Optimizing EMR and Athena for Large-Scale Data Processing in S3 Environments

Bharath Thandalam Rajasekaran

University of Maryland

College Park, MD 20742, United States

barat007@gmail.com

Prof. (Dr) Sangeet Vashishtha

 IIMT University

O Pocket, Ganga Nagar, Meerut, Uttar Pradesh 250001 India

Abstract

This abstract discusses a research on Amazon EMR and Athena optimization for big data processing in S3 environments. The research compares different approaches to improve query performance, minimize processing time, and minimize operational costs when processing enormous data in Amazon S3. By analyzing configuration best practices, performance tuning techniques, and resource planning strategies, the research illustrates how to utilize EMR’s distributed computing platform and Athena’s serverless, interactive query feature. The research finds that a balanced approach—utilizing the strengths of both platforms—can greatly improve data processing effectiveness and scalability, allowing organizations to extract timely insights from gargantuan data warehouses at a cost-effective and high-performance level.

Keywords

Amazon EMR, Athena, S3, large-scale data processing, performance optimization, distributed computing, serverless querying, cost efficiency, scalability, data management.

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