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.
Get a 30-day free trial.
Get started today with the industry’s leading in-memory computing platform.
The in-memory speed you count on, with the convenience and scalability of cloud.
Full cluster recovery from either planned shutdowns or cluster-wide crashes.
Overview
Hazelcast Jet’s Lossless Recovery feature provides advanced fault-tolerance support for high-volume, mission-critical applications
Automatic Replication and Persistence of Critical Data
Data is continuously snapshotted and backed up to the disk, allowing the seamless recovery from the cluster-wide failures and shutdowns.
Uninterrupted Performance for Customer-Facing Streaming Data
Snapshotting, replication and persisting automatically occur in milliseconds, with essentially no noticeable effect on user experience.
Features
Jet stores data in multiple replicas (copies) across the cluster, which are recovered from the back-up if a node fails.
Continuously persists the states of the cluster members on disk in a format specifically designed for restart performance and to work in concert with SSDs.
Consistent snapshots of each job are saved to storage, configured to be persistent with Hazelcast Hot Restart.
Rewindable sources are rewound using offsets saved in the snapshot (Kafka, Hazelcast IMap, Hazelcast ICache data sources support rewinding).
Resources
Rahul Gupta will show how easy it is to add a fast, highly efficient stream-processing engine to a distributed in-memory cache. You will see how to analyze data already in-memory, to pull data from outside and to push results elsewhere.
Come join us for the "Introduction to In-Memory Data Grids" NightClazz with Hazelcast and Zenika, in Lille, France. Neil Stevenson, Senior Solution Architect, Hazelcast will be discussing "What is an in-memory data grid? Is it the same as a cache? What can I use it for?"
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.
