Volume 16 Number 4 (Jul. 2021)
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JSW 2021 Vol.16(4): 174-181 ISSN: 1796-217X
doi: 10.17706/jsw.16.4.174-181

Architecting an Enterprise Data Lake, A Covid19 Case Study

Bushra1*, Mohsin Ali Memon2, Salahuddin Saddar3
1P.A.Q Construction Company, Hyderabad, Sindh, Pakistan.
2,3Mehran University of Engineering and Technology, Jamshoro, Sindh.


Abstract—Data is increasing at an enormous rate every day. Traditionally data has resided in silosacross any organization,so it’s difficult to have a complete picture for data driven business decision making. Data lake addresses the problem of rate of increase of data by providing “schema on read”, better integration and cheaper storage. It also solves the data silos problemby providing a central platform for a variety of data housing needs. However, implementing a data lake becomes challenging as the implementation needs to address the additional needs like metadata management, data discovery, data governance, data lifecycle management, security and centralized access controls mechanisms. This paper intends to provide a comprehensive architecture of data lake to address these challenges. We have also conducted and documented our experiments with publicly available datasets about COVID19 to validate the design and applicability of the proposed architecture for business analytics purposes.

Index Terms—Big data, data lake, data governance, data lake management, serverless architecture.

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Cite: Bushra, Mohsin Ali Memon, Salahuddin Saddar, "Architecting an Enterprise Data Lake, A Covid19 Case Study," Journal of Software vol. 16, no. 4, pp. 174-181, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)


 

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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