A Comparative Study of NoSQL Database Performance in Big Data Analytics

Abstract

As organizations continue to grapple with the massive influx of data in the era of Big Data, the selection of an appropriate database management system becomes critical for efficient data storage, retrieval, and analytics. This study focuses on evaluating and comparing the performance of various NoSQL databases commonly employed in the context of Big Data analytics. The research methodology involves the creation of a controlled experimental environment to simulate real-world scenarios, ensuring a fair and unbiased comparison. We consider prominent NoSQL databases such as MongoDB, Cassandra, Couchbase, and Redis, examining their capabilities in handling the diverse and complex data structures inherent in Big Data. Key performance metrics include read and write throughput, query response time, scalability, and fault tolerance.