Optimization of Data Engineering Processes Using AI

Abstract

This paper explores how Artificial Intelligence (AI) can optimize data engineering processes, offering a transformative approach to handling data at scale. From data collection to integration, AI introduces automation and intelligence that streamline workflows, enhance data quality, and enable faster data-driven insights. Key techniques, such as machine learning for data quality, natural language processing in data transformation, and predictive models for resource allocation, demonstrate AI's potential to improve efficiency and accuracy across data engineering workflows. This research evaluates the technical mechanisms, challenges, and future opportunities for AI-driven optimization in data engineering, with case studies and data-driven analyses that underline its efficacy.