Announcing Red Hat JBoss Data Virtualization 6.3

We are excited to announce the release of Red Hat JBoss Data Virtualization 6.3.

Data integration has always been challenging. Modern technology trends like big data and cloud, coupled with the need for more real time data access and analysis, are adding to the complexity facing enterprises today.

JBoss Data Virtualization is a data access and integration solution that offers an alternative to physical data consolidation and data delivery by allowing flexibility and agility in data access. Data virtualization creates a single logical view of data from varied data sources, including transactional systems, relational databases, cloud data stores, and big data stores.

What’s New in JDV 6.3

JBoss Data Virtualization 6.3 adds capabilities to help organizations integrate big data and provide high performance data access in real time.  The release notes cover the full features and enhancements for JBoss Data Virtualization 6.3. There are a number of new features to enable expanded connectivity, enhanced security and developer productivity support.

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Announcing Red Hat JBoss Data Grid 7.0

Red Hat JBoss Data Grid 7.0 is here.

This significant new release offers a variety of features which are designed for complex, distributed, and high-volume, velocity data requirements. JBoss Data Grid can support both on-premise and cloud-based infrastructures, and it can handle the near-instantaneous, complex demands of Internet of Things and big data environments.

What It Brings

JBoss Data Grid can be deployed in different architectures, depending on the needs of your environment. In addition to the traditional usage as distributed cache, in-memory data grids can function as the primary data store for applications or as a compute grid for distributed computing.

Data Storage

There are two use cases for using JBoss Data Grid as a data store:

  • Data caching and transient storage. As an in-memory data store for frequently accessed application data or for transient data, such as shopping cart information or session data. This avoids hitting transactional backend systems are frequently, which reduces operating costs.
  • Primary data store. Data Grid can function as a key-value store similar to a NoSQL database. This can be the primary data source for applications for rapid retrieval of in-memory data and to persist data for recovery and archiving. Applications can run data-intensive operations like queries, transaction management, and distributed workloads.

Computing Grid

Modern architectures require flexible, distributed, and scalable memory and data storage. Using JBoss Data Grid as a distributed computing grid can help support the most demanding architectures:

  • Scale-out compute grid and event-driven computing. Through storage node clusters, JBoss Data Grid can do a distributed architecture with application logic at each node for faster data processing and lower latency and traffic. This architecture also supports event-driven computing by executing application logic at the node as data are updated.
  • Big data and the Internet of Things. JBoss Data Grid can support massive data streams — hundreds of thousands of updates per second. The Internet of Things can have data streams from thousands of connected devices, updating frequently. Clustering and scale, application logic and processing, and both in-memory and persistent storage in JBoss Data Grid enable those big data architectures by managing those massive data streams.

Real-Time Analytics and Performance for Digital Business

DIgital transformation means that organizations are pushing into a new intersection between their physical goods or services and online, on-demand applications. This digital environment is reliant on data — and unlike previous generations, this technology uses -near live data streams rather than historical data collections.

JBoss Data Grid is a leading high-performance, highly-scalable, in-memory data grid. In-memory data grids provide a means of scalable memory so that even rapidly changing application data can be processed.  Better data processing and management enables organizations to make fast, accurate decisions using large data streams. JBoss Data Grid 7.0 offers a data foundation for real time analytics:

  • Low latency data processing through memory and distributed parallel execution
  • Data partitioning and distribution across cluster nodes for horizontal scalability
  • High availability through data replication
  • Shared data services for real-time and in-memory analytics and event processing

A Short List of Major Features

The release notes cover the full features and enhancements for JBoss Data Grid 7.0. There are a number of features for improved ease of use, real-time analytics, and language support:

  • Distributed streams, which uses the Java 8 Stream API to take complex collections of data and run defined analytics operations.
  • Resilient distributed dataset (RDD) and DStream integration with Apache Spark 1.6, allowing Data Grid to be a data source for Spark and to execute Spark and Spark Streaming operations on data in Data Grid.
  • Hadoop InputFormat/OutputFormat integration, so that Hadoop tooling and oeprations can be used with data stored in Data Grid.
  • New administrative consoles for cluster management to simplify common tasks for managing the cache, nodes, and remote tasks.
  • Control operations for clusters including graceful shutdowns and startup and restores from persistent storage.
  • A new Node.js Hot Rod client to support using Data Grid as a NoSQL database with Node.js applications.
  • Running remote tasks (business logic) on a Data Grid server from the Java Hot Rod client.
  • Support for a Cassandra cache store, which persists the entries of a distributed cache on a shared Apache Cassandra instance.

Additional Resources

Messaging: The Underappreciated Element of Integration

Many people take integration messaging for granted, and many organizations assume that any messaging platform is as fully featured as any other. But in today’s increasingly connected world, with the emergence of major trends in consumer and enterprise technology like mobile, cloud computing, big data, and the Internet of Things, your organization needs to carefully review its messaging platforms and capabilities if you hope to continue to reliably serve your customers and deliver and maintain critical advantages over your competition.

To illustrate how vital and varied messaging platforms can be, let’s explore what exactly messaging is and compare several different approaches to meeting your organization’s messaging needs.

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Red Hat Updates JBoss BPM Suite and JBoss BRMS Products

We’re pleased to announce the launch of Red Hat JBoss BPM Suite 6.2 and Red Hat JBoss BRMS 6.2, our business process management (BPM) and business rules management platforms. These updates introduce a number of feature enhancements and new capabilities designed to infuse greater speed, quality, and control into the day-to-day creation and management of business rules and processes.

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How To Use Data Virtualization to Boost Business Results

Organizations across a range of industries are sitting on treasure troves of data. For the business intelligence team with the right tools, they can mine this data and unearth brilliant insights that could lead to the creation of new products and services and improve customer service and retention rates. With the possibility of such rich opportunities mere queries away, it’s no wonder that IT departments are increasingly concerned about data usage for analytics.

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Red Hat JBoss Data Grid 6.5 Now Available

We are pleased to announce the general availability of Red Hat JBoss Data Grid 6.5, the next version of our high performance in-memory data store, which introduces new capabilities and feature enhancements around overall product performance, remote data cache deployments, and deeper integration with other products in our middleware portfolio. In addition, version 6.5 adds support for the JCache caching API, JSR-107, in both Library mode and Client-Server mode.

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