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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, enables lower mainframe costs due to the reduction in unnecessary MIPS. At the same time, it adds powerful new architectures related to cloud, microservices, and data streaming. An integration with IBM and Hazelcast […]
Read why you should use Hazelcast over the Red Hat Data Grid for your application acceleration and architecture modernization initiatives.
BNP Paribas S.A. is a French international banking group and one of the largest banks in the world by total assets.
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast as a Hibernate second level cache within Spring Boot
Sr. engineer Grzegorz Piowawarek walks through the integration of Hazelcast into a Quarkus application.
Join Hazelcast and Intel for this deep dive into how your organization can create a single, unified data plane that extends all the way from the myriad things at the edge of your business right into your corporate cloud or data center.
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