Big data architectures for distributed acoustic sensing: a review of storage, real-time processing, and scalable analytics

This review examines the end-to-end computational pipeline for DAS big data workloads, from acquisition constraints shaped by fiber optic physics to storage architectures, real-time processing frameworks, and scalable analytics. We synthesize peer reviewed evidence on compression strategies, stream processing engines, distributed training, and hardware acceleration, while critically evaluating claims of cost reduction, latency improvement, and energy efficiency against operational benchmarks from field deployments. Domain applications in industrial monitoring, geophysical sensing, and environmental observation are discussed with attention to cross-domain transferability and its limits. We also identify unresolved challenges, including uncertainty in lossy compression, model drift under concept shift, and the need for standardized benchmarking protocols. Finally, we outline prospective developments photonic accelerators, foundation models, and autonomous closed loop systems as emerging research directions rather than established capabilities. This review is intended for optics and photonics researchers seeking to understand how computational infrastructure choices interact with the physical properties of fiber-optic sensing.