Resilient, continuously active data - with no compromise

Sponsored Feature Today's digital economy is generating data in unprecedented volumes, flooding enterprise IT systems from a multitude of sources, including banking transactions, IoT devices, cloud services, and e-commerce platforms.

Legacy infrastructure can struggle to handle information at this magnitude, especially in scenarios where milliseconds of delay can make a material difference to business outcomes. Traditional IT systems remain well-suited for many tasks, but in a growing number of operationally critical situations, they now introduce a gap between when data is generated and when it is fit and ready to inform action.

That gap, is where risk accumulates and where avoidable extra costs can creep in, damaging profitability. This is especially true in financial services, where decisions are continuous, margins narrow, and errors - whether caused by latency, inconsistency, or unavailability - can have regulatory, financial, and reputational consequences. It's no longer simply a matter of processing data quickly, but of ensuring that vital decisions are based on data that is correct, current, and dependable at the exact moment of use.

Banking in the digital era is all about micro-margins. An IT system designed to detect fraud in financial transactions, for example, needs to flag anomalies as they occur. Approving credit card transactions in milliseconds is not just a customer experience issue. Any delay can mean that a fraud attempt slips through rather than being stopped. In trading and payments, data that arrives too late - or cannot be trusted - can translate directly into financial loss.

The cost of allowing data's value to decay, even briefly, is real. Revenue opportunities are missed, marketing spend is wasted, and operational inefficiencies creep in. A single failure can damage customer trust and attract regulatory scrutiny, eroding both profit and confidence.

Resilient, continuously active data

The solution is not just a matter of technical nitty gritty details, but is tied into an enterprise's whole approach to managing data. This needs to start with real-time data processing, standing in contrast to batch processing which waits to process data in bulk. Real-time systems collect, analyze, and act on data as it is created, enabling decisions that are sensitive not just to speed, but to correctness in the moment.

Of the data management solutions on the market, Hazelcast offers a unique perspective with its vision for resilient, continuously active data without compromise. Rather than focusing solely on throughput or latency, Hazelcast addresses the growing need for operational data that must remain available, usable, and correct while it is actively changing and being acted upon.

Hazelcast's unified architecture combines a fast, distributed, in-memory data store and compute engine with stream processing to support systems where stopping, replaying, or rebuilding state is not an option. It is designed to handle growth, unexpected load spikes, and component or regional failures while systems are live and active, rather than relying on recovery after the fact.

Crucially, this approach eliminates forced trade-offs or compromises. Enterprises should not have to choose between speed and correctness, scale and resilience, or innovation and regulatory compliance.

Hazelcast integrates with existing enterprise infrastructure and modernization initiatives, typically complementing databases and systems of record rather than replacing them outright. As a result, customers with mission-critical applications in financial services, e-commerce, logistics, and transportation use Hazelcast as part of their real-time decisioning, payments, and AI/ML-driven applications at scale.

From front to back-office: resilience as a standard

Ashish Sahu, senior director of product marketing at Hazelcast, describes the platform as a runtime for continuously active operational data that is required to remain active and dependable across the enterprise.

"It is deployed by many of the top banks in the world, across front, middle, and back offices," he explains. "In the front office, Hazelcast is used for digital banking and other customer-facing applications, where latency and availability directly impact customer trust."

In these scenarios, even brief slowdowns can translate into abandoned transactions or reputational damage. Data must be available and consistent while systems are under constant demand.

Sahu points to the middle office as an area where the stakes are often higher but less visible.

"This is where compliance, risk management, and operational resilience come into play," he says. "Regulations like the EU's Digital Operational Resilience Act (DORA), a European Union regulation that requires financial entities to improve their operational resilience, or get penalized, are pushing institutions to demonstrate that systems remain dependable even during disruption."

The resilience that DORA demands, he points out, is about more than just getting the business restarted after disaster. It's about maintaining the correctness and availability of live data at all times.

Failures here can not only cost banks a fortune in fines and customer goodwill, but also in loss of confidence at a board and regulatory level. Hazelcast's built-in fault tolerance, active-active geo-replication, and CP Subsystem for strong consistency keep data correct during active processing, not just during post-failure recovery. It can do this across multiple geographies.

"In the back office, Hazelcast plays a prominent role in payment processing infrastructure, helping with fraud detection and managing SWIFT payments," adds Sahu. "This becomes especially important when organizations modernize toward microservices. If every service depends on a different database, latency and complexity quickly become systemic issues."

Reducing costs and complexity without sacrificing control

Sahu notes that Hazelcast is often deployed in environments where data is constantly changing and being recomputed, rather than simply being read repetitively.

