High-Performance NoSQL Databases in Healthcare: A Comparative Benchmarking of Cassandra and MongoDB

Abstract

Introduction: The exponential growth in healthcare data requires robust and high-performance database solutions that could efficiently manage large datasets. There has been a wide adoption of NoSQL databases like Cassandra and MongoDB in handling complex voluminous data. This paper represents the comparative benchmarking among Cassandra and MongoDB in terms of performance in handling health care data.

Objectives: The Objective of this study is to the debate on NoSQL databases' performance, offering very important guidance to healthcare organizations and researchers to make decisions on choosing the best database solution.

Methods: This paper represents the comparative benchmarking among Cassandra and MongoDB in terms of performance in handling health care data. In this paper, we compare both databases against a number of critical performance metrics— namely, read/write latency, throughput, and scalability—with MIMIC-III. We set up a controlled environment in standardized configurations for a fair comparison.

Results: The results of these tests are presented to show the strengths and weaknesses of both databases in different scenarios of operations, shedding light on their appropriateness for different healthcare applications. Based on these findings, we make some recommendations for the selection of the right database technology to satisfy the intended performance in healthcare data management.

Conclusions: This research compares Cassandra and MongoDB for healthcare data management with MIMIC-III. Results indicate MongoDB is better in throughput, whereas Cassandra is better in write-heavy workloads. The selection relies on application requirements—Cassandra for high availability and MongoDB for flexible querying. These findings assist healthcare organizations in choosing the appropriate database. Future research can investigate other NoSQL solutions and real-time analytics.