Data Lakes: A Survey of Concepts and Architectures

Author:

Azzabi Sarah1ORCID,Alfughi Zakiya1,Ouda Abdelkader1ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Western University, London, ON N6A 5B9, Canada

Abstract

This paper presents a comprehensive literature review on the evolution of data-lake technology, with a particular focus on data-lake architectures. By systematically examining the existing body of research, we identify and classify the major types of data-lake architectures that have been proposed and implemented over time. The review highlights key trends in the development of data-lake architectures, identifies the primary challenges faced in their implementation, and discusses future directions for research and practice in this rapidly evolving field. We have developed diagrammatic representations to highlight the evolution of various architectures. These diagrams use consistent notations across all architectures to further enhance the comparative analysis of the different architectural components. We also explore the differences between data warehouses and data lakes. Our findings provide valuable insights for researchers and practitioners seeking to understand the current state of data-lake technology and its potential future trajectory.

Funder

Libyan Ministry of Higher Education and Scientific Research

Publisher

MDPI AG

Reference77 articles.

1. Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S.A., Montesano, N., Tariq, M.I., De-la Hoz-Franco, E., and De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. Advances in Intelligent Data Analysis and Applications, Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, Arad, Romania, 15–18 October 2019, Springer.

2. (2024, May 27). Data Growth Worldwide 2010–2025|Statista. Available online: https://www.statista.com/statistics/871513/worldwide-data-created/.

3. Critical analysis of Big Data challenges and analytical methods;Sivarajah;J. Bus. Res.,2017

4. John, T., and Misra, P. (2017). Data Lake for Enterprises, Packt Publishing Ltd.

5. LaPlante, A. (2016). Architecting Data Lakes, O’Reilly Media.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3