research-article

A Comparative Study of MongoDB, ArangoDB and CouchDB for Big Data Storage

Authors:

Konstanitnos Mavrogiorgos,

Athanasios Kiourtis,

Argyro Mavrogiorgou,

Dimosthenis KyriazisAuthors Info & Claims

ICCBDC '21: Proceedings of the 2021 5th International Conference on Cloud and Big Data Computing

Pages 8 - 14

https://doi.org/10.1145/3481646.3481648

Published: 26 November 2021 Publication History

Abstract

A distinctive aspect of the current era is the ferocious amount of data that is generated and processed in a daily basis. There is no wonder that this epoch is generally characterized as the “Era of Big Data”. Thus, many enterprises and research initiatives strive to find a way to effectively and efficiently collect, store and analyze Big Data in order to improve their services and make efficient decisions. Those approaches refer to several domains such as healthcare, transportation, governance, or insurance. Towards this direction, in this paper we contribute into the selection of the most appropriate database for efficiently storing and retrieving Big Data. More specifically, taking into account the nature of Big Data and the main categories of databases that currently exist, three (3) NoSQL document-based databases were considered for this comparative study, namely the ArangoDB, the MongoDB and the CouchDB. The performance of these databases was measured based on specific metrics and criteria, including the total execution time for the same CRUD operations and their corresponding demands for resources, concluding to the most suitable database for storing Big Data.

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A Comparative Study of MongoDB, ArangoDB and CouchDB for Big Data Storage
1. Information systems
2. Software and its engineering

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ICCBDC '21: Proceedings of the 2021 5th International Conference on Cloud and Big Data Computing

August 2021

122 pages

ISBN:9781450390408

DOI:10.1145/3481646

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 November 2021

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Operational Program Competitiveness, Entrepreneurship and Innovation

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ICCBDC 2021

ICCBDC 2021: 2021 5th International Conference on Cloud and Big Data Computing

August 13 - 15, 2021

Liverpool, United Kingdom

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Chakraborty PBhunia S(2024)A Self-Aware Digital Memory Framework Powered by Artificial IntelligenceIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33758345:7(3579-3594)Online publication date: Jul-2024
https://doi.org/10.1109/TAI.2024.3375834
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