Hazelcast Cloud is an enterprise-grade in-memory computing platform deployed and managed by the Hazelcast CloudOps team. The service is powered by Hazelcast IMDG Enterprise HD and leverages widely adopted technologies, such as Docker and Kubernetes, to provide dynamic orchestration and containerization. Hazelcast Cloud supports applications developed in some of the most common languages, including Java, Node.js, Python. Go, and .NET.
Hazelcast Cloud delivers enterprise-grade Hazelcast software in the cloud, deployed as a fully managed service. Leveraging over a decade of experience and best practices, Hazelcast Cloud delivers a high-throughput, low-latency service that scales to your needs while remaining simple to deploy. If you’re considering moving to the Cloud, or are looking for an easy ramp on deploying in-memory technology, this white paper on migrating in-memory to the cloud is an informative and helpful resource.
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!
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Ensar’s primary areas of interest are distributed data, replication, consistency, and storage. He has more than seven years of hands-on expertise in designing, developing, and testing distributed algorithms, with solid experience in concurrency. He has authored a number of articles on distributed data and stream processing, and is a frequent speaker at industry conferences on topics such as replication and distributed systems. Several of his talks can be found on YouTube, including Replication Distilled, Distributed Systems for Mere Mortals, and Replication in the Wild. Ensar is a Ph.D. candidate in computer science at Bilkent University in Ankara, Turkey.
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At Hazelcast we take reliability very seriously. With the new CP Subsystem module, Hazelcast has become the first and only IMDG that offers a linearizable distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. In addition to well-grounded designs and proven algorithms, reliability also requires a substantial amount of testing. We […]
“Distributed locks aren’t real”, some like to remind us. “Anyone who’s trying to sell you a distributed lock is selling you sawdust and lies.” This may sound rather bleak, but it doesn’t say that locking itself is impossible in a distributed system: it’s just that all of the system’s components must participate in the protocol. […]
The CP Subsystem of Hazelcast IDMG 3.12 offers a new linearizable implementation of Hazelcast’s concurrency APIs on top of the Raft consensus algorithm. These implementations live alongside AP data structures in the same Hazelcast IMDG cluster (new BFFs, yay!). You can store large data sets on hundreds of Hazelcast members and coordinate your operations using […]
We put Java locks on steroids! Hazelcast is now the first and only In-Memory Data Grid (IMDG) to offer a linearizable and distributed implementation of the Java concurrency primitives backed by the Raft consensus algorithm. Sounds interesting? Just keep calm and carry on reading… Hazelcast IMDG has been offering a set of concurrency APIs for […]
In Hazelcast Jet 0.5, we introduced fault tolerance for streaming computations. Hazelcast Jet periodically takes snapshots of the state of a running job and stores these snapshots in Hazelcast IMaps. In case of a failure, the job is restarted from the last successful snapshot. Hazelcast Jet 0.6 uses the same snapshotting mechanism to enable dynamic […]
Stream processing is a paradigm for on-the-fly processing of unbounded data feeds. We have been witnessing that stream processing engines (SPEs) get more attention every day in the era of fast data and become a fundamental component of data processing pipelines. They usually run in distributed settings to be able to cope with the flood […]
Stream processing is an emerging computational paradigm for on-the-fly processing of live data feeds, targeting low latency and high throughput. Streaming applications are usually deployed on multiple servers to achieve these requirements. Since even a single failure may lead to incorrect results or long interruptions in result delivery, fault tolerance is of paramount importance in […]
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