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Architectural Deep Dive and Performance Evaluation: Scaling Relational Workloads with Amazon Aurora

rahul goel

2025 · DOI: 10.36948/ijfmr.2025.v07i02.40977
International Journal For Multidisciplinary Research · 0 Citations

TLDR

This paper explores Aurora’s unique architecture, focusing on its distributed storage system, adaptive scaling mechanisms, and high availability features, and provides insights into best practices for optimizing Aurora-based systems to achieve cost-efficient and resilient database solutions.

Abstract

Amazon Aurora, a cloud-native relational database engine, has emerged as a leading solution for scalable and high-performance database management. This paper explores Aurora’s unique architecture, focusing on its distributed storage system, adaptive scaling mechanisms, and high availability features. Through real-world case studies, we analyze how organizations have leveraged Aurora to enhance performance and scalability. Additionally, insights from prior benchmarking studies [21] provide an in-depth understanding of Aurora’s efficiency in handling dynamic workloads. This study provides insights into best practices for optimizing Aurora-based systems to achieve cost-efficient and resilient database solutions.