Adaptive ETL Orchestration Using Reinforcement Learning in Multi-Cloud Data Pipelines

Abstract

With the fast rise in the number of companies using the capabilities of big data technologies, it became essential to introduce more advanced solutions to support Extract, Transform, and Load (ETL) operations within a wide range of cloud architectures. In general, the traditional approach to ETL orchestration involves scheduling tasks according to the established static rules and allocating resources according to predefined procedures, which often fails to address the needs of varying workloads, limited availability of computing resources, changing network performance, and different billing structures typical of multi-cloud environments. In order to overcome such problems, the paper is aimed at evaluating reinforcement learning for orchestrating ETL data pipelines. In this case, the proposed architecture will feature a learning-based solution designed to ensure the continuous adjustment of the schedule, computing resources, and data storage according to the current status of the ETL process. With the help of information about execution results, the reinforcement learning approach will be able to determine the best policy in terms of processing latency, throughput, failure rates, and other important factors. Thus, it will be possible to analyze the main components, principles of operation, and potential advantages of adaptive ETL orchestration based on reinforcement learning.