AI Driven Enhancement of ETL Workflows for Scalable and Efficient Cloud Data Engineering

Abstract

This article explores the role of AI in enhancing Extract, Transform, Load (ETL) workflows to improve scalability, efficiency, and performance in cloud data engineering. Traditional ETL processes face challenges such as high latency, resource inefficiencies, and complex transformations. AI-driven optimizations, including intelligent workload management, automated schema evolution, and anomaly detection, are revolutionizing data pipeline efficiency. This article delves into key AI-driven enhancements, implementation strategies, and real-world use cases demonstrating improved data processing and operational efficiency.