Large-scale processing of big data with Python in modern business analytics

Mshvidobadze T. I.

Article

Issue:	2025 №4 (92)
Section:	Economics of enterprise
UDK:	004.932:330.4
DOI:	https://doi.org/10.33271/ebdut/92.188
Article language:	English
Pages:	188-194
Title:	Large-scale processing of big data with Python in modern business analytics
Author:	Mshvidobadze T. I., Gori State University
Annotation:	Methods. The paper is based on a comprehensive review of the impact of Big Data Analytics on modern business analytics and presents an innovative solution for efficient processing of this data in high-performance computing (HPC) environments. The study aims to demonstrate how the integration of Big Data technologies can qualitatively change strategic planning, risk management and operational optimization in a business environment. The analysis covers the dynamic landscape of modern business analytics, emphasizing its transformative power in obtaining critical insights from large and diverse data sets. Special attention is paid to the challenges associated with insufficient performance and general versatility of existing big data processing tools in HPC infrastructures. To address these problems, the study discusses PyCOMPS, a task-based programming model in Python, for the first time. The performance and efficiency of PyCOMPS are evaluated by applying it to implement a complex machine learning algorithm, namely Cascade SVM. Novelty. The novelty of the work lies in the integrated approach, which not only summarizes the multifaceted role of big data analytics (in particular, in customer-oriented initiatives and operational optimization), but also offers a specific, high-performance and flexible solution. PyCOMPS is positioned as an excellent answer to the problem of the lack of productive and universal tools for distributed big data processing in HPC. The results of the Cascade SVM implementation serve as empirical evidence of its advantages. Results. The study details the transformational path of big data analytics in modern business intelligence, confirming its crucial role in reducing risks and increasing operational efficiency. The main result is a demonstration of the high performance of PyCOMPS for the efficient development and execution of Big Data analytical tasks. The work concludes with practical recommendations for organizations seeking to maximize the potential of big data analytics in the data-driven era. Practical value. The work has high practical value for IT architects, data engineers and analysts. Discussion of the benefits PyCOMPS provides a direct toolkit for high-performance and efficient development of big data analytics in the environment of modern business intelligence systems. This solution provides better flexibility and performance compared to traditional HPC models, making complex data processing more accessible to developers using Python.
Keywords:	Big data, Business Intelligence, Data analytics, Modern Business, PyCOMPS
File of the article:	EV20254_188-194.pdf
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