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Aryaman is a final year, B. Tech Data Science student completing his four-year degree course from NMIMS - Mukesh Patel School of Technology, Management and Engineering.  Aryaman joined the program to work on a HPCC Systems Architectural Enhancements project:  local deployment of HPCC Systems on a K3D cluster for ECL training. This deployment utilises a Docker Daemon, K3d, Helm & Kubectl. It also utilises a local storage that is mounted to the K3d-cluster and users can access ECL-Watch through localhost on a web-browser. ECL IDE can also be linked to this deployed HPCC Systems by designating the server as localhost.

Poster Abstract

Earlier HPCC Systems was having a pre-packaged Virtual Machine for usage with VirtualBox or Hyper-V to help new users experiment, learn more about HPCC Systems and utilise it for troubleshooting purposes. HPCC Systems has since moved to the cloud, which now supports several alternative local cloud environments such as Docker Desktop, Minikube, etc. The objective of this project was to deploy HPCC Systems in any standalone machine (Linux, Windows) which allows users to train and learn more about HPCC Systems. 

We are employing a local deployment procedure which is cost effective, since it removes dependency on cloud and its associated costs like maintenance etc. This deployment can use a local storage as well as an external storage as per user requirement. The user can also configure their HPCC Systems deployment on their own, through YAML configuration files. This is deployed through a k3d cluster on the user’s local system with the help open source/ free CLIs & software’s and thus reduces the overall cost for trainers, trainees, and other HPCC-Big Data practitioners and for the company itself. 

Presentation

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HPCC Systems local deployment in K3D clusters

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