This article describes how to run a client-server application for JBoss Data Grid on Openshift using Red Hat Container Development Kit 3.0 Beta and Minishift. This environment for this tutorial can be set up quickly following up this previous post on the Developer Blog.
Continue reading “Using JBoss DataGrid in Openshift PaaS”
We’re excited to announce the availability of Red Hat JBoss Data Grid (JDG) Version 7.1.
Thanks and congratulations to the JDG engineering and product management team for this release.
JDG 7.1 release focuses on the following areas:
- Performance enhancements
- Apache Spark 2.x integration
- Several other enhancements
Continue reading “What’s new in Red Hat JBoss Data Grid 7.1”
Expanding on Tristan’s blog, where he spoke of enabling security for JBoss Data Grid caches, in this post we will cover how to add LDAP based security to the JDG caches. The principles and techniques remain defined by Tristan, but there are some minor changes that I will be highlighting in this blog for a successful working configuration of JDG enabled with LDAP security.
Continue reading “Enabling LDAP Security for DataGrid Cache”
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.
Continue reading “Offload your database data into an in-memory data grid for fast processing made easy”
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.
Continue reading “External materialized views demystified in Red Hat JBoss Data Virtualization and Red Hat JBoss Data Grid”
Welcome to another episode of the series: “Unlock your Red Hat JBoss Data Grid (JDG) data with Red Hat JBoss Data Virtualization (JDV).”
This post will guide you through an example of connecting to Red Hat JBoss Data Grid data source, using Teiid Designer. In this example, we will demonstrate connecting to a local JDG data source. We’re using the JDG 6.6.1, but you can connect to any local or remote JDG source (version 6.6.1) if you wish, using the same steps.
Continue reading “Unlock your Red Hat JBoss Data Grid data with Red Hat JBoss Data Virtualization”
A feature of OpenShift is jobs and today I will be explaining how you can use jobs to run your spark machine, learning data science applications against Spark running on OpenShift. You can run jobs as a batch or scheduled, which provides cron like functionality. If jobs fail, by default OpenShift will retry the job creation again. At the end of this article, I have a video demonstration of running spark jobs from OpenShift templates against Spark running on OpenShift v3.
Continue reading “Running Spark Jobs On OpenShift”
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
Continue reading “Announcing Red Hat JBoss Data Grid 7”
At DevNation, Red Hat’s Galder Zamarreño gave a talk with a live demo, Building reactive applications with Node.js and Red Hat JBoss Data Grid. The demo consisted of building an event-based three tier web application using JBoss Data Grid (JDG) as the data layer, an event manager running on Node.js, and a web client. Recently, support for Node.js clients was added to JDG, opening up the performance of a horizontally scalable in-memory data grid, to reactive web and mobile applications.
Continue reading DevNation Live Blog: Building Reactive Applications with Node.js and Red Hat JBoss Data Grid
DevNation sneak peek is a behind-the-scenes preview of sessions and information that will take place at DevNation 2016. Sign up for DevNation at www.devnation.org. Learn more. Code more. Share more. Join the Nation.
Continue reading DevNation 2016: Galder Zamarreno on “Building reactive applications with Node.js and Red Hat JBoss Data Grid”