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Generative AI in Enterprise Data Engineering: Integrating Copilot for ETL Automation

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Generative AI in Enterprise Data Engineering: Integrating Copilot for ETL Automation


Mustafa Abbas Al-Khafaji | Huda Karim Al-Saedi



Mustafa Abbas Al-Khafaji | Huda Karim Al-Saedi "Generative AI in Enterprise Data Engineering: Integrating Copilot for ETL Automation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-4, June 2022, pp.2390-2395, URL: https://www.ijtsrd.com/papers/ijtsrd50069.pdf

As enterprises grapple with growing volumes and complexity of data, traditional extract transform load (ETL) processes are increasingly strained by scalability demands, evolving business requirements, and the need for rapid delivery of analytics-ready datasets. Conventional automation approaches address some inefficiencies but often fall short in adaptability and context-awareness. This paper explores the integration of generative AI specifically Copilot-style assistants into enterprise data engineering workflows to accelerate and enhance ETL automation. Generative AI introduces a paradigm shift by enabling natural language–driven pipeline generation, automated schema mapping, intelligent error handling, and adaptive optimization, thereby reducing manual intervention and development bottlenecks. Beyond productivity gains, AI-powered ETL fosters greater collaboration between technical engineers and business stakeholders, bridging skill gaps and democratizing data transformation tasks. Key considerations such as governance, data quality, security, and regulatory compliance are examined to ensure responsible deployment at scale. The proposed framework positions generative AI not merely as a coding assistant, but as a strategic enabler for modern data platforms, empowering enterprises to build more resilient, agile, and intelligent data engineering ecosystems.

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IJTSRD50069
Volume-6 | Issue-4, June 2022
2390-2395
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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