Digital Twin Models for Simulating and Optimizing Enterprise Data Pipeline Performance

The rapid growth of cloud computing, big data analytics, and artificial intelligence has significantly increased the operational complexity of modern enterprise data pipeline environments, creating challenges related to resource utilization, energy consumption, system reliability, and predictive maintenance. Digital Twin technology has emerged as a promising solution by enabling real-time monitoring, simulation, and optimization of physical infrastructure through virtual replicas. This study evaluates the effectiveness of a Digital Twin–enabled framework for enterprise data pipeline environments optimization using a simulation-based experimental approach. A large-scale simulated enterprise data pipeline environments environment was developed utilizing a 12 TB operational dataset and 250 computational workloads to assess system performance under varying operational conditions.