Bibliographic citations
Mere, C., Sandoval, L. (2023). Arquitectura big data en cloud computing para la generación de reportes de monitoreo de equipos de red en el área de operaciones TI de una empresa proveedora de servicios gestionados [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/667797
Mere, C., Sandoval, L. Arquitectura big data en cloud computing para la generación de reportes de monitoreo de equipos de red en el área de operaciones TI de una empresa proveedora de servicios gestionados [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2023. http://hdl.handle.net/10757/667797
@misc{renati/405167,
title = "Arquitectura big data en cloud computing para la generación de reportes de monitoreo de equipos de red en el área de operaciones TI de una empresa proveedora de servicios gestionados",
author = "Sandoval Nuñovero, Luis Felipe",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2023"
}
The objective of this project is to propose the implementation of a big data architecture in cloud computing for the generation of customized reports for a company that provides technological services, which is motivated by the need to optimize the manual processes of extraction and transformation of data. This is due to the fact that, currently, the flow of reporting is slow due to the presence of manual tasks and operational activities assigned to managers who, on certain occasions, do not have the adequate technical knowledge to carry out this type of activity, which generates a negative impact in the support service and after-sales quality of the company. For the company, the development of the solution will provide a significant change, since they will stop using their own on-premise solutions and will begin to explore other types of resources resulting from the digital transformation, which will increase the speed and agility of the business. For this reason, the use of the cloud computing platform Microsoft Azure was chosen to guarantee the flexibility and availability of the process. This must go along with databases services and big data processing tools, as well as some additional resources, such as storage, file formats, and data integration method, which ensure the speed of processing and availability of the data.
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