Adopción de metodologías DevOps para la Inteligencia de Negocio = Business Intelligence adoption of DevOps methodologies

Antoñanzas Martínez, Guillermo (2022). Adopción de metodologías DevOps para la Inteligencia de Negocio = Business Intelligence adoption of DevOps methodologies. Tesis (Master), E.T.S. de Ingenieros Informáticos (UPM).

Descripción

Título: Adopción de metodologías DevOps para la Inteligencia de Negocio = Business Intelligence adoption of DevOps methodologies
Autor/es:
  • Antoñanzas Martínez, Guillermo
Director/es:
  • Vinju, Jurgen
  • Ochoa Venegas, Lina
Tipo de Documento: Tesis (Master)
Título del máster: Digital Innovation: Data Science
Fecha: Marzo 2022
Materias:
ODS:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Otro
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

Texto completo

[thumbnail of TFM_GUILLERMO_ANTONANZAS_MARTINEZ.pdf] PDF (Portable Document Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (2MB)

Resumen

For this Master Thesis, we want to know what is the state of the Business Intelligence (BI) tools in the Development and Operation (DevOps) maturity level scale. To the best of our knowledge, no analysis like this exists in the state of the art. Moreover, the communication between the DevOps teams is fundamental for any project and it is even more relevant to those that keep growing, like how BI projects tend to be. To this aim, we will take an in-depth look into the most relevant and most used BI tools and the features they have, with reference to the DevOps models and its maturity levels, as well as some additional features that are of interest to the community (e.g. license, activity, maintainability). We will analyze their characteristics in the DevOps categories and obtain the theoretical maximum maturity levels for each tool to show the gaps that the BI tools currently have. Multiple DevOps Maturity Models will be used to widen the scope of the analysis and have a better representation of how tools implement DevOps. Additionally, we will analyze real cases of BI projects from the internship company, NTTData, to see if the theoretical maximum maturity levels are reached and propose changes to improve those levels.

Más información

ID de Registro: 70181
Identificador DC: https://oa.upm.es/70181/
Identificador OAI: oai:oa.upm.es:70181
Depositado por: Biblioteca Facultad de Informatica
Depositado el: 04 Abr 2022 08:06
Ultima Modificación: 04 Abr 2022 08:06