Reverse-Engineering the Design Rules for Cloud-Based Big Data Platforms
Document Type
Article
Source of Publication
Cloud Computing and Data Science
Publication Date
2-23-2022
Abstract
Big Data's 5 V complexities are making it increasingly difficult to develop an understanding of the end to end process. Big Data platforms play a crucial role in many critical systems, combining with Internet-of-Things, Artificial Intelligence and Business Analytics. It is both relevant and important to understand Big Data systems to identify the best tools that fit the requirements of heterogeneous platforms. The objective of this paper is to "discover" a set of design principles and rules for Cloud-based Big Data platforms for complex, heterogeneous environments. The design scope comprises Big Data's significance, challenges and architectural impacts. Using a methodology Reverse Engineered Design Science Research (REDSR), artifacts from leading vendors are used to elicit the design principles and rules with relevant details of Big Data components. We conclude that the findings are relevant and useful for DevOps architects and practitioners in operating complex, heterogeneous Cloud-based Big Data platforms.
DOI Link
ISSN
Volume
3
Issue
2
First Page
39
Last Page
59
Disciplines
Computer Sciences
Keywords
big data inter-operability, design specifications, heterogeneous cloud computing
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Sharma, Ravi S.; Mannava, Purna N.; and Wingreen, Stephen C., "Reverse-Engineering the Design Rules for Cloud-Based Big Data Platforms" (2022). All Works. 6431.
https://zuscholars.zu.ac.ae/works/6431
Indexed in Scopus
no
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series