"Some systems work best with historical or relatively static data," he says. "Hazelcast is designed for situations where data is fresh, derived, and acted upon continuously."

Hazelcast's unified in-memory architecture reduces the number of moving parts required to support these workloads, simplifying operations without removing governance or control. Enterprises enjoy lower total cost of ownership and faster time to value due to streamlined licensing fees and minimizing the amount of infrastructure (clusters) needed, all while maintaining consistency and resilience.

"Plus, as a runtime, it can be embedded directly into the application, with no complex installation process," adds Shau. "This takes a lot of headaches away from DevOps. It's easy to use."

He points out that enterprises can save further money on CPU cycles, providing more ROI. Research from an Association for Computing Machinery (ACM) study examined the cost impact of distributed in-memory caches. Under the right conditions, it was found that operating costs can be reduced by up to 4x due to savings from reduced CPU consumption. By serving frequently accessed or computationally expensive data from memory, systems avoid repeated query processing, serialization, and network hops to backend storage. These are often the dominant drivers of cloud cost.

The study also makes clear that these savings depend on some architectural assumptions. The strongest gains appear in read-heavy or skewed workloads, particularly where applications repeatedly assemble complex or 'rich' objects from multiple backend queries.

Conversely, designs that rely on frequent per-read consistency checks or rebuilding derived state can quickly erode the benefit, as those checks reintroduce backend processing and coordination overhead. This highlights why enterprises are paying closer attention not just to performance, but to whether data platforms can preserve correctness while data remains active, rather than validating it after the fact.

When modernization creates challenges

ING Türkiye offers a practical example of the business value of resilient, continuously active data. A long-standing digital banking innovator, the bank embarked on a major modernization initiative, migrating from monolithic C# applications to around 300 Java-based microservices running on Red Hat OpenShift.

The objective was to improve scalability, performance, and agility, supporting more than 25 million transactions per day while accelerating the rollout of new digital services.

However, the new architecture introduced an unexpected constraint. The microservices generated a surge of requests to a centralized Oracle database, creating performance bottlenecks, scalability issues, and cost pressures. Database dependency became a limiting factor, undermining the benefits of modernization and increasing operational risk during peak demand.

After evaluating several possible solutions, ING Türkiye chose Hazelcast based on its proven performance at linear scale, ease of implementation, integration with OpenShift, and its light resource footprint. It implemented Hazelcast as a unified, distributed in-memory computing and caching layer, providing the high-speed data access required and offering a resilient foundation for existing and future applications, without compromising data consistency.

"We eliminated latency and unlocked independent scaling across 300 microservices," says Doğukan Guran, chapter lead of core frameworks, platform, and BPM at ING Türkiye. "This turned our architecture into a growth engine that keeps the digital banking experience fast even at over 25 million daily transactions, and gives our teams a platform they can build on quickly."

Beyond performance, the move to Hazelcast delivered measurable business value across the organization. By drastically reducing system latency, the bank now provides a consistently fast and seamless mobile banking experience for its millions of users. Hazelcast handles growing transaction volumes with ease, enabling microservices to scale independently without performance degradation and significantly increasing overall business agility.

Beyond the customer experience, the adoption has improved operational efficiency, ensuring all services are delivered within their strict SLAs while empowering development teams with a platform that simplifies data management and accelerates the delivery of new services. Crucially, the move has led to a lower total cost of ownership by reducing dependency on expensive legacy database infrastructure and enabling more streamlined resource utilization for significant cost optimization.

Time to level up data handling at speed

Data is not just increasing in volume. It is becoming faster and is constantly changing or being acted upon. This makes it less forgiving, where availability, resilience, and latency creep in. In banking, data's real value increasingly exists in the narrow window between 'just happened' and 'decision made'.

ING Türkiye's experience shows that modernization alone is not enough. Microservices and cloud-native architecture can amplify bottlenecks if the data layer cannot keep up or cannot remain correct under continuous load.

Banks and large enterprises often engage Hazelcast when they see signals such as:

In these environments, 'near-real time' is often another way of saying 'too late'. Hazelcast addresses these scenarios by supporting data that stays active, correct, and available while systems are running. For institutions where decisions cannot wait for recovery, rebuilds, or reconciliation, that distinction matters

Customers and industry analysts have also recognized this positioning. Hazelcast was named a Strong Performer in the Forrester Wave™ for Streaming Data Platforms, Q4 2025. Forrester noted that "Hazelcast excels at extensibility and fault tolerance, enabling robust performance for low-latency streaming workloads in enterprise applications."

Forrester's assessment aligns with Hazelcast's broader focus on resilience and correctness for continuously active data, particularly in environments where operational risk and regulatory accountability are front-of-mind.

Sponsored by Hazelcast.