Hazelcast Debuts Accelerated Event Processing for IoT, Edge and Cloud

In-memory computing platform provider Hazelcast has announced the general availability of Hazelcast Jet. This embeddable application collects, categorizes, and processes high volumes of data with low latency to support continuous intelligence practices. Because this streaming engine doesn’t depend on external systems, it offers accelerated event processing for IoT, edge and cloud applications.

See also: How to apply machine learning to event processing

By integrating Hazelcast Jet’s high performance streaming engine with its Hummingbird visualization platform, SigmaStream has enabled its customers to process high-frequency data from many channels and address inefficiencies in real time. This process shrinks project time and potentially saves companies millions of dollars.

Single System Design

As a single, lightweight system, Hazelcast Jet simplifies the deployment process. It addresses and accommodates a complex set of architectural requirements. It eliminates costs, enables rapid time-to-value and reduces the need for multiple skill sets.

Industry’s Fastest Streaming

Hazelcast Jet’s distributed architecture and in-memory processing maintains millisecond speeds at scale and ultra-low latency, and the latency stays low regardless of scale

Run Anywhere

Its small footprint and architecture makes it lightweight, highly scalable and able to provide multiple deployment options, whether in Kubernetes microservices environments, private data centers, public clouds or embedded in applications. It is also Kubernetes-ready to support containerized workloads and validated to run in Pivotal Cloud Foundry and Red Hat OpenShift cloud environments.

Elastic and Resilient

Its clustering model can scale up or down without interruption or go offline without data loss. During an outage, in-memory data replication provides fault tolerance and fast recovery.

Machine Learning Modeling

Because processes Hazelcast Jet allows events upon ingestion, it’s ideal for machine learning models that need the latest information for decision making. It’s integrated with TensorFlow for real-time classification and prediction workloads at scale. Users can choose between embedded, in-process Java runner or remote TensorFlow options.

In-Memory Computing Platform

Combined with Hazelcast IMDG, it enables enterprises to deploy a scalable and high performance in-memory computing platform that can handle data in motion and at rest.

Hazelcast Jet 3.0 is available now.