Evaluating the Performance and Scalability o fan Apache Spark and Hadoop Cluster in a Low-Cost Environment
This article presents the design, configuration, and implementation of a distributed computing cluster using Apache Spark and Hadoop on Ubuntu Server 24.04.1 LTS. The architecture consists of a master node and multiple slave nodes connected to a local network via Ethernet. The installation, configur...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Perú |
| Institución: | Universidad Nacional Micaela Bastidas de Apurímac |
| Repositorio: | UNMB-Riqchary |
| Idioma: | español |
| OAI Identifier: | oai:ojs2.68.168.220.155:article/218 |
| Acceso en línea: | https://revistas.unamba.edu.pe/index.php/riqchary/article/view/218 |
| Access Level: | acceso abierto |
| Palabra clave: | Apache Spark data processing Hadoop distributed cluster distributed computing low-cost cluster apache spark procesamiento de datos hadoop clúster distribuido computación distribuida clúster de bajo costo |
| Sumario: | This article presents the design, configuration, and implementation of a distributed computing cluster using Apache Spark and Hadoop on Ubuntu Server 24.04.1 LTS. The architecture consists of a master node and multiple slave nodes connected to a local network via Ethernet. The installation, configuration, and performance testing process with PySpark are detailed. The results demonstrate that, while a local configuration is more efficient for small datasets (<100 MB), the distributed cluster offers significant improvements for data volumes greater than 1 GB, validating its scalability and viability for resource-constrained educational and research environments. |
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