AI-Driven Data Engineering: Transforming Data Processing and Analytics Workflows

Abstract

The integration of Artificial Intelligence (AI) into data engineering represents a transformative 
shift in data processing and analytics workflows. This paper explores how AI-driven 
methodologies enhance the efficiency, accuracy, and scalability of data engineering processes. We 
present a comprehensive analysis of AI techniques applied to data ingestion, processing, and 
analytics, focusing on their impact on real-time decision-making, predictive modeling, and 
automated data management. Key applications in financial trading, healthcare monitoring, and 
manufacturing predictive maintenance illustrate the practical benefits of AI integration. Our 
findings reveal that AI-enhanced data pipelines significantly reduce latency, improve data quality, 
and enable more precise analytics. We discuss the methodologies employed, including machine 
learning models, natural language processing, and deep learning techniques, and their role in 
automating data workflows and generating actionable insights. This paper concludes by 
highlighting the future directions for AI-driven data engineering, emphasizing the need for further 
research to address challenges such as ethical considerations, model interpretability, and scalability 
in complex data environments.