Red Hat JBoss Data Virtualization on OpenShift: Part 4 – Bringing data from outside to inside the PaaS

Welcome to part 4 of Red Hat JBoss Data Virtualization (JDV) running on OpenShift.

JDV is a lean, virtual data integration solution that unlocks trapped data and delivers it as easily consumable, unified, and actionable information. JDV makes data spread across physically diverse systems such as multiple databases, XML files, and Hadoop systems appear as a set of tables in a local database.

Continue reading “Red Hat JBoss Data Virtualization on OpenShift: Part 4 – Bringing data from outside to inside the PaaS”


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For more information about Red Hat OpenShift and other related topics, visit: OpenShift, OpenShift Online.

Running Spark Jobs On OpenShift

Introduction:

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”


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For more information about Red Hat OpenShift and other related topics, visit: OpenShift, OpenShift Online.

Red Hat JBoss Data Virtualization on OpenShift: Part 3 – Data federation

Welcome to part 3 of Red Hat JBoss Data Virtualization (JDV) running on OpenShift.

JDV is a lean, virtual data integration solution that unlocks trapped data and delivers it as easily consumable, unified, and actionable information. JDV makes data spread across physically diverse systems such as multiple databases, XML files, and Hadoop systems appear as a set of tables in a local database.

When deployed on OpenShift, JDV enables:

  1. Service enabling your data
  2. Bringing data from outside to inside the PaaS
  3. Breaking up monolithic data sources virtually for a microservices architecture

Together with the JDV for OpenShift image, we have made available several OpenShift templates that allow you to test and bootstrap JDV.

Continue reading “Red Hat JBoss Data Virtualization on OpenShift: Part 3 – Data federation”


Join Red Hat Developers, a developer program for you to learn, share, and code faster – and get access to Red Hat software for your development.  The developer program and software are both free!

 


For more information about Red Hat OpenShift and other related topics, visit: OpenShift, OpenShift Online.

Red Hat JBoss Data Virtualization on OpenShift: Part 1 – Getting started

Red Hat JBoss Data Virtualization (JDV) is a lean, virtual data integration solution that unlocks trapped data and delivers it as easily consumable, unified, and actionable information. JDV makes data spread across physically diverse systems such as multiple databases, XML files, and Hadoop systems appear as a set of tables in a local database.

When deployed on OpenShift, JDV enables:

  1. Service enabling your data
  2. Bringing data from outside to inside the PaaS
  3. Breaking up monolithic data sources virtually for a microservices architecture

Together with the JDV for OpenShift image, we have made available OpenShift templates that allow you to test and bootstrap JDV.

This article will demonstrate how to get started with JDV running on OpenShift. JDV is available as a containerized xPaaS image that is designed for use with OpenShift Enterprise 3.2 and later. We’ll be using the Red Hat Container Development Kit (CDK) to get started quickly.

The CDK provides a pre-built CDK based on Red Hat Enterprise Linux to help you develop container-based (sometimes called docker) applications quickly. The containers you build can be easily deployed on any Red Hat container host or platform, including: Red Hat Enterprise Linux, Red Hat Enterprise Linux Atomic Host, and our platform-as-a-service solution, OpenShift Enterprise 3.

Prerequisites

Continue reading “Red Hat JBoss Data Virtualization on OpenShift: Part 1 – Getting started”


Join Red Hat Developers, a developer program for you to learn, share, and code faster – and get access to Red Hat software for your development.  The developer program and software are both free!

 


For more information about Red Hat OpenShift and other related topics, visit: OpenShift, OpenShift Online.

Announcement: Red Hat JBoss Data Virtualization on OpenShift now available

We are happy to announce the availability of Red Hat JBoss Data Virtualization (JDV) 6.3 image running on OpenShift.

jdvose

JDV is a lean, virtual data integration solution that unlocks trapped data and delivers it as easily consumable, unified, and actionable information. JDV makes data spread across physically diverse systems such as multiple databases, XML files, and Hadoop systems appear as a set of tables in a local database.

When deployed on OpenShift, JDV enables:

Continue reading “Announcement: Red Hat JBoss Data Virtualization on OpenShift now available”


Join Red Hat Developers, a developer program for you to learn, share, and code faster – and get access to Red Hat software for your development.  The developer program and software are both free!

 


For more information about Red Hat OpenShift and other related topics, visit: OpenShift, OpenShift Online.

Unlock your Hadoop data with Hortonworks and Red Hat JBoss Data Virtualization

Welcome to this first episode of this series: “Unlock your [….] data with Red Hat JBoss Data Virtualization (JDV).”

