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Financial institutions face continuously increasing customer expectations, regulatory scrutiny and business model disruption from fintech startups and neobanks. Their future depends on their ability to leverage their treasure trove of data to provide a better and more profitable service. Yet many still rely on mainframes architected for a batch-processing world.
Let’s face it, batch systems were not built for today’s demands. They lag in responsiveness, drive up costs and limit agility, especially when every millisecond counts. That gap between legacy infrastructure and modern demands and expectations grows wider by the day.
While it might seem like a ‘rip-and-replace’ strategy to extinguish mainframes is the way forward, it comes with its own risks and requires years to implement. For a modern bank, this is not a viable solution. Mainframes still serve critical roles in the banking industry. Their reliability, transactional precision and operational integrity make them indispensable for many core functions. However, when institutions rely on them exclusively for both operational and analytical workloads, without a real-time processing layer added, those strengths become constraints.
At Ververica, we helped a top-tier global bank to navigate a smarter route. The solution? A more strategic augmentation of their architecture through mainframe offloading.
Mainframe offloading has become a key strategy for this bank to handle growing transaction volumes and deliver faster digital services. By moving selected workloads from its core IBM mainframe to modern distributed systems, the bank reduces strain on critical infrastructure while cutting costs and enabling real-time processing. At the center of its operations is an IBM mainframe running COBOL-based batch jobs. These jobs power essential financial functions such as interest calculations, fraud detection, account status updates and regulatory reporting.
In just three weeks, the bank was able to offload its most demanding processing jobs to Ververica’s Unified Streaming Data Platform while keeping its mainframe intact. The results:
- 90% reduction in mainframe processing costs — resulting in a saving of over 1 million annually.
- 60% reduction in job runtime.
By streaming data in and out of mainframe systems through Change Data Capture (CDC) and JDBC connectors, the bank now processes millions of events per second with sub-second latency. Batches that previously ran for eight hours now finish in under three. Regulatory reporting updates are enabled in real time, and fraud detection runs continuously to monitor, alert and take action for any suspicious activity. This hybrid model allows the organization to preserve the integrity and resilience of its legacy infrastructure while unlocking new capabilities through real-time data processing.
This transformation centers around several key technical and architectural advancements:
- Efficient Data Export: Using JDBC connectors, the team extracts transactional data stored in VSAM files directly from the IBM mainframe. These exports are carefully orchestrated to minimize the impact on production workloads while ensuring completeness and consistency.
- Modern Codebase Migration: Critical business logic embedded in aging COBOL programs, such as rules for interest accrual or eligibility checks, is systematically refactored into Java, preserving functionality while making the codebase maintainable, testable and extensible in a modern development environment.
- Seamless Cloud Integration: Processed streams are routed into Azure Blob Storage for durable storage and further downstream use. The architecture is built to integrate smoothly with Apache Kafka® for event-driven workflows, and the team has future plans to adopt Apache Paimon™ for efficient lakehouse-style analytics.
- Real-Time Stream Processing at Scale: Apache Flink, managed via Ververica’s enterprise-grade solution, enables the processing of millions of events per second with sub-second latency. This allows the bank to analyze transactions in real-time, enabling immediate responses to anomalies and opportunities alike.
This bank’s approach offers a new paradigm solution. By intelligently offloading select workloads to a real-time, cloud-native platform, they preserved their investment in proven infrastructure while stepping into a future defined by speed, scalability and responsiveness. For enterprises weighed down by outdated architectures, the path forward doesn’t have to mean a full teardown. It can start with selective modernization that delivers impact fast, technically sound, financially justified and operationally secure.
FAQs
What workloads are the best candidates for offloading from the mainframe to modern platforms?
In banking and finance, the use cases that benefit the most are the very time-sensitive ones and those that benefit from highly parallelized computation. Migrating batch processes to real-time is particularly impactful for use cases such as transaction processing, fraud detection, payment processing and real-time gross settlement systems. Beyond finance, numerous industries also can benefit from real-time transformation, including (but not limited to): customer relationship management, inventory management, patient record updates, insurance claims processing, customer data analytics, airline reservations and telecommunications: where responsiveness, accuracy and operational agility are critical. Ververica enables the seamless extraction and processing of streaming data from mainframe sources allowing banks to move time-sensitive, event-driven workloads off the mainframe. This reduces mainframe MIPS usage while enabling real-time insights and responsiveness.
How can we ensure data consistency and integrity when moving processes from the mainframe to distributed systems?
Ververica is built on Apache Flink which provides exactly-once processing semantics ensuring no data loss or duplication, even during failures. This guarantees consistency and correctness when replicating or transforming data streams from mainframe systems. Combined with checkpointing and stateful processing, Ververica maintains data integrity across distributed environments, matching the reliability expected from mainframe-grade operations.
What are the cost implications of offloading versus continuing to maintain or scale mainframe infrastructure?
Mainframes are expensive to scale due to MIPS-based licensing and specialized hardware. By offloading event-driven workloads to Ververica running on cloud or on-prem Kubernetes, organizations achieve significant cost savings. Ververica reduces the dependency on mainframe compute cycles while enabling elastic, pay-as-you-go scaling on commodity infrastructure, lowering TCO and freeing budget for innovation.
How do we integrate offloaded applications with existing legacy systems without creating fragile, hard-to-maintain interfaces?
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Ververica acts as a real-time integration layer between mainframes and modern systems. It supports native connectors (for Kafka and several enterprise databases) and integrates with Flink CDC to ingest data streams from mainframe database transaction logs. Its stream processing pipelines transform, enrich and route data seamlessly. With declarative SQL and Java/Python APIs, workflows are maintainable, observable and resilient replacing brittle batch ETL processes with robust, event-driven integration.
What security and compliance challenges arise when moving sensitive workloads off the mainframe, and how can they be mitigated?
Ververica delivers enterprise-grade security required for financial workloads: End-to-end encryption (in motion and at rest) Role-based access control (RBAC) and integration with LDAP/SSO Audit logging and data lineage tracking GDPR/CCPA compliance via data masking and retention policies. By centralizing secure stream processing, Ververica ensures sensitive financial data remains protected, even as it moves beyond the mainframe perimeter.
Which cloud-native technologies are best suited to replace mainframe functions effectively? How can we measure the success of mainframe offloading in terms of performance, scalability and business agility?
Ververica’s Unified Streaming Data Platform is built for cloud-native, Kubernetes-based environments, making it ideal for modernizing mainframe workloads. It leverages Apache Flink for stateful, low-latency stream processing, Kubernetes for elasticity and resilience, microservices architecture for modularity and scalability CI/CD pipelines for rapid, reliable deployment. This stack enables banks to replace rigid, batch-oriented mainframe processes with agile, real-time, always-on services aligned with fintech-grade architectures.
How can we measure the success of mainframe offloading in terms of performance, scalability and business agility?
Ververica provides real-time observability and monitoring through dashboards, metrics and alerts. Success can be measured by measuring the reduced mainframe MIPS consumption (direct cost savings) sub-second processing latency for critical workflows, system uptime and fault recovery time (rapid restart via state snapshots), throughput scalability under peak loads (e.g.end-of-day batch surges) time-to-market for new real-time services and instant fraud alerts).
