Author: Phil de Valence

Organizations migrate and modernize mainframes to AWS for agility, cost reduction, and for mitigating mainframe risks. Learn about the key agility attributes needed by mainframe workloads, and explore the evolutionary approach to quickly and incrementally modernize from a mainframe monolith to agile AWS services. In this post, we focus on application and infrastructure agility to change functionality, configuration, or resources quickly and inexpensively.

Historically, mainframes have hosted core-business processes, applications, and data, all of which are blocked in these rigid and expensive systems. AWS and Qlik can liberate mainframe data in real-time, enabling customers to exploit its full business value for data lakes, analytics, innovation, or modernization purposes. In this post, we describe how customers use Qlik Replicate real-time data streaming to put mainframe core-business data onto AWS.

Mainframes typically host core business processes and data. To stay competitive, customers have to quickly transform their mainframe workloads for agility while preserving resiliency and reducing costs. There is a challenge in defining the agility attributes and prioritizing the corresponding transformations for maximum business value in the least amount of time. In this post, dive deep in the practical agility attributes needed by mainframe workloads, and how to accelerate the transformation towards such agility with AWS.

Large mainframes have challenging non-functional requirements in order to process large volumes of data and users for core-business workloads. Micro Focus Enterprise Server mainframe capabilities, combined with AWS global infrastructure, provide the quality of service required by large applications. With this solution, enterprises and public institutions deploy mainframe workloads on AWS with high security, high availability, elasticity, and robust system management.

Many companies or institutions still possess legacy non-x86 systems in their datacenters: mainframe, midrange, or UNIX proprietary systems. Migrating these workloads across hardware architectures to the AWS Cloud requires advanced software technology. Short-term migration approaches mainly use hardware emulation, middleware emulation, automated refactoring, or middleware replatforming. This post highlights short-term migration options, their key technical differences, as well as their differentiated benefits.

The New York Times had a critical business workload running on a mainframe as the core IT system supporting the daily Home Delivery Platform of its newspaper. They collaborated with APN Partner Modern Systems to successfully transform their legacy COBOL-based application into a modern Java-based application, which today runs on AWS. This post describes the project, including the automated refactoring process and AWS architecture, as well as key lessons learned, business outcomes, and future technology plans.

Customers that still have business-critical data locked in mainframes want to exploit this data with AWS agile services. Fortunately, Treehouse Software’s tcVISION replicates data in real-time and bi-directionally between mainframes and AWS to allow for these new use cases. Learn about the solution, customer use cases, and explore a practical example of how to replicate data in real-time from DB2 z/OS to Amazon Aurora.

Companies still rely on mission-critical applications running on Sun SPARC, Alpha, or other legacy systems like PDP, VAX, or HP 3000. Maintenance costs for these systems goes up as time goes on, and reliability declines due to their age. Fortunately, they can be re-hosted on AWS in a matter of days using cross-platform hypervisors from Stromasys. Here, we describe the challenges customers face with legacy systems, the Stromasys technical solution, and a successful customer migration to AWS.

Ippon Technologies has successfully re-written a large mainframe third-party software package to Java Angular Spring Boot microservices. The package supported 130 TPS and 1,800 MIPS, catered to over 5,000 users, and housed more than 5 TB of business-critical data. Ippon helped the customer define the approach and architecture, and then developed the microservices along with the CI/CD pipeline on AWS. Learn about the project’s technical aspects, methodologies, and lessons learned.

A multi-company team led by ARRAY delivered for the U.S. Air Force a successful modernization of a COBOL-based system running on aged mainframes to a Java-based system running on x86 Red Hat Enterprise Linux (RHEL). AWS provides the system’s capabilities for reliability, scalability, and this post describes the objectives, approach, solution, lessons, and customer benefits realized from this experience.