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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The in-memory speed you count on, with the convenience and scalability of cloud.
In this webinar, we will present the tools that Hazelcast Jet brings to the table when it comes to operating long-running streaming applications in the cloud. Can’t attend the live times? You should still register! We’ll be sending out the recording after the webinar to all registrants.
Hazelcast Jet® is a 3rd generation stream processing engine that adds advanced data processing capabilities to Hazelcast IMDG®. Jet makes it simple to build distributed, fault-tolerant data processing pipelines on top of Hazelcast IMDG and provides 500% performance increase over similar processing done with Apache Spark. Just like Hazelcast®, it can be embedded into your application or run as standalone.
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The goal of streaming systems is to process big data volumes and provide useful insights into the data prior to saving it to long-term storage. The traditional approach to processing data at scale is batching; the premise of which is that all the data is available in the system of record before the processing starts. In the case of failures the whole job can be simply restarted. While quite simple and robust, the batching approach clearly introduces a large latency between gathering the data and being ready to act upon it. The goal of stream processing is to overcome this latency. It processes the live, raw data immediately as it arrives and meets the challenges of incremental processing, scalability and fault tolerance.
This white paper provides a point by point comparison of Hazelcast IMDG and Apache Ignite/GridGain across a number of dimensions.
Background The purpose of this benchmark is to reproduce the benchmark conducted by RedisLabs in 2018, correcting for deficiencies in their benchmark. The things we changed were: We used Java clients for both. The original benchmark used C++ with Redis. We added Redis with the Lettuce client to RadarGun We used the same benchmarking tool […]
Hazelcast offers a handy productivity tool from Speedment that automates database integration. It streamlines the development of Hazelcast applications by generating a Java domain model representation (POJOs and more) of the database, allowing companies to be productive with Hazelcast in no time.
When your customers and business processes demand real-time interactions and high-speed data streaming from a broad range of sources, the Hazelcast in-memory computing platform (IMCP) can deliver a comprehensive and integrated solution that is relied on by the most demanding companies in the world.
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