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.
Drive resilience for Hazelcast IMDG by keeping multiple Hazelcast IMDG clusters in sync on multi-site and multi-cloud deployments worldwide.
Overview
Imagine you have different data centers in New York, London and Tokyo each running an independent Hazelcast cluster. Every cluster is operating at native speed in its own LAN (Local Area Network), but you also want some or all record sets in these clusters to be replicated to each other – updates in the Tokyo cluster should also replicate to London and New York, and updates in the New York cluster are to be synchronized to the Tokyo and London clusters.
Hazelcast WAN Replication allows you to keep multiple Hazelcast clusters in sync by replicating their state over WAN environments, such as the internet.
Architecture
Hazelcast WAN Replication supports data recovery in both Active-Passive or One-way mode, enables geographic locality in Active-Active or Two-way mode and provides ease of use for test / development environment set-up.
Hazelcast WAN Replication provides support for Hazelcast Map (IMap): PutIfAbsent, HigherHits, PassThrough, LatestUpdate, as well as Hazelcast JCache (ICache): HigherHits, and PassThrough.
Hazelcast WAN Replication delivers default synchronization, sending all data to a target cluster to align the state of the target with the source, as well as Delta Synchronization using Merkle Trees, which synchronize only the different entries, instead of sending all entries.
Hazelcast WAN Replication includes WanBatchReplication, which sends replication events to the target cluster after a pre-defined number of replication events or a pre-defined amount of time, as well as SolaceWanPublisher, allowing WAN Replication users to use Solace.
Resources
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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