Big Data

Develop and test a Quarkus client on Red Hat CodeReady Containers with Red Hat Data Grid 8.0

Develop and test a Quarkus client on Red Hat CodeReady Containers with Red Hat Data Grid 8.0

This article is about my experience installing Red Hat Data Grid (RHDG) on Red Hat CodeReady Containers (CRC) so that I could set up a local environment to develop and test a Quarkus Infinispan client. I started by installing CodeReady Containers and then installed Red Hat Data Grid. I am also on a learning path for Quarkus, so my last step was to integrate the Quarkus Infinispan client into my new development environment.

Initially, I tried connecting the Quarkus client to my locally running instance of Data Grid. Later, I decided I wanted to create an environment where I could test and debug Data Grid on Red Hat OpenShift 4. I tried installing Data Grid on OpenShift 4 in a shared environment, but maintaining that environment was challenging. Through trial-and-error, I found that it was better to install Red Hat Data Grid on CodeReady Containers and use that for my local development and testing environment.

In this quick tutorial, I guide you through setting up a local environment to develop and test a Quarkus client—in this case, Quarkus Infinispan. The process consists of three steps:

  1. Install and run CodeReady Containers.
  2. Install Data Grid on CodeReady Containers.
  3. Integrate the Quarkus Infinispan client into the new development environment.

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Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

Open Data Hub 0.6.1: Bug fix release to smooth out redesign regressions

It is just a few short weeks since we released Open Data Hub (ODH) 0.6.0, bringing many changes to the underlying architecture and some new features. We found a few issues in this new version with the Kubeflow Operator and a few regressions that came in with the new JupyterHub updates. To make sure your experience with ODH 0.6 does not suffer because we wanted to release early, we offer a new (mostly) bugfix release: Open Data Hub 0.6.1.

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Open Data Hub 0.6 brings component updates and Kubeflow architecture

Open Data Hub 0.6 brings component updates and Kubeflow architecture

Open Data Hub (ODH) is a blueprint for building an AI-as-a-service platform on Red Hat’s Kubernetes-based OpenShift 4.x. Version 0.6 of Open Data Hub comes with significant changes to the overall architecture as well as component updates and additions. In this article, we explore these changes.

From Ansible Operator to Kustomize

If you follow the Open Data Hub project closely, you might be aware that we have been working on a major design change for a few weeks now. Since we started working closer with the Kubeflow community to get Kubeflow running on OpenShift, we decided to leverage Kubeflow as the Open Data Hub upstream and adopt its deployment tools—namely KFdef manifests and Kustomize—for deployment manifest customization.

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Red Hat Data Grid 8.0 brings new server architecture, improved REST API, and more

Red Hat Data Grid 8.0 brings new server architecture, improved REST API, and more

Red Hat Data Grid helps applications access, process, and analyze data at in-memory speed. Red Hat Data Grid 8.0 is included in the latest update to Red Hat Runtimes, providing a distributed in-memory, NoSQL datastore. This release includes a new Operator for handling complex applications, a new server architecture that reduces memory consumption and increases security, a faster API with new features, a new CLI, and compatibility with a variety of observability tools.

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JBoss Data Virtualization: Integrating with Impala on Cloudera

JBoss Data Virtualization: Integrating with Impala on Cloudera

Cloudera Impala is a tool to rapidly query Hadoop data in HBase or HDFS using SQL syntax.  You can use Red Hat JBoss Data Virtualization to query that same data via Impala to take advantage of its optimization. You can also combine that data with other data sources in real time.  The goal of this guide is to import data from a Cloudera Impala instance, manipulate it, and then expose that data as a data service.  This guide includes access to a repository with example scripts, creating a custom base and view model, exposing it as a data service, and finally consuming that data via REST. This is a peer article to Unlock Your Cloudera Data with Red Hat JBoss Data Virtualization.

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Unlock Your Cloudera Data with Red Hat JBoss Data Virtualization

Unlock Your Cloudera Data with Red Hat JBoss Data Virtualization

After Unlock your Hadoop data with Hortonworks and Red Hat JBoss Data Virtualization episode, let’s continue the journey with another “Apache Hadoop” 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.

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