Abstract: The rapid evolution of cloud technology has given rise to autonomous databases, which leverage artificial intelligence and machine learning to automate management tasks, optimize performance, and ensure robust security. This paper provides a comprehensive benchmarking analysis of three leading autonomous database systems: Oracle Autonomous Database, Snowflake, and AWS Aurora. Each platform is evaluated based on key criteria, including performance, scalability, cost-efficiency, security, and integration capabilities.
Through simulated workloads and real-world case studies in finance, retail, and healthcare, the study highlights the strengths and limitations of each system. Oracle Autonomous Database excels in transactional workloads with advanced security and automation. Snowflake demonstrates exceptional performance in analytical tasks due to its cloud-native architecture and elastic scaling. AWS Aurora offers a balanced solution with high availability and cost-efficiency for mixed workloads.
The findings reveal distinct advantages tailored to different organizational needs, emphasizing that the choice of an autonomous database should align with specific use cases and business goals. By providing actionable insights, this paper aims to guide enterprises in selecting the optimal autonomous database system to drive innovation and operational efficiency in a rapidly evolving data landscape. Future directions include exploring hybrid deployments and long-term cost implications.
Keywords: Autonomous Databases, Transactional Workloads, Performance Evaluation, Analytical Workloads.
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DOI:
10.17148/IJARCCE.2024.131109
