Strategic Data Management: Comparing Amazon Redshift and MongoDB

Authors

  • Rajesh Remala Author
  • Krishnamurty Raju Mudunuru Author

Abstract

In today's data-driven landscape, the rapid proliferation of digital information has made efficient data management a cornerstone of organizational success. As businesses across industries strive to harness the power of data analytics and real-time insights, selecting the right data management platform becomes imperative. Among the myriads of available technologies, Amazon Redshift and MongoDB have emerged as leading contenders, each offering unique strengths tailored to specific data workloads.

Amazon Redshift, a robust cloud-based data warehousing solution, is engineered to handle complex analytical queries at scale. Its architecture leverages columnar storage and massively parallel processing (MPP) to deliver high performance for structured data analytics. Redshift's separation of compute and storage resources provides flexibility in scaling, allowing organizations to efficiently manage large volumes of data while optimizing costs.

Conversely, MongoDB, a prominent NoSQL database, is designed for flexibility and scalability in managing unstructured and semi-structured data. With its document-oriented data model and distributed architecture, MongoDB excels in applications requiring rapid data ingestion and dynamic schema evolution. The platform's support for sharding and replication ensures high availability and horizontal scalability, making it an ideal choice for agile, data-driven applications.

This paper presents a comprehensive analysis of Amazon Redshift and MongoDB, examining their key features, architectural designs, and practical use cases. The study delves into critical aspects such as performance, scalability, consistency, and cost-effectiveness, providing a nuanced understanding of each platform's strengths and limitations. Additionally, it explores strategies for optimizing data management with Redshift and MongoDB, highlighting best practices for schema design, query optimization, and data loading processes.

By aligning their data management strategies with the capabilities of Redshift and MongoDB, businesses can unlock the full potential of their data assets, driving innovation and competitive advantage in today's dynamic business environment.

Downloads

Download data is not yet available.

References

E. S. Kumar, S. Kesavan, R. C. A. Naidu, S. Kumar R, and Latha, "Comprehensive Analysis of Cloud Based Databases," IOP Conf. Ser.: Mater. Sci. Eng., vol. 1131, no. 1, p. 012021, 2021. DOI: 10.1088/1757-899X/1131/1/012021.

Y. Li and S. Manoharan, "A performance comparison of SQL and NoSQL databases," in 2013 IEEE Pacific Rim Conf. Commun. Comput. Signal Process. (PACRIM), Victoria, BC, 2013, pp. 15-19.

S. Sakr, A. Liu, D. Batista, and M. Alomari, "A survey of large scale data management approaches in cloud environments," IEEE Commun. Surv. Tutorials, vol. 13, no. 3, pp. 311-336, 2011.

A. Gupta, S. Tyagi, N. Panwar, S. Sachdeva, and U. Saxena, "NoSQL databases: Critical analysis and comparison," in 2017 Int. Conf. Comput. Commun. Technol. Smart Nation (IC3TSN), Gurgaon, 2017, pp. 293-299. DOI: 10.1109/IC3TSN.2017.8284494.

K. Sahatqija, J. Ajdari, X. Zenuni, B. Raufi, and F. Ismaili, "Comparison between relational and NoSQL databases," in 2018 41st Int. Conv. Inf. Commun. Technol., Electron. Microelectron. (MIPRO), Opatija, 2018, pp. 0216-0221. DOI: 10.23919/MIPRO.2018.8400041.

M. A. Qureshi, J. Tahir, and I. Mehmood, "Comparative analysis of relational and NoSQL databases for IoT-based applications," Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, pp. 1-7, 2019.

K. Fraczek and M. Plechawska-Wojcik, "Comparative Analysis of Relational and Non-relational Databases in the Context of Performance in Web Applications," in 13th Int. Conf. Beyond Databases Architect. Struct. (BDAS 2017), 2017, pp. 153-164.

F. Haleemunnisa and W. Kumud, "Comparison of SQL NoSQL and NewSQL Databases for Internet of Things," in IEEE Bombay Sect. Symp., 2016, pp. 1-6.

K. B. Kumar, S. Sundhara, and S. Mohanavalli, "A performance comparison of document-oriented NoSQL databases," in 2017 Int. Conf. Comput. Commun. Signal Process. (ICCCSP), 2017.

J. C. Anderson, J. Lehnardt, and N. Slater, CouchDB: The Definitive Guide. O'Reilly Media, 2010.

Downloads

Published

2023-03-09

Issue

Section

Articles

How to Cite

Strategic Data Management: Comparing Amazon Redshift and MongoDB. (2023). International Numeric Journal of Machine Learning and Robots, 7(7), 1-10. https://injmr.com/index.php/fewfewf/article/view/145

Most read articles by the same author(s)

1 2 3 4 5 6 > >>