Analytical Study on Revolutionizing Data Transformation with Generative AI in Data Engineering

Abstract

The rapid evolution of data engineering has witnessed a paradigm shift with the integration of generative artificial intelligence (AI) technologies. This paper, "Analytical Study on Revolutionizing Data Transformation with Generative AI in Data Engineering," explores the transformative potential of generative AI in optimizing data pipelines, enhancing data quality, and automating complex transformations. Generative AI, leveraging advancements in natural language processing (NLP) and deep learning, introduces innovative approaches to data wrangling, schema mapping, and augmentation. By analyzing case studies, industry applications, and experimental results, this study highlights how generative AI reduces manual intervention, accelerates workflows, and fosters scalability in data engineering processes. The research further examines the challenges, including computational costs, ethical concerns, and data privacy implications, while proposing solutions to address them. This analytical exploration aims to provide a comprehensive framework for integrating generative AI into data engineering, underscoring its potential to redefine the future of data transformation.