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Review Article Open Access March 22, 2025

Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism

Hanza Parayil Salim 1 and Yanas Rajindran 2
1
Staff Engineer, Neiman Marcus, Texas, USA
2
Lead Engineer, AT&T, Texas, USA
Publihed in: Current Research in Public Health (Volume 5, Issue 1, 2025)
Page(s): 38-43
DOI: 10.31586/jaibd.2025.6049
Received
February 02, 2025
Revised
March 08, 2025
Accepted
March 19, 2025
Published
March 22, 2025
Keywords
Distributed computing; Parallel processing; Data Acquisition; Apache Spark; RESTful Web Services; REST API; Data Analytics
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2025. Published by Scientific Publications
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Cite This Article

APA Style
Salim, H. P. , & Rajindran, Y. (2025). Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism. Current Research in Public Health, 5(1), 38-43. https://doi.org/10.31586/jaibd.2025.6049
ACS Style
Salim, H. P. ; Rajindran, Y. Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism. Current Research in Public Health 2025 5(1), 38-43. https://doi.org/10.31586/jaibd.2025.6049
Chicago/Turabian Style
Salim, Hanza Parayil, and Yanas Rajindran. 2025. "Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism". Current Research in Public Health 5, no. 1: 38-43. https://doi.org/10.31586/jaibd.2025.6049
AMA Style
Salim HP, Rajindran Y. Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism. Current Research in Public Health. 2025; 5(1):38-43. https://doi.org/10.31586/jaibd.2025.6049 
@Article{crph6049,
AUTHOR = {Salim, Hanza Parayil and Rajindran, Yanas},
TITLE = {Enhancing Scalability and Performance in Analytics Data Acquisition through Spark Parallelism},
JOURNAL = {Current Research in Public Health},
VOLUME = {5},
YEAR = {2025},
NUMBER = {1},
PAGES = {38-43},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/6049},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2025.6049},
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.},
}
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AB  - 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.
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