Design principles for hadoop-based platforms: A reverse-engineered design-science approach

Document Type

Article

Source of Publication

Journal of Management Information and Decision Sciences

Publication Date

1-27-2022

Abstract

Big data has revolutionised industry in many fields and has emerged as an integral component of the 4th industrial revolution. Many organisations adopt big data for making significant strategic decisions. However, due to the availability of multiple big data platforms and analytical tools, organisations are faced with the challenge of developing inter-operable platforms. The objective of this research is to identify design principles and rules that may be effectively used in heterogeneous distributions of Hadoop-based big data platforms for both development and operations (DevOps). The methodology adopted is a “reverse-engineered design science research” approach for extracting the design principles and rules from artefacts. Three big data platforms were evaluated using this approach in order derive ten design principles and associated rules that aid in the implementation of a big data platform with emphasis on inter-operability. This is the theoretical contribution of the research. A practical contribution is the general guidelines for a reverse-engineered, design science research approach that supports the derivation and validation of effective implementation rules.

ISSN

1532-5806

Volume

25

Issue

S2

Disciplines

Computer Sciences

Keywords

Big Data Analytics, Hadoop Platform, Design Science Research, Reverse Engineering

Indexed in Scopus

no

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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