According to a recent announcement, Hazelcast, an in-memory computing platform company, has made available its Hazelcast Jet streaming engine. The company says the engine is the industry’s fastest stream processing engine and that it simplifies implementation for deployments.
Whether deployed in constrained environments like IoT sensors or in cloud-scale applications, Hazelcast Jet is designed to ingest, categorize, and process vast amounts of data with ultra-low latency to support continuous intelligence practices.
“SigmaStream specializes in high-frequency data and works with some of the world’s largest companies that operate in the most constrained environments. By integrating Hazelcast Jet’s high-performance streaming engine with our Hummingbird visualization and processing platform, we process high-frequency data from dozens of channels and address inefficiencies in real-time,” said Hari Koduru, CEO, SigmaStream. “The performance and optimization at such a fine level enable SigmaStream’s customers to shrink the time spent on a project, ultimately saving them millions of dollars.”
The compnay says that Hazelcast Jet will simplify deployment because it is a single, lightweight system that addresses a complex set of architectural requirements. Hazelcast Jet’s single-system design reportedly enables rapid time-to-value, eliminates costs and complexity associated with multi-component architectures, and reduces the need for multiple skill sets.
“Hazelcast has once again delivered a powerful leap forward for the industry, this time by radically simplifying how stream event processing is implemented,” said Kelly Herrell, CEO, Hazelcast. “Time is money, and the ability to process data at the moment it is generated — wherever it is generated — produces measurable business benefits whether at a financial trading desk or edge-based sensors. When time matters, companies choose Hazelcast and now they have a compelling and flexible streaming solution for fast data processing in Hazelcast Jet.”
Hazelcast Jet is Kubernetes-ready to support containerized workloads and validated to run in Pivotal Cloud Foundry and Red Hat OpenShift cloud environments.
Edited by Ken Briodagh