Authors:
Bhagath Chandra Chowdari Marella

Addresses:
Department of Financial Services Insights & Data, Capgemini America Inc, New Jersey, United States of America.                                        

Abstract:

Big data transformed industries to keep data insights at arm’s reach but was stalled by the inefficiency of proper processing. This paper considers the viability of serverless frameworks to enhance big data processing. We explain how serverless environments process large-scale data pipelines at improved performance with reduced latency. Serverless systems provision resources on demand based on function-as-a-service (FaaS) and event-driven programming models. This work provides an elaborate methodology, dataset description, architecture figure, and empirical results through graphical and tabular displays. Data utilized in this research incorporates transactional data from online stores, such as customer ID, purchase history, product categories, timestamp, and geolocation, which were processed and evaluated using Python and Node.js on AWS Lambda and Google Cloud Functions. Findings reveal that serverless frameworks dramatically impact the rate at which data is processed and reduce operational costs. The performance test environments used to benchmark the performance were AWS Lambda, Google Cloud Functions, and Apache Spark. Our paper highlights key findings, limitations, and future research directions to integrate serverless computing into big data systems. Our work has contributed to understanding serverless architecture as an economical and scalable solution to data processing problems.

Keywords: Big Data; Serverless Architecture; Data Processing; Cloud Computing; Function-As-A-Service (Faas); Google Cloud Functions; Elaborate Methodology; Serverless Architecture.

Received on: 18/06/2024, Revised on: 29/08/2024, Accepted on: 11/10/2024, Published on: 14/12/2024

DOI: 10.69888/FTSIN.2024.000291

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 4, Pages: 242-251

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