AI-Driven Fault Detection in Cloud-Based Data Engineering Architectures

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.