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.
Join this webinar on April 11th at 8:00 am PT / 11:00 am ET / 4:00 pm GMT 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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industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
Update streaming jobs in Hazelcast Jet without data loss or interruptions.
Job Upgrade makes use of Jet state snapshots to address a variety of requirements, including modifications in business logic, bug fixes, and configuration changes.
Easy to Implement
Job upgrades can be triggered via a Java API or from a command line.
State snapshots of current jobs are taken and saved.
New classes or changes are distributed to the Jet nodes.
New versions are started, and data is read from the saved snapshot.
No Disruption to Customers
Everything listed above happens in milliseconds, with essentially no latency.
Allows A/B Testing on New Features
Multiple versions of a Jet Job can run concurrently, writing results to separated sinks.
After the testing period, pick the better version, and shut down the other.
Introduce relevant filtering or transforming steps to streaming data-centric applications.
Change business rules or add new operators as the business logic driving your streaming applications evolves.
Change IP addresses for sources or sinks without disrupting streaming services.
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.
The streaming benchmark is intended to measure the latency overhead for a streaming system under different conditions such as message rate and window size. It compares Hazelcast Jet, Apache Flink, and Apache Spark Streaming. Summary of Findings Jet is the only system with a latency below one second across all benchmarks. Spark’s max latency exceeds […]
Microservices, as an architectural approach, has shown a great deal of benefit over the legacy style of monolithic single applications. Nevertheless, microservices are not without their drawbacks. The purpose of this white paper is to show the first steps for using Spring Boot and Hazelcast IMDG® and how they contribute to the microservices landscape, enhancing the benefits and alleviating some of the common downsides.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.