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

Author(s)	rahul goel
Country	United States
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.
Keywords	Amazon Aurora, cloud database, scaling, performance, distributed systems, database optimization
Field	Computer > Data / Information
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-04-08
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.40977
Short DOI	https://doi.org/g9fb9d

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International Journal For Multidisciplinary Research