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Analysis Study Research Paper | Computer Science & Engineering | India | Volume 10 Issue 6, June 2021 | Rating: 3.2 / 10
Architecting Resilient REST APIs: Leveraging AWS, AI, and Microservices for Scalable Data Science Applications
Sai Tarun Kaniganti [8] | Venkata Naga Sai Kiran Challa [6]
Abstract: This paper discusses the understanding and building of re-stifle APIs and further elaborates on how the applications of big data science can be made efficient, utilizing AWS, AI, and microservices. It fulfils the requirements associated with the processing of big data and functioning in nearly real-time within web applications. The paper reiterates concepts like statelessness, the feature of being cachable, having a uniform interface design and using a layered architecture design. Besides, it looks at how AI and ML can improve REST API scalability by integrating the following factors; predictive scaling, outlier detection, caching, and request priority. Scenarios and architectural patterns show how these technologies adopt them with AWS services these include API Gateway, AWS Lambda, DynamoDB, ElastiCache, SQS, and CloudWatch. The intended audience is to offer the complete guide to constructing truly 'restful' representational State Transfer APIs to be 'fit-for-purpose' to support modern Web applications.
Keywords: REST APIs, scalability, AWS, Artificial Intelligence, Microservices, big data applications, stateless, cache, layered architecture, predictive scaling, intelligent caching, Machine learning, API Gateway, AWS Lambda, DynamoDB, ElastiCache, SIMPLE Queue Service, CloudWatch
Edition: Volume 10 Issue 6, June 2021,
Pages: 1825 - 1834