Hazelcast IMDG is a clustered, in-memory data-grid that manages application data and distributes processing using in-memory storage and parallel execution for breakthrough application speed and scale. In this Quick Start Guide, learn what an in-memory data grid can be used for, how to do simple query operations with Hazelcast IMDG, what sharing means with Hazelcast, and more. This guide is intended for software architects and developers who are planning or building systems requiring distribute infrastructure for application scalability and performance.
This white paper, written by Java Champion Ben Evans, provides an introduction for architects and developers to Hazelcast®’s distributed computing technology.
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.
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Hazelcast IMDG® has been a popular choice with MuleSoft users to cache data in an external in-memory cluster. This is also a very easy way to pass data or share state from one service to another. This document provides a step-by-step guide on how to store, retrieve and share data between services using an external Hazelcast® cluster from multiple MuleSoft services.
Separating data from the integration layer while not compromising on functionality will prevent the overloading of MuleSoft with data. Hazelcast IMDG also provides a better capability to monitor and alert on storage and use of data. Hazelcast being a compute grid provides additional features to move data and compute intensive tasks out of MuleSoft to be executed on the Hazelcast cluster.
Overall, storing data outside of MuleSoft and in an external Hazelcast cluster will not only improve performance of MuleSoft but also will add additional computation and monitoring features.
This guide is intended for software architects who are managing or planning a MuleSoft ESB deployment at scale.