A Comparative Study of NoSQL Database Performance in Multi-Cloud Architectures for High-Throughput Data Processing

Abstract

This paper examines the performance of NoSQL databases in multi-cloud environments, with a focus on high-throughput data processing. As enterprises adopt multi-cloud architectures to enhance flexibility, fault tolerance, and cost efficiency, NoSQL databases like Cassandra, MongoDB, and Couchbase have become critical for managing distributed data. The study compares these databases across cloud platforms, analyzing key performance metrics such as latency, throughput, scalability, and fault tolerance. Cassandra demonstrates strong performance in write-heavy workloads due to its peer-to-peer architecture, while MongoDB excels in flexible data management but may experience higher read latency in multi-cloud environments. Couchbase offers high performance for read-heavy workloads, particularly through its caching mechanisms, but may need tuning for write-intensive applications. The paper also discusses strategies for optimizing NoSQL performance in multi-cloud setups, including data partitioning, replication, and load balancing, to ensure scalable and reliable data processing.