Hugo Hiden

I originally did a Chemical Engineering undergraduate degree at Newcastle University before completing a PhD on the use of Artificial Intelligence and Machine Learning for modelling and monitoring chemical process plants. This led me away from Chemical Engineering towards software development with a particular focus on data intensive applications. After a spell of six years in industry creating data analysis systems for high throughput chemical laboratories, I moved back to Newcastle University to work in the newly formed e-Science Centre. This group was involved in a number of projects across the University making use of Grid systems. This research group eventually became the Digital Institute. Throughout this time, I have also maintained an active development role and, along with some University colleagues, developed the e-Science Central product (www.esciencecentral.co.uk) which is a Cloud based data processing and analytics platform. This has been used in a number of research projects (typically in the Medical School) and also formed the basis for the company that a few of us formed (Inkspot.co) which sold the e-Science Central platform into industry (most notably to Unilever for their connected consumer devices programs). This background has led my interest in IoT type applications, streaming data ingest, visualisation and machine learning.


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EventFlow: Event-driven microservices on Red Hat OpenShift (Part 2)

Learn how to deploy the EventFlow management platform on Red Hat OpenShift, install a set of sample processors, and build a flow.

Article

EventFlow: Event-driven microservices on OpenShift (Part 1)

This post is the first in a series that describes a lightweight cloud-native distributed microservices framework called EventFlow that targets the Kubernetes/OpenShift platforms and models event-processing applications as a connected flow or stream of components. EventFlow can be used to develop event-processing applications that can process CloudEvents, which are an effort to standardise upon a data format for exchanging information regarding events generated by cloud platforms.

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Smart-Meter Data Processing Using Apache Kafka on OpenShift

Learn how to process and aggregate huge streams of IoT data using Strimzi and Apache Kafka on Red Hat OpenShift. The data stream is processed using the Red Hat AMQ distributed streaming platform to perform aggregations in real time as data is ingested into the application.