Chapter 1. Introduction

The primary use for Red Hat Data Grid is to provide a fast in-memory cache of frequently accessed data. Suppose you have a slow data source (database, web service, text file, etc): you could load some or all of that data in memory so that it’s just a memory access away from your code. Using Red Hat Data Grid is better than using a simple ConcurrentHashMap, since it has additional useful features such as expiration and eviction.

If your data doesn’t fit in a single node, or you want to invalidate entries across multiple instances of your application, Red Hat Data Grid can scale horizontally to several hundred nodes.

If you need to make your application cluster-aware, integrate Red Hat Data Grid and get access to features like topology change notifications, cluster communication and clustered execution.

If you want to be able to scale your caching layer independently from your application, or you need to make your data available to different applications, possibly even using different languages / platforms, use Red Hat Data Grid Server and its various clients.

Data you place in Red Hat Data Grid doesn’t have to be temporary: use Red Hat Data Grid as your primary store and use its powerful features such as transactions, notifications, queries, distributed execution, distributed streams, analytics to process data quickly.

Red Hat Data Grid supports replication between clusters, allowing you to backup your data across geographically remote sites.