This post will guide you through an example of connecting to a Hadoop source via the Hive2 driver, using Teiid Designer. In this example we will demonstrate connection to a local Hadoop source.  We’re using the Hortonworks 2.5 Sandbox running in Virtual Box for our source, but you can connect to another Hortonwork source if you wish using the same steps.

Hortonworks provides Hive JDBC and ODBC drivers that let you connect popular tools to query, analyze and visualize data stored within the Hortonworks Data Platform (HDP).

Note: we support HBase as well, stay tuned for an episode of Unlock your HBase data with Hortonworks and JDV.

Continue reading “Unlock your Hadoop data with Hortonworks and Red Hat JBoss Data Virtualization”


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A Mongo Shell Cheat Sheet

There’s a whole host of GUI tools to connect to MongoDB databases and browse, however despite a steeper learning curve, I’ve always found myself more productive using a command line interface (CLI).

Then, there’s that moment when something has gone wrong on the database server, and we need to SSH 4-levels deep in order to debug a problem with a database. Sometimes, there’s no other option available, and this makes familiarity with the CLI invaluable.

I learn best by example, and I’ve kept this cheat sheet of commands I use most often by my desk for many years – there’s always one command I’ve forgotten.

Together with the Red Hat Developer Team, I’ve put together this handy cheat sheet – hopefully, you’ll find it useful too!


@cianclarke is a Software Engineer with Red Hat Mobile. Primarily focused on the mobile-backend-as-a-service space, Cian is responsible for many of Red Hat’s mBaaS developer features.


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DevNation Live Blog: Building Reactive Applications with Node.js and Red Hat JBoss Data Grid

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.

JDG is capable of processing and storing real-time streams of data, while maintaining very fast response times. It does this by using the memory available from a dynamically scalable grid of machines. Galder described JDG as a four-in-one package capable of being:

  • a distributed cache.
  • a high performance NoSQL primary data store.
  • an event-driven data store, particularly for real time event processing.
  • a big data and Internet of Things (IoT) data store.

The three-tiered web app in the demo consisted of:

  • A web client written in Elm, which is a functional language that compiles to JavaScript.  It is statically typed, which the presenter feels leads to well architected code. Elm competes with platforms such as React and Angular. Any of those other platforms could be used, but Galder chose Elm for the live demo, particularly given the useful error messages the compiler generates as a virtue of using a statically typed language.
  • An event manager running on Node.js using Express.js.
  • JBoss Data Grid as the data store.  Three nodes were used, running on the same laptop. Each element was guaranteed to be stored in two nodes, providing redundancy for fail over.

Node.js based applications have become very popular. Many use JavaScript on all three tiers, including NoSQL data stores. However, most of those data stores can’t match the scalability and response times of JDG. Traditionally, developers have needed to use Java to take advantage of JBoss Data Grid. The new fully asynchronous Node.js interface to JBoss Data Grid should enable developers to build some truly interesting next-generation reactive applications.

You can download JBoss Data Grid from developers.redhat.com. If you’d like to get involved, join the open source community at infinispan.org.

 


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More about DevNation:  DevNation 2014 was our inaugural open source, polyglot conference for application developers and maintainers. View some of the DevNation 2015 session recordings here.  DevNation 2016 will be in San Francisco, USA, the week of June 26.  Be sure to follow its status and register at www.devnation.org.

REST and microservices – breaking down the monolith step by asynchronous step

A few days ago I had a rant about the misuse and misunderstanding of REST (typically HTTP) for microservices.

To summarize, a few people/groups have been suggesting that you cannot do asynchronous interactions with HTTP, and that as a result of using HTTP you cannot break down a monolithic application into more agile microservices. The fact that most people refer to REST when they really mean HTTP is also a source of personal frustration, because by this stage experienced people in our industry really should know the difference. If you’re unsure of the difference then check out the restcookbook or even Roy’s PhD thesis (it’s quite a good read!)

However, I digress, so back to the rant: My goal is to point people in the right direction and make some recommendations, hence this followup post.

Continue reading “REST and microservices – breaking down the monolith step by asynchronous step”


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Microservice principles and Immutability – demonstrated with Apache Spark and Cassandra

Shipping_containers_at_ClydeContainerizing things is particularly popular these days.   Today we’ll talk about the idioms we can use for containerization, and specifically play with apache spark and cassandra in our use case for creating easily deployed, immutable microservices.

 

Note: This post is done using centos7 as a base for the containers, but these same recipes will apply with RHEL and Fedora base images.

Continue reading “Microservice principles and Immutability – demonstrated with Apache Spark and Cassandra”


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