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Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism

Year: 2025

Type: article

Abstract: Data acquisition serves as a critical component of modern data architecture, with REST API integration emerging as one of the most common approaches for sourcing external data. This study evaluates the efficiency of various methodologies for collecting data via REST APIs and benchmark their performance. It explores how leveraging the Spark distributed computing platform can optimize large scale REST API calls, enabling enhanced scalability and improved processing speeds to meet the demands of high volume data workflows.

Source: Journal of Artificial Intelligence and Big Data

DOI: 10.31586/jaibd.2025.6049

Authors: Hanza Parayil Salim, Yanas Rajindran

Institution: AT&T (United States)

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4408723823

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