Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
Greg Luck is a leading technology entrepreneur with more than 15 years of experience in high-performance in-memory computing. He is the founder and inventor of Ehcache, a widely used open source Java distributed cache that was acquired by Software AG (Terracotta) in 2009, where he served as CTO. Prior to that, Greg was the Chief Architect at Australian start-up Wotif.com that went public on the Australian Stock Exchange (ASX:WTF) in 2006. Greg is a current member of the Java Community Process (JCP) Executive Committee, and since 2007 has been the Specification Lead for JSR 107 (Java Specification Requests) JCACHE. Greg has a master's degree in Information Technology from Queensland University of Technology and a Bachelor of Commerce from the University of Queensland.
No posts were found matching that criteria.
I joined Hazelcast in January 2014 as employee number 12. At that time Hazelcast 3.1 was the current version. Since then, about every 7-8 months we have done a “minor” release, the latest of which is 3.12. Minor in the sense that they maintained binary compatibility. We added many features and improved many aspects of […]
Today, Hazelcast is moving to a three-license software licensing model: Apache 2.0 for the core of our systems, consistent with our history A new source-available Hazelcast Community License A proprietary license for selected Enterprise operational features, consistent with our history The license model change will have no impact on users and is only intended to […]
Introduction We all know that selecting the right technology for your business-critical systems is hard. You first have to decide what characteristics are most important to you, and then you need to identify the technologies that fit that profile. The problem is that you typically only get a superficial view of how technologies work, and […]
Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business – IoT sensor network data streams, mobile usage statistics, large scale monitoring, the list is endless. Numerous applications seek the ability to quickly react to dynamic streaming data, as it is either a mandatory requirement or a competitive advantage. API Churn As a consequence, lots […]
UPDATE (9/4/19): I posted a new blog that showcases how we believe Redis achieves the performance in its benchmarks. Due to its underlying architecture and many years of optimization, Hazelcast is extremely fast and dramatically outperforms Redis Labs (and Redis open source), especially at scale. Last year, Redis Labs published a very misleading benchmark against […]
Given the widely public reaction to the Commons Clause licensing change from another vendor, we felt it was important to let the community at large know that Hazelcast’s 10-year consistent stance on open-source licensing is not changing. In fact that was one of the first public questions posed by InfoQ to our new CEO, Kelly […]
Update 6 March 2019 Hazelcast 3.12 introduced a new CP Subsystem based on an In-Memory RAFT for the atomic data structures discussed in this blog. This is an ideal solution recommended by Kyle Kingsbury which we have now implemented. See Hazelcast IMDG 3.12 Introduces CP Subsystem. Jepsen Analysis Kyle Kingsbury (aka @aphyr) has prepared an […]
I am very excited to announce that after months of engineering work and assistance from Pivotal, Hazelcast Jet is now available as a Tile on Pivotal Cloud Foundry (PCF) – the first general purpose data processing platform available on PCF. As a member of the Cloud Foundry community we are determined to enhance the services […]
We recently conducted a second performance benchmark between Redis 3.2.8 cluster and a Hazelcast IMDG 3.8 cluster, following on from an earlier benchmark we did last year against 3.0.7. Check out the benchmark. Hazelcast Faster In summary, when comparing get performance, Hazelcast IMDG was up to 56% faster than Redis. For set performance, the Hazelcast […]
There are no more posts.