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Generative AI as an Accelerator for Data Engineering
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Author(s): N. Rajkumar (Alliance School of Advanced Computing, Alliance University, India), C. Viji (Alliance School of Advanced Computing, Alliance University, India), Balusamy Nachiappan (Prologis, USA), A. Mohanraj (Sri Eshwar College of Engineering, India), Judeson Antony Kovilpillai (Amity University, India)and Oswalt Manoj S. (Alliance School of Advance Computing, Alliance University, India)
Copyright: 2026
Pages: 26
Source title:
Generative AI-Powered Data Architectures: From Governance to Autonomous Analytics
Source Author(s)/Editor(s): Bahaa Eddine Elbaghazaoui (Sultan Moulay Slimane University, Morocco), Mohamed Amnai (Ibn Tofail University, Morocco)and Noreddine Gherabi (Sultan Moulay Slimane University, Morocco)
DOI: 10.4018/979-8-3373-5616-7.ch003
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Abstract
The exponential growth of data in the digital era has heightened the importance of efficient data engineering practices. Traditional approaches to data ingestion, cleaning, transformation, and governance often require significant manual intervention, domain expertise, and development time, leading to bottlenecks in analytics and decision-making. By leveraging natural language prompts, contextual learning, and synthetic data generation capabilities, GenAI can facilitate code generation for data pipelines, improve data quality, enhance metadata documentation, and support intelligent decision-making in real-time environments. This chapter explores the integration of Generative AI into data engineering workflows, providing a comprehensive review of relevant technologies, conceptual frameworks, industrial applications, and challenges. Several case studies highlight the tangible benefits of this synergy, including improved productivity, reduced operational costs, and higher data accuracy.
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