This article introduces a novel framework for AI-driven cloud data engineering that addresses the growing challenges of scalable analytics in enterprise environments. The article presents an intelligent system architecture that leverages machine learning techniques to dynamically optimize extract, transform, and load (ETL) processes across distributed cloud infrastructures. The approach employs adaptive resource allocation, predictive scaling mechanisms, and metadata-driven processing to significantly enhance data pipeline efficiency while minimizing operational costs. The framework incorporates a self-tuning transformation engine that autonomously manages schema evolution and workload distribution based on historical performance patterns and real-time system metrics. Experimental evaluation across multiple industry scenarios demonstrates substantial improvements in processing throughput, resource utilization, and overall system reliability compared to traditional ETL methodologies. The proposed solution provides data engineers with an adaptive platform that evolves alongside changing data volumes and complexity, offering a promising direction for next-generation enterprise data architectures.
Keywords: Artificial Intelligence, Cloud Computing, ETL optimization, data pipeline automation, scalable analytics
