Comparative Analysis of Database Management Systems in Handling Large-Scale Data

Abstract

The exponential growth of digital data in today’s technologically advanced landscape has significantly increased the demand for efficient and scalable database management systems (DBMSs). This study provides a comprehensive comparative analysis of prominent DBMS categories—traditional relational databases, NoSQL systems, and emerging NewSQL solutions—in their capacity to handle large-scale datasets. Through a blend of theoretical exploration, simulation-based experiments, and statistical validation, the research evaluates key performance indicators such as query response time, throughput, and data consistency under varied workloads. By systematically examining each system’s architecture and operational characteristics, this work highlights the trade-offs between scalability, performance, and consistency. The findings aim to guide data engineers, researchers, and decision-makers in selecting an appropriate DBMS tailored to specific application requirements, particularly in environments with massive data volumes. The study also discusses the evolution of database technologies, noting the convergence trends that blur traditional boundaries and suggest future directions in hybrid DBMS solutions.