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.

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Data Encapsulation vs. Immutability in Javascript

A while ago, I wrote a fairly long post attempting to shed some light on a few things you can do in your JavaScript classes to enforce the concept of data encapsulation – or data “hiding”. But as soon as I posted it, I got some flak from a friend who is a Clojure programmer. His first comment about the article was this.

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Automate integration CI/CD process

Red Hat Fuse Integration Service 2.0 tech preview was released a few weeks ago and as it’s based on Red Hat OpenShift 3.3, which has pipeline capability on top of it (tech preview on OpenShift as well), you are able to get one step closer to a more automated and agile continuous integration. As well as, a deployment one-stop platform for us, the integration developer.

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Unlock your PostgreSQL data with Red Hat JBoss Data Virtualization

And here we go for another episode of the series: “Unlock your [….] data with Red Hat JBoss Data Virtualization.” Through this blog series, we will look at how to connect Red Hat JBoss Data Virtualization (JDV) to different and heterogeneous data sources.

JDV is a lean, virtual data integration solution that unlocks trapped data and delivers it as easily consumable, unified, and actionable information. It 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. By providing the following functionality, JDV enables agile data use:

  1. Connect: Access data from multiple, heterogeneous data sources.
  2. Compose: Easily combine and transform data into reusable, business-friendly virtual data models and views.
  3. Consume: Makes unified data easily consumable through open standards interfaces.

It hides complexities, like the true locations of data or the mechanisms required to access or merge it. Data becomes easier for developers and users to work with. This post will guide you step-by-step on how to connect JDV to a PostgreSQL database using Teiid Designer. We will connect to a PostgreSQL database using the PostgreSQL JDBC driver.

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Welcome to Red Hat Enterprise Linux, MSBuild, a build tool for .NET Core CLI!

Microsoft announced the first “alpha” release of the new MSBuild-based .NET Core tools. .NET Core SDK which can be downloaded from the Red Hat Developer Program site consists of .NET Core Runtime and .NET Core command line tools (.NET Core CLI). (Reminder – you must have a Red Hat Enterprise Linux subscription first.  If you don’t, you can go here for a no-cost subscription.) The MSBuild tool is included in .NET Core 1.0 preview 3 (not in the latest release .NET Core 1.1). The version number is something complicated because .NET CLI is not GA but still under preview. The MSBuild tool can be used with both .NET Core 1.0 and .NET Core 1.1 runtimes. RHEL is not listed in the .NET Core SDK 1.0 Preview 3 download list. But you can try MSBuild with the .NET Core CLI daily build.

NOTE: Red Hat has just released .NET Core 1.1. However, .NET Core 1.1 doesn’t include the MSBuild tool, you can try MSBuild following this blog.

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