Analytical Exploration of Transforming Data Engineering through Generative AI

Abstract

This research explores the challenges associated with generative AI in data engineering, such as computational costs, ethical concerns, and data privacy issues, while offering potential solutions. The study provides an in-depth framework for integrating generative AI into data engineering, emphasizing its capacity to reshape the future of data transformation. As data engineering rapidly evolves, the integration of generative AI technologies has sparked a paradigm shift. The paper, titled "Analytical Study on Revolutionizing Data Transformation with Generative AI in Data Engineering," delves into how generative AI can optimize data pipelines, improve data quality, and automate complex transformations. By utilizing advances in natural language processing (NLP) and deep learning, generative AI brings fresh solutions to data wrangling, schema mapping, and data augmentation. Through case studies, industry applications, and experimental findings, this study demonstrates how generative AI reduces manual labor, accelerates workflows, and enhances scalability in data engineering.