wave

Inventory COBOL, copybooks, JCL, schedulers, transaction interfaces, databases and external services. Identify where the mainframe and AWS must exchange data while both are in service. Pick a first wave that represents key patterns and has clear dependencies and acceptance criteria.

Select AWS services based on the workload

Amazon EC2 can fit workloads that need VM-level control. For container-ready applications, evaluate Amazon ECS or Amazon EKS against runtime dependencies and team skills. Consider Amazon RDS where the selected database engine and operating requirements fit. Define where jobs run, how schedules and dependencies are enforced, and how applications read and write shared data.

Design identity, network access, encryption, monitoring and recovery with the application. Test connectivity to systems that remain on premises. Pay particular attention to synchronous calls that cross environments and to the volume of data moved during batch windows. Benchmark those flows with representative inputs.

Convert code and prove the AWS target

Use CodeTurn for supported code transformations and DataTurn for mapped data conversion. Agentic AI can assist with understanding legacy rules and planning work. Engineers validate those findings, govern the conversion rules and review how the new application handles operational dependencies.

Replay representative transactions and compare original and target batch outputs using application tests and data reconciliation. Test failover, restore procedures and batch restart. Rehearse the cutover with realistic data volumes and agree when the team will stop writes, run the final reconciliation, switch traffic or roll back. Confirm the operations team can support the workload from the first day.