Data Warehouse Design and Prototyping in the Health Insurance Context

In the modern era, technological advances have led to an influx of 'Big Data' in the corporate sector. Databases and Data Warehouses are crucial for storing and managing operational data, as well as enabling strategic analysis. This analytical capability is essential for informed decision-making and future planning. Together, they form a vital synergy for effective business information management. Designing a Data Warehouse, especially for Big Data, is complex yet rewarding. It serves as a centralized repository for analyzing and interpreting Big Data, facilitating easy access, analysis, and reporting. The ETL process is pivotal in Data Warehouse creation, involving data flow management and business rule application. Choosing the type of Data Warehouse impacts its architecture, which depends on data volume and analysis requirements. The DFM (Dimensional Fact Model) is a possible formalism for data warehousing design. Incorporating the DFM efficiently categorizes data, enhancing sorting capabilities. This model uses dimensions for context and facts for