Optimizing Large-Scale ETL Workflows Using Distributed Processing and Intelligent Scheduling

Abstract

As organizations process increasingly large datasets, traditional ETL workflows struggle to meet performance and reliability expectations. This paper presents an optimization framework combining distributed data processing with intelligent job scheduling techniques. By leveraging workload profiling and adaptive resource allocation, the proposed approach reduces execution latency and operational costs. Empirical evaluation across multiple cloud environments confirms significant improvements in throughput and fault tolerance.