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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The in-memory speed you count on, with the convenience and scalability of cloud.
Update streaming jobs in Hazelcast Jet without data loss or interruptions.
Overview
Job Upgrade makes use of Jet state snapshots to address a variety of requirements, including modifications in business logic, bug fixes, and configuration changes.
Easy to Implement
Job upgrades can be triggered via a Java API or from a command line.
State snapshots of current jobs are taken and saved.
New classes or changes are distributed to the Jet nodes.
New versions are started, and data is read from the saved snapshot.
No Disruption to Customers
Everything listed above happens in milliseconds, with essentially no latency.
Allows A/B Testing on New Features
Multiple versions of a Jet Job can run concurrently, writing results to separated sinks.
After the testing period, pick the better version, and shut down the other.
Features
Introduce relevant filtering or transforming steps to streaming data-centric applications.
Change business rules or add new operators as the business logic driving your streaming applications evolves.
Change IP addresses for sources or sinks without disrupting streaming services.
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.
