Most programs need data in order to work. Sometimes this data is provided to the program when it runs, and sometimes the data is built into the program. In this article, I’ll explain how to store large amounts of data inside a program so that it is there when the program runs.
Continue reading “How to store large amounts of data in a program”
Managing data reconciliation through a specific process is a common necessity for projects that require Digital Process Automation (formerly known as Business Process Management), and Red Hat Process Automation Manager helps to address such a requirement. This article provides good practices and a technique for satisfying data reconciliation in a structured and clean way.
Red Hat Process Automation Manager was formerly known as Red Hat JBoss BPM Suite, so it’s worth mentioning that jBPM is the upstream project that fuels Process Automation Manager. The blog post From BPM and business automation to digital automation platforms explains the reasons behind the new name and shares exciting news for this major release.
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In the following blog post, we will learn how to create and access federated views from a various data source using JBoss Data Virtualization.
This lab is from the JBoss Developer Guidebook/ch5 Exposing Data as service book (40% with discount code JBDG40 offered from October 1-31).
Continue reading “Tutorial: Building and consuming Virtual Microdatabase with JBoss Data Virtualization”
OpenShift Container Platform (OCP) offers many different types of persistent storage. Persistent storage ensures that data should be insistent between builds and container migrations. When choosing a persistent storage backend to ensure that the backend supports the scaling, speed, dynamic provisioning, RWX/RWO support and redundancy that the project requires. Container-Ready Storage (CRS), or native Gluster for OCP, is defined by the concept of persistent volumes, which are OCP created objects that allow storage to be defined and then used by pods to allow for data persistence.
Continue reading “Gluster for OpenShift – Part 1: Container-Ready Storage”
ELK (or Elastic stack) is the name for the Elasticsearch/Logstash/Kibana stack. Logstash gets log information, reports it to Elasticsearch for searching, and Kibana lets you analyze it. While the tools work independently, and with other software, they play together especially well. To understand what’s going on, let’s look at each one individually. This guide is meant to be a bit of a guided tour to each of these services.
Continue reading “ELK Exploration Companion”
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”
2016 was certainly an interesting year and, although we could probably discuss the election alone for an hour, there is one particular epidemic which has plagued the developer community in more ways than we probably care to mention. It seems as though even the best data encryption and reformatting of SSD’s is slowly becoming not enough when it comes to the continuous evolution of the hacker community and this is a pretty unsettling situation.
Continue reading “The Year of Data Breaches: Why Encryption and Reformatting SSD’s is Not Enough”