Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning. These applications are built around efficient data abstractions and operators that suit the applications of different domains. Often lack of a clear definition of data structures and operators in the field has led to other implementations that do not work well together. The HPTMT architecture that we proposed recently, identifies a set of data structures, operators, and an execution model for creating rich data applications that links all aspects of data engineering and data science together efficiently. This paper elaborates and illustrates this architecture using an end-to-end application with deep learning and data engineering parts working together.
@article{arxiv.2108.06001,
title = {HPTMT Parallel Operators for High Performance Data Science & Data Engineering},
author = {Vibhatha Abeykoon and Supun Kamburugamuve and Chathura Widanage and Niranda Perera and Ahmet Uyar and Thejaka Amila Kanewala and Gregor von Laszewski and Geoffrey Fox},
journal= {arXiv preprint arXiv:2108.06001},
year = {2021}
}