Vol. 9 (2025): Publicación continua
Artículos

Integration of microservices with Laravel, Python and Django for teaching data processing

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  • José de Jesús Solorzano Rangel,
  • Mariana Carolyn Cruz-Mendoza,
  • César Primero-Huerta
José de Jesús Solorzano Rangel
Tecnológico Nacional de México-Tecnológico de Estudios Superiores de Valle de Bravo, Estado de México
Bio
Mariana Carolyn Cruz-Mendoza
Tecnológico Nacional de México - Valle de Bravo
Bio
César Primero-Huerta
Tecnológico de Estudios Superiores de Valle de Bravo
Bio
Portada-9
DOI: https://doi.org/10.33010/recie.v9i0.2460

Published 2025-06-13

Keywords

  • information access,
  • algorithm,
  • information processing
  • acceso a la información,
  • algoritmo,
  • procesamiento de la información

How to Cite

Solorzano Rangel, J. de J., Cruz-Mendoza, M. C., & Primero-Huerta, C. (2025). Integration of microservices with Laravel, Python and Django for teaching data processing. RECIE. Revista Electrónica Científica De Investigación Educativa, 9, e2460. https://doi.org/10.33010/recie.v9i0.2460

Copyright (c) 2025

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

This article demonstrates how to integrate various technologies into an educational environment to facilitate the teaching of data processing using microservices. The project employs Laravel, Alpine.js and Django for data management and transformation via microservices. Implementing a microservice-based architecture allows the application to be divided into manageable components, which not only improves the system’s scalability and maintainability but also provides a practical model for students to understand these technologies. A prototype was developed following the prototyping methodology, enabling an iterative system design with a focus on teaching key concepts such as data loading, category homogenization, duplicate elimination and categorical variable transformation. Performance evaluations indicate satisfactory response times when processing numerical data with small datasets, while areas for improvement were identified when handling large volumes of categorical data. This educational approach aims to reduce the technical skills gap among students interested in machine learning and data processing.

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