How do customers build an end-to-end IoT solution using commercial grade, open source products? This is the question we (Patrick Steiner, Maggie Hu and I) wanted to address with our session at the Red Hat Summit, Boston. The end-to-end solution is based on three-tier Enterprise IoT Architecture, which integrates IoT data with existing business processes and the human element.
Continue reading “Building a Secure IoT Solution: Summit 2017”
Most of the time, when we think about collecting, parsing and storing Logs, the first thing that pops in our mind is the ElasticStack or ELK. It is well positioned in developer and sysadmin’s minds. The stack combines the popular Elasticsearch, Logstash and Kibana projects together to easy the collection/aggregation, store, and visualization of application logs. As an Apache Camel rider and Infinispan enthusiast, I prepared this exercise to produce my own log collector and store stack using Red Hat’s products, JBoss Fuse and JBoss Data Grid, instead.
Continue reading “Implementing a Log Collector using Red Hat JBoss Fuse and Red Hat JBoss Data Grid”
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”