This article explores conceptual approaches to optimizing ETL processes in distributed systems using a hybrid algorithmic solution based on the integration of Grey Wolf Optimizer (GWO) and Tabu Search (TS) methods. The study analyzes the characteristics of ETL under cloud-based architectures and identifies key challenges, such as high computational complexity, data redundancy, and the difficulty of clustering when handling large volumes of information. The results confirm the hypothesis that the synergy between GWO and TS algorithms leads to more efficient ETL processes, which is especially relevant for modern distributed systems and cloud computing environments. The article will be of interest to other researchers and graduate students specializing in distributed computing systems, big data processing, and ETL process optimization, as it presents an analysis of methodological approaches aimed at improving data integration efficiency within scalable architectures. The findings are also valuable for IT practitioners, enterprise system architects, and developers seeking to integrate advanced ETL optimization methods into modern information systems to enhance their performance and resilience.

ETL, optimization, distributed systems, cloud computing, Grey Wolf Optimizer

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