Compare AWS Mainframe Modernization vs. Rocket Data Virtualization in 2026

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Description

The AWS Mainframe Modernization service offers a distinctive platform that facilitates the migration and modernization of your on-premises mainframe applications into a fully-managed, cloud-native runtime environment on AWS. By transitioning your applications to the cloud, you can eliminate the hardware and personnel expenses associated with conventional mainframes. This service enables you to systematically manage your entire migration process through a combination of infrastructure, software, and tools designed to refactor and convert legacy applications. It also allows for accelerated modernization and extensive regression testing in a scalable manner with its cloud-native capabilities. As a comprehensive suite of managed tools, AWS Mainframe Modernization provides the necessary infrastructure and software to modernize, migrate, test, and operate mainframe applications effectively. Embarking on your mainframe modernization journey becomes easier, leading to improved outcomes by leveraging specialized knowledge in the field. In addition, it simplifies project complexity and fosters better collaboration across different teams. Furthermore, you can automate the transformation of legacy programming language applications into agile Java-based services, utilizing AWS Blu Age and contemporary web frameworks, ensuring your applications are not only modernized but also optimized for the future. This service positions your organization to remain competitive and responsive in an ever-evolving technological landscape.

Description

Hybrid data stacks create duplication and delay: mainframe records, on prem apps, and cloud platforms often end up with mismatched copies, brittle ETL, and long lead times for “just one more feed.” Moving large datasets for every use case is slow, costly, and expands the security surface. Rocket® Data Virtualization™ is a data virtualization and federated query solution that enables a governed, virtual data model across mainframe, distributed, and cloud sources—so BI tools, analysts, and applications can query sensitive data in place. Key capabilities: • Federated SQL queries/joins across heterogeneous sources with pushdown • Standard connectivity (e.g., JDBC/ODBC/REST) for BI, analytics, and apps • Virtual views/semantic layer to simplify access and reuse logic • Centralized security controls, auditing, and masking (where supported) • Optional caching/materialization to balance performance and freshness Result: faster time to data with less ETL and lower migration risk.