Comparing Globally Distributed Databases and Traditional Relational Databases: An Architectural Analysis

Authors

  • Amit Kumar Garg

DOI:

https://doi.org/10.22399/ijcesen.4900

Keywords:

Globally Distributed Databases, Relational Database Architecture, Disaster Recovery Mechanisms, Consensus-Based Consistency, Operational Complexity Trade-Offs

Abstract

Globally-distributed database designs differ from customary RDBMS designs. Trade-offs in multi-region operational capacity, disaster recovery, and long-term operational viability can lead to both conflicting and complementary planned priorities. Globally-distributed databases make use of synchronous replication protocols, automated failover procedures, and consensus-based consistency models to provide strong availability guarantees across physical and geographic boundaries. However, these designs still suffer from the intrinsic latency and performance tradeoffs of cross-region coordination, while relational database architectures like PostgreSQL are able to achieve superior local performance characteristics and cost efficiency when workloads are localized to regions. That said, they require explicit operational documentation and orchestration for cross-region disaster recovery and failover. Beyond technical considerations, the architectural choices also include differing assumptions of system complexity: is it centralized within the database platform or distributed among operational processes and organizational practices? Organizational preference for a high base cost with automated resilience or for a low base cost with flexibility and explicit DR responsibilities affects the choice of architecture. An organization's decision depends on the requirement for geographic distribution, the tolerance for downtime and access latency, the cost of the infrastructure, and the availability of operational expertise.

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Published

2026-02-11

How to Cite

Amit Kumar Garg. (2026). Comparing Globally Distributed Databases and Traditional Relational Databases: An Architectural Analysis. International Journal of Computational and Experimental Science and Engineering, 12(1). https://doi.org/10.22399/ijcesen.4900

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Section

Research Article