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.
Certified native images from Hazelcast enable you to run IMDG Enterprise, Enterprise HD, and Jet Enterprise in the leading enterprise cloud-container environments.
Hazelcast Enterprise PaaS enables deployment of Hazelcast In-Memory Computing solutions in the leading enterprise cloud container environments.
IBM Cloud Private
Easy to deploy Hazelcast IMDG Enterprise and Management Center via IBM Cloud Private Catalog.
Seamless integration with IBM Websphere Liberty on IBM Cloud Private for Session Replication Use Cases.
Number one in-memory caching solution in microservices based deployments on IBM Cloud Private, built by IBM and Hazelcast joint effort.
Pivotal Cloud Foundry
Hazelcast IMDG for PCF is based on Hazelcast IMDG Enterprise, the leading in-memory data grid.
Hazelcast Jet for PCF is based on Hazelcast Jet Enterprise.
Ability to dynamically pass your Hazelcast configuration in JSON format while creating services.
Red Hat OpenShift
Hazelcast can be run inside OpenShift, benefiting from Kubernetes for discovery of members.
Architecture and Features
IBM Cloud Private is an application platform for developing and managing on-premises, containerized applications.
Cloud Foundry is an open source cloud platform as a service (PaaS) for building, deploying, running and scaling applications. PCF Tiles are standard approach to create on-demand services for Pivotal Cloud Foundry.
Simplifies deployment of Hazelcast IMDG Enterprise-based standalone infrastructure, as a certified Red Hat Enterprise Linux-based image.
This use case outlines how a logistics company has cut maintenance costs and drastically reduced the overhead of setting up new applications. Hence, time to market is shortened by streamlining the process of keeping the data model of the in-memory data grid in sync with the data sources.
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.
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.
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.
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