Publicação
Importação Massiva de Dados Mestres com Processamento Paralelo Eficiente
| Resumo: | With the growth of technology towards an increasingly digital market, organizations have been transforming the way they manage data and processes. In the retail sector, characterized by large volumes of information and high operational complexity, the consistency and quality of business data, such as brands, suppliers, and products, play a strategic role in operational efficiency and data governance. In this context, the adoption of Master Data Management (MDM) systems, responsible for governing core organizational data, becomes a key factor for the efficiency and integration of business processes. In environments characterized by frequent system integrations and large data volumes, the need for bulk master data imports is particularly critical. The lack of adequate mechanisms to support these processes compromises operational efficiency and limits the evolution of enterprise architectures. This work proposes, implements, and evaluates an architecture for bulk master data import capable of processing large volumes of information in a parallel, flexible, and scalable manner, integrated into a distributed enterprise ecosystem. The proposed solution supports data imports from generic formats through configurable mechanisms and incorporates validation, transformation, performance monitoring, and data traceability features. The proposed approach was evaluated in both experimental and production environments, indicating significant performance gains when compared to sequential approaches, demonstrating the effectiveness of parallel processing for large-scale data imports. The results highlight the solution’s applicability in real business contexts, contributing to improved large-scale operational efficiency and consolidation of more scalable, modular data architectures aligned with digital transformation principles in the retail sector. |
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| Autores principais: | Teixeira,Ana Lília Alves |
| Assunto: | Master Data Bulk Data Import Digital Transformation Parallel Processing Retail |
| Ano: | 2026 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | português |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | With the growth of technology towards an increasingly digital market, organizations have been transforming the way they manage data and processes. In the retail sector, characterized by large volumes of information and high operational complexity, the consistency and quality of business data, such as brands, suppliers, and products, play a strategic role in operational efficiency and data governance. In this context, the adoption of Master Data Management (MDM) systems, responsible for governing core organizational data, becomes a key factor for the efficiency and integration of business processes. In environments characterized by frequent system integrations and large data volumes, the need for bulk master data imports is particularly critical. The lack of adequate mechanisms to support these processes compromises operational efficiency and limits the evolution of enterprise architectures. This work proposes, implements, and evaluates an architecture for bulk master data import capable of processing large volumes of information in a parallel, flexible, and scalable manner, integrated into a distributed enterprise ecosystem. The proposed solution supports data imports from generic formats through configurable mechanisms and incorporates validation, transformation, performance monitoring, and data traceability features. The proposed approach was evaluated in both experimental and production environments, indicating significant performance gains when compared to sequential approaches, demonstrating the effectiveness of parallel processing for large-scale data imports. The results highlight the solution’s applicability in real business contexts, contributing to improved large-scale operational efficiency and consolidation of more scalable, modular data architectures aligned with digital transformation principles in the retail sector. |
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