...The tool is particularly valuable in data-intensive workflows such as machine learning, where large datasets can be accessed on demand without requiring full local downloads. It supports performance optimizations like file caching, which stores frequently accessed data on local storage to significantly improve throughput and reduce latency. The system integrates with cloud-native environments such as Kubernetes and can be used in distributed architectures where multiple compute nodes access shared datasets.