Intelligent Optimization of ETL Processes through Adaptive ML Approach

Abstract

In modern data ecosystems, the efficiency of Extract, Transform, Load (ETL) processes is critical for ensuring data quality,
availability, and scalability. This study investigates the optimization of ETL processes through the integration of adaptive
machine learning models. Traditional ETL methods are often static, limited in their ability to handle dynamic data flows
and real-time updates. By leveraging machine learning (ML) techniques, the study explores methods to dynamically
optimize ETL workflows, automate data transformation tasks, and reduce data processing time. The results demonstrate
significant improvements in the performance, accuracy, and scalability of ETL systems, offering insights into the practical
benefits of adaptive machine learning in data pipeline management. Future research could explore further integrations
with cloud-based services and real-time analytics to drive further efficiencies.