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Data Lake Architecture: A New Repository for Data Engineer

Data Lake Architecture: A New Repository for Data Engineer
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Author(s): Arvind Panwar (GGSIP University, Delhi, India) and Vishal Bhatnagar (Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi, India)
Copyright: 2020
Volume: 10
Issue: 1
Pages: 13
Source title: International Journal of Organizational and Collective Intelligence (IJOCI)
Editor(s)-in-Chief: Victor Chang (Teesside University, UK)
DOI: 10.4018/IJOCI.2020010104


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Data is the biggest asset after people for businesses, and it is a new driver of the world economy. The volume of data that enterprises gather every day is growing rapidly. This kind of rapid growth of data in terms of volume, variety, and velocity is known as Big Data. Big Data is a challenge for enterprises, and the biggest challenge is how to store Big Data. In the past and some organizations currently, data warehouses are used to store Big Data. Enterprise data warehouses work on the concept of schema-on-write but Big Data analytics want data storage which works on the schema-on-read concept. To fulfill market demand, researchers are working on a new data repository system for Big Data storage known as a data lake. The data lake is defined as a data landing area for raw data from many sources. There is some confusion and questions which must be answered about data lakes. The objective of this article is to reduce the confusion and address some question about data lakes with the help of architecture.

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