Author: Alexis Henry

Mainframe workloads are often tightly-coupled legacy monoliths with millions of lines of code, and customers want to modernize them for business agility. Manually rewriting a legacy application for a cloud-native architecture requires lots of re-engineering. See how AWS Blu Age transformed a typical mainframe CICS application to AWS containers and AWS Lambda functions. You’ll also learn how to increase mainframe workload agility with refactoring to serverless and containers.

Mainframe monoliths have grown over the years with overwhelming complexity. They often mix different languages and data stores with various interfaces, evolving coding standards, online and batch, and millions of lines of code. With AWS, customers use microservices for agility, elasticity, and pay-as-you-go cloud technology. In this post, we explore AWS Blu Age solutions to peel off AWS microservices from a mainframe monolith, and how to solve data challenges associated with such a migration.

While modernizing customer mainframes, the team at AWS Blu Age discovered that Batch can be a complex aspect of a mainframe migration to AWS. It’s critical to design your AWS architecture to account for the key Batch stringent performance requirements such as intensive I/Os, large datasets, and short durations. Let’s explore how to migrate mainframe Batch to AWS microservices using AWS Blu Age automated transformation technology.