COMPARING SCALABLE SERVERLESS ANALYTICS ARCHITECTURE ON AMAZON WEB SERVICES AND GOOGLE CLOUD

Abstract

Serverless computing has changed how data analytics workloads are managed because of its readiness, scalability, cost, and ease of management. This paper compares AWS and GCP regarding serverless analytics architecture, which is prevalent in the two cloud platforms. Key serverless technologies in the study include Amazon Kinesis, AWS Lambda, Amazon Athena, Google Pub/Sub, Google Cloud Functions, and BigQuery, considering metrics like efficiency, cost, scalability, and usability. Based on criteria and real-world case studies, I compare and contrast how each platform performs regarding scalability on large analytical tasks and batch and real-time analytics. The study has shown that both services provide powerful serverless tools. However, there are specific disparities in the pricing mechanism, query processing capability, and integration facilities. The recommendations in this paper enable organizations in a dilemma between AWS and Google Cloud to adopt serverless analytics to identify the right platform for specific workloads.