Hazelcast Debuts Accelerated Event Processing for IoT, Edge and Cloud

PinIt

Hazelcast Jet is a lightweight, scalable, real-time streaming engine for continuous event processing.

In-memory computing platform provider Hazelcast has announced the general availability of Hazelcast Jet, a streaming engine with no dependency on external systems, resulting in accelerated event processing for IoT, edge and cloud applications. Hazelcast Jet is able to collect, categorize and process high volumes of data with low latency to support continuous intelligence practices.

See also: How to apply machine learning to event processing

“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 of 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.”

Key features include:

Single System Design-Hazelcast Jet simplifies the deployment process by being a single and lightweight system that can address and accommodate a complex set of architectural requirements. This eliminates costs, enables rapid time-to-value and reduces the need for multiple skill sets.

Industry’s Fastest Streaming-Hazelcast Jet is able to maintain millisecond speeds at scale and ultra-low latency due to its distributed architecture and in-memory processing, 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 including 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 with no interruptions and can be taken offline with no data loss. If an outage occurs, in-memory data replication provides fault tolerance and fast recovery, and the in-memory data can also be persisted continuously for maintenance shutdowns.

Machine Learning Modeling-Hazelcast Jet allows events to be processed upon ingestion, making it idea for machine learning models that need the latest information for decision making. Its 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.

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

Leave a Reply