datagrid

Offload your database data into an in-memory data grid for fast processing made easy

Offload your database data into an in-memory data grid for fast processing made easy

An in-memory data grid is a distributed data management platform for application data that:

  • Uses memory (RAM) to store information for very fast, low-latency response time, and very high throughput.
  • Keeps copies of that information synchronized across multiple servers for continuous availability, information reliability, and linear scalability.
  • Can be used as distributed cache, NoSQL database, event broker, compute grid, and Apache Spark data store.

The technical advantages of an in-memory data grid (IMDGs) provide business benefits in the form of faster decision-making, greater productivity, and improved customer engagement and experience.

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External materialized views demystified in Red Hat JBoss Data Virtualization and Red Hat JBoss Data Grid

Red Hat JBoss Data Virtualization (JDV) provides several capabilities for caching data including: materialized views, result set caching, and code table caching. These techniques can be used to significantly improve performance in many situations.

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Announcing Red Hat JBoss Data Grid 7

We are very excited to announce General Availability (GA) of Red Hat JBoss Data Grid (JDG) 7!

JDG supercharges today’s modern applications and allows developers to meet tough requirements of high performance, availability, reliability, and elastic scale. JBoss Data Grid is compatible with the existing data tier as well as applications written in any language, using any framework and any platform via multiple APIs such as memcached, HotRod, and REST. Red Hat JBoss Data Grid empowers developers to obtain a streamlined approach to standing up new applications, avoiding the challenges normally associated with integrating applications and traditional databases.

JDG 7 introduces the following major new features:

Real-time Data Analytics

  • Distributed Streams
    JDG 7 introduces a distributed version of the Java 8 Stream API which enables you to perform rich analytics operations on data stored in JDG using the functional expressions available in the Stream API.
  • Apache Spark integration
    JDG 7 introduces a Resilient Distributed Dataset (RDD) and Discretized Stream (DStream) integration with Apache Spark version 1.6. This enables you to use JDG as a highly scalable, high-performance data source for Apache Spark, executing Spark and Spark Streaming operations on data stored in JDG.
  • Apache Hadoop Integration
    JDG 7 features a Hadoop InputFormat/OutputFormat integration, which enables use of JDG as a highly scalable, high performance data source for Hadoop. This enables use of tools from the Hadoop ecosystem which support InputFormat/OutputFormat for processing on data stored in JDG.
  • Remote Task Execution
    JDG 7 features the ability to execute tasks (business logic) on JDG Server from the Java Hot Rod client. The task can be expressed as a Java executable loaded on JDG Server or as stored JavaScript procedure which executes on the Java 8 (Nashorn) scripting engine on JDG Server.

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