Optimizing ETL Pipelines with AI: A Framework for Intelligent Data Integration

This paper presents a comprehensive framework for optimizing Extract, Transform, Load (ETL) pipelines using artificial intelligence (AI) techniques such as reinforcement learning and incremental learning. The proposed AI-driven ETL pipeline dynamically adjusts data extraction, transformation, and loading processes, leading to improved performance in data integration. Experiments conducted using a financial transactions dataset demonstrated significant improvements in key metrics, including a 48 percent reduction in latency, a 78 percent increase in throughput, and a five to six percent improvement in accuracy compared to a traditional ETL system. These results highlight AI’s capability to provide scalable, efficient, and accurate data integration solutions for modern big data environments While the individual techniques used in this work-reinforcement learning and incremental learning-are well-established, their unified application within a dynamic, real-time ETL optimization framework presents a novel contribution. The system redefines pipeline execution as a sequential decision-making problem, enabling adaptive data integration in latency-sensitive environments.