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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industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
Maintain a consistent customer experience and eliminate delays and downtime through Hazelcast Web Session Clustering.
Looking for DEVELOPER content on web session clustering?
Hazelcast.org | IMDG Open Source
Web and application servers can be made to scale out in order to handle huge loads by adding devices, such as a load balancer. This has a secondary effect of providing redundancy. However, for applications that use web sessions, if a server goes down and the load balancer moves the user to a new server, the session is lost, to the frustration of the user.
Hazelcast provides web session clustering, where user sessions are maintained in the Hazelcast IMDG cluster, using multiple copies for redundancy.
In the event of an application server failure, the load balancer redirects to a new application server that has access to the user session. The hand-off to the new application server is seamless for the user. This provides the highest level of customer experience.
As an in-memory solution, performance of web session clustering and replication is very high, and needs no alterations to your application tier. Perform web session clustering without modifying existing applications.
As your online business grows and customers increasingly use your website, Hazelcast can keep up with the load by adding new Hazelcast members to extend your cluster capacity.
Hazelcast IMDG is an in-memory data grid, so all data operations are handled in-memory. This gives users of your online business a significantly faster (and better!) user experience.
Configure Web Session Replication to use an existing cluster as a session store, so web servers will request session data from a central Hazelcast cluster, helping your development operations team save maintenance across your company.
Hazelcast is an open source, highly scalable, transactional, distributed/partitioned implementation of queue, map, set, list, lock and executor services for Java. Hazelcast is for you if you like to easily: share data/state among many servers (e.g. web session sharing), cache your data (distributed cache), cluster your application, partition your in-memory data, send/receive messages among applications, distribute workload onto many servers, take advantage of parallel processing or provide fail-safe data management.
In-memory data grids are already widely used for scaling web applications with caching, clustering and session replication. What is less known is that IMDGs have other great features that make them an excellent fit for modern web applications. This talk will explore the use of distributed data structures to push live data to the browser as soon as it updates. In particular we will focus on Entry Listeners and Continuous Query in combination with WebSockets to power a real-time web app.
This case study profiles a major US media company's use of in-memory technology to enable high-volume Machine Learning and Artificial Intelligence applications used to build out 360-degree customer profiles, in real-time, that are used to create chatbots to handle customer support inquiries.
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.