Publication
ImplementaĆ§Ć£o de sistema de dashboard reporting e analytics na ePharma
| datacite.subject.fos | Engenharia e Tecnologia | |
| datacite.subject.sdg | 09:IndĆŗstria, InovaĆ§Ć£o e Infraestruturas | |
| dc.contributor.advisor | Gueiral, Nuno Eduardo Dias | |
| dc.contributor.author | CAPELAS, MARIA ANA FERREIRA | |
| dc.date.accessioned | 2025-09-15T14:46:58Z | |
| dc.date.available | 2025-09-15T14:46:58Z | |
| dc.date.issued | 2025-07-28 | |
| dc.description.abstract | Atualmente, com o grande volume de dados disponĆvel nas organizaƧƵes e a necessidade de ferramentas de apoio Ć decisĆ£o, advĆ©m a necessidade da existĆŖncia de sistemas capazes de auxiliar na tomada de decisĆ£o. No caso especĆfico da plataforma ePharma 3.0, identificou-se a inexistĆŖncia de um sistema de Dashboard Reporting e Analytics que permitisse analisar, de forma eficaz, as mĆ©tricas relativas a encomendas, margens e vendas. Assim, esta dissertaĆ§Ć£o tem como principal objetivo a implementaĆ§Ć£o de uma soluĆ§Ć£o de Business Intelligence (BI) que permita a anĆ”lise e visualizaĆ§Ć£o de dados relevantes para os vĆ”rios stakeholders da plataforma. A metodologia utilizada baseou-se na construĆ§Ć£o de um pipeline de dados, onde foi utilizado o Azure Data Factory para o processo de extraĆ§Ć£o, transformaĆ§Ć£o e carregamento (ETL), e o Databricks para a organizaĆ§Ć£o dos dados segundo a arquitetura de camadas bronze, silver e gold. Os dashboards foram desenvolvidos com a ferramenta Microsoft Power BI, permitindo a anĆ”lise visual dos dados armazenados no Data Lake. Como consequĆŖncia, foram criados dashboards que permitem a monitorizaĆ§Ć£o de indicadores de desempenho relacionados com encomendas, margens e vendas. A soluĆ§Ć£o aplicada provou ser eficiente na anĆ”lise dos dados provenientes de diversas fontes. Em suma, a adoĆ§Ć£o de uma arquitetura Data Lakehouse, aliada Ć utilizaĆ§Ć£o de ferramentas de BI, demonstrou-se promissora na otimizaĆ§Ć£o dos processos de anĆ”lise e apoio Ć tomada de decisĆ£o na ePharma. | por |
| dc.description.abstract | Nowadays, with the large volume of data available in organizations and the need for decision support tools, there is a need for systems capable of assisting in decision-making. In the specific case of the ePharma 3.0 platform, it was identified that there was no dashboard reporting and analytics system to effectively analyze metrics relating to orders, margins and sales. The main aim of this dissertation is therefore to implement a Business Intelligence (BI) solution that enables the analysis and visualization of relevant data for the platform's various stakeholders. The methodology used was based on building a data pipeline, using Azure Data Factory for the extraction, transformation and loading (ETL) process, and Databricks for organizing the data according to the bronze, silver and gold layer architecture. Dashboards were developed using the Microsoft Power BI tool, allowing visual analysis of the data stored in the Data Lake. As a result, dashboards were developed to monitor performance indicators associated with orders, margins and sales. The solution implemented proved to be effective in analyzing useful information from data originating from various sources. In conclusion, the adoption of a Data Lakehouse architecture, combined with the use of BI tools, has proved promising in optimizing ePharma's analysis and decision-making support processes. | eng |
| dc.identifier.tid | 204000033 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/30456 | |
| dc.language.iso | por | |
| dc.rights.uri | N/A | |
| dc.subject | Business Intelligence | |
| dc.subject | Azure Databricks | |
| dc.subject | Microsoft Power BI | |
| dc.subject | Pharmaceutical sector | |
| dc.subject | Dashboard Reporting | |
| dc.subject | Analytics | |
| dc.subject | Setor farmacĆŖutico | |
| dc.title | ImplementaĆ§Ć£o de sistema de dashboard reporting e analytics na ePharma | por |
| dc.title.alternative | Implementation of dashboard system reporting and analytics at ePharma | eng |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| thesis.degree.name | Mestrado em Engenharia BiomƩdica |