Resilient geospatial data management: a comparative analysis of cloud-native and distributed ledger technology synchronization models for multi-cloud environments

This study presents a comparative analysis of cloud-native and Distributed Ledger Technology (DLT)-based synchronization models for resilient geospatial data management in multi-cloud environments. With the rising demand for real-time geospatial data in applications such as smart cities, disaster response, and environmental monitoring, ensuring data consistency, availability, and integrity across distributed cloud infrastructures has become increasingly critical. Cloud-native models offer high throughput and scalability through managed replication and consistency protocols but may be limited by eventual consistency and reliance on provider-managed security. In contrast, DLT-based models, particularly those using blockchain, enhance data integrity and auditability through decentralized, tamper-proof synchronization, albeit at the cost of increased latency and operational complexity. To evaluate these trade-offs, we propose a composite performance framework encompassing resilience, synchronization efficiency, and operational cost. Using simulation-based analysis, we assess both models under various failure scenarios and performance conditions. Simulation results demonstrate that cloud-native (CNM) models excel in latency (100ms vs. 200ms for DLT) and throughput (800 ops/sec vs. 500 ops/sec), while DLT ensures superior data integrity (0.99 vs. 0.90 for CNM). Results highlight the strengths and limitations of each approach and underscore the value of a hybrid model combining the speed of cloud-native systems with the trust guarantees of DLT for mission-critical geospatial applications. This research offers practical recommendations for system designers and contributes to the evolving integration of blockchain, cloud, and AI technologies in secure, multi-cloud geospatial infrastructures.