Offload your database data into an in-memory data grid for fast processing made easy

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”

Share

External materialized views demystified in Red Hat JBoss Data Virtualization and Red Hat JBoss Data Grid

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”

Share

Unlock your Red Hat JBoss Data Grid data with Red Hat JBoss Data Virtualization

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”

Share

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”

Share

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”

Share

Red Hat JBoss Data Virtualization on OpenShift: Part 2 – Service enable your data

Welcome to the part 2 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 OpenShift templates that allow you to test and bootstrap JDV.

Introduction

In part 1 we described how to get started with JDV running on OpenShift. During the build phase of the pod several artifacts were downloaded from the provided GitHub URL in the JDV OpenShift template. We deployed two virtual databases (VDBs) called country-ws (external web service-based datasource) and marketdata-file (file-based datasource).

Continue reading “Red Hat JBoss Data Virtualization on OpenShift: Part 2 – Service enable your data”

Share