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.
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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.
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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 […]
The Java 9 EA version is out and we can now see how to use sun.misc.Unsafe. I led the public campaign to retain access to it in Java 9 which was ultimately successful, leading to the amendments to JEP 260. So, … Continue reading →
Today, we are thrilled to announce the availability of Hazelcast Striim Hot Cache. This joint solution with Hazelcast’s in-memory data grid uses Striim’s Change Data Capture to solve the cache consistency problem. With Hazelcast Striim Hot Cache, you can reduce the latency of propagation of data from your backend database into your Hazelcast cache to […]
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