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Hazelcast Brings Industry-Leading In-Memory Speed and Scalability to Intel Optane DC Persistent Memory
Hazelcast announced that its in-memory data grid features optimizations for Intel Optane DC Persistent Memory, which provides increased data density and more cost-efficient access to in-memory speeds. The latest update to Hazelcast IMDG also improves performance, scalability and reliability for mission-critical, time-sensitive workloads. 

"The embrace of ultra-low latency computing is quickly becoming a defining element for signaling whether an enterprise is a leader or laggard in this data-centric world," said Kelly Herrell, CEO of Hazelcast. "With support for Intel Optane, Hazelcast continues our industry leadership providing continued advancements for large enterprises to leverage in-memory computing to power business-critical applications at the lowest possible latency and accelerate their businesses."

Project Veyron Gaining Speed

In September 2019, Hazelcast and Intel announced Project Veyron, a co-engineering initiative to accelerate high-performance computing for real-time applications, artificial intelligence (AI) and internet of things (IoT) solutions. In collaboration with Intel, Hazelcast is introducing support for Intel Optane DC Persistent Memory, which enterprises can leverage as a more scalable and cost-effective alternative to RAM.

With certified support for Intel Optane DC Persistent Memory, Hazelcast is enabling customers to cost-effectively scale larger in-memory workloads. Compared to RAM, Intel Optane DC Persistent Memory provides higher data density, which when paired with Hazelcast allows organizations to run more workloads in-memory. As an example, payment processors can increase the number of fraud detection algorithms to further prevent illegitimate transactions, while also reducing the number of false positives. Furthermore, when deployed in persistence mode, Intel Optane DC Persistent Memory and Hazelcast provide advanced resiliency by enabling fast recovery upon temporary node outages.

Improved Performance and Reliability

The release of Hazelcast IMDG 4.0 also features numerous advancements in performance that allow enterprises to get the most value from their hardware investments. Among the updates is a re-engineered inter-client protocol that reduces network hops and results in a 30 percent increase in write throughput. Additionally, Hazelcast is speeding client-server connectivity and transport layer security (TLS) communications performance by 50 percent and 200 percent, respectively.

Earlier this year, Hazelcast introduced the industry's only CP capability in an in-memory data grid, which delivers the highest data correctness. This capability, which refers to the consistency and partition tolerance guarantees in the CAP Theorem for distributed systems, ensures a continuously consistent view of data even in the event of a computer or network failure. In the latest release, Hazelcast enhances the CP Subsystem by adding robust data persistence, so that CP values will be available on restart.

"Where competitive offerings fail at maintaining data correctness during an outage, let alone at scale, Hazelcast thrives," said Greg Luck, CTO of Hazelcast. "Our continued work in the area of data consistency and data safety continues to lead the industry and provides our customers with the confidence to deploy Hazelcast in the most aggressive environments."

Cloud-Native and Kubernetes Verified

True to its cloud-native heritage, Hazelcast IMDG is verified and validated on Kubernetes, the de facto standard for container orchestration of all major cloud providers. In this release, Hazelcast increases its industry-leading support with new auto-scaling functionality within Kubernetes. The result is that enterprises can now further customize how applications manage workload spikes by setting pre-defined metrics.

Availability and Resources

Hazelcast IMDG 4.0 is available now in beta, with general availability expected by the end of December.

Published Wednesday, November 06, 2019 7:01 AM by David Marshall
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