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This whitepaper discusses how an in-memory computing platform is used in the healthcare industry to help improve patient care.
Learn how the cloud-native architecture of Hazelcast works with Kubernetes when deploying fast cloud applications.
This paper covers implementation details behind the Hazelcast credit value adjustment risk calculation solution.
This paper describes a modern architecture for calculating risk so banks can rethink their legacy systems and aspire for greater efficiency.
This video by Hazelcast senior solutions architect Sharath Sahadevan walks through a setup of WAN Replication on Google Cloud Platform.
Machine learning (ML) brings exciting new opportunities, but applying the technology in production workloads has been cumbersome, time consuming, and error prone. In parallel, data generation patterns have evolved, generating streams of discrete events that require high-speed processing at extremely low response latencies. Enabling these capabilities requires a scalable application of high-performance stream processing, distributed application of ML technology, and dynamically scalable hardware resources.
See how the distributed compute features of Hazelcast can be used to build a rule engine for low-latency, high-throughput transaction processing.
Mainframe computers are used at many companies today, but the need for more cost-effectiveness is forcing changes. A popular strategy, mainframe optimization, balances mainframe usage with in-memory computing closer to the application tier, reducing unnecessary MIPS. At the same time, it adds powerful new architectures related to cloud, microservices, and data streaming. An integration with […]
Read why you should use Hazelcast over the Red Hat Data Grid for your application acceleration and architecture modernization initiatives.
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