Journal:An overview of data warehouse and data lake in modern enterprise data management
| Full article title | An overview of data warehouse and data lake in modern enterprise data management |
|---|---|
| Journal | Big Data and Cognitive Computing |
| Author(s) | Nambiar, Athira; Mundra, Divyansh |
| Author affiliation(s) | SRM Institute of Science and Technology |
| Primary contact | Email: athiram at srmist dot edu dot in |
| Year published | 2022 |
| Volume and issue | 6(4) |
| Article # | 132 |
| DOI | 10.3390/bdcc6040132 |
| ISSN | 2504-2289 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.mdpi.com/2504-2289/6/4/132 |
| Download | https://www.mdpi.com/2504-2289/6/4/132/pdf (PDF) |
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This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed. |
Abstract
Data is the lifeblood of any organization. In today’s world, organizations recognize the vital role of data in modern business intelligence systems for making meaningful decisions and staying competitive in the field. Efficient and optimal data analytics provides a competitive edge to its performance and services. Major organizations generate, collect, and process vast amounts of data, falling under the category of "big data." Managing and analyzing the sheer volume and variety of big data is a cumbersome process. At the same time, proper utilization of the vast collection of an organization’s information can generate meaningful insights into business tactics. In this regard, two of the more popular data management systems in the area of big data analytics—the data warehouse and data lake—act as platforms to accumulate the big data generated and used by organizations. Although seemingly similar, both of them differ in terms of their characteristics and applications.
This article presents a detailed overview of the roles of data warehouses and data lakes in modern enterprise data management. We detail the definitions, characteristics, and related works for the respective data management frameworks. Furthermore, we explain the architecture and design considerations of the current state of the art. Finally, we provide a perspective on the challenges and promising research directions for the future.
Keywords: big data, data warehousing, data lake, enterprise data management, OLAP, ETL tools, metadata, cloud computing, internet of things
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Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
