Experimental and Functional comparison of BigchainDB and SQL server for Data Management

Abstract

The rapid growth of digital data has increased the demand for data management systems that provide not only high performance and scalability but also strong security, integrity, and trust guarantees. This paper presents an experimental and functional comparison between SQL Server, a traditional relational database, and BigchainDB, a Blockchain-based decentralized database that integrates distributed ledger features with database capabilities. Both systems were deployed in an identical containerized simulation environment using Docker to ensure fair and reproducible evaluation. A unified dataset containing up to 100,000 records was generated and used to assess insertion performance, query latency, scalability, and system resource consumption (CPU and memory). System behavior was continuously monitored using Prometheus and Grafana. In addition to performance metrics, functional metrics including immutability, traceability, and ownership control were evaluated.

The experimental results show that SQL Server achieves significantly lower latency and faster query response, but at the cost of higher CPU and memory utilization. Conversely, BigchainDB demonstrates lower resource consumption and provides strong security and tamper-resistance guarantees, though with increased latency due to consensus and transaction validation mechanisms. These findings highlight the trade-offs between centralized and decentralized data management solutions and provide practical guidance for selecting the appropriate technology based on application requirements for performance, trust, and security.