Year: 2020
Type: article
Abstract: Business intelligence, translated as data processing and analysis results for company decision-making, is integral to the modern business environment. DataOps and Agile Data Engineering cover a model concerning the automation and making of strong data workflows reaching maximum reliability and scalability. This paper focuses on using DataOps in data engineering and how it aids in integrating agile methodologies to aid decision-making. Some of the major factors which need to be addressed include: data management, data quality and pipeline management challenges. This paper explains how DataOps builds up the idea of data engineering using comparison with DevOps in detail. In addition, there is a case of practising real-time analytics that provides insight into how the usage of these methodologies occurs. In the last section, the authors have pointed out the set of recommendations, possible future trends in using DataOps, and its possibilities to bring transformation in a world where data are increasingly becoming important
Author: Rahul Cherekar
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4411610555