Browse Poster Wiki: 2022 Poster Award Winners, Awards Ceremony (Watch Recording from minute marker 1630), Posters by 2022 HPCC Systems Interns, Posters by Academic Partners, Poster Judges, About Virtual Judging, 2022 Poster Contest Home Page, Poster Contest Previous Years
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Amy Ma is a 12th Grade student attending Marjory Stoneman Douglas High School in Florida, USA. Amy joined the HPCC Systems Intern Program to work on a project that supports our Cloud Native platform. Read Amy's 2021 intern blog journal which includes a more in depth look of her work and view her 2021 Poster.
Amy returned to the HPCC Systems Intern Program for the second time in 2022 to complete a project involving the collecting dispersed information about data patterns and combining it into one document. This document will be added to our current suite of manuals which are available here on our website.
As well as the resources included here, read Amy's 2022 intern blog journal which includes a more in depth look of her 2022 HPCC Systems intern project.
Poster Abstract
Data Patterns is a library or ECL Bundle that provides data profiling and research tools for use by ECL programmers. Data Patterns was an existing feature of HPCC Systems, however it was never formally documented. The information about usage was documented in three separate files. The purpose of this project was to gather the information from the various files and consolidate them into a book accessible to users from the Documentation area of the HPCC Systems website The documentation includes the following sections about the usage and functions of Data Patterns:
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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
In this Video Recording, Amy Aryaman provides a tour and explanation of her his poster content.
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HPCC Systems local deployment in K3D clusters
Click on the poster for a larger image.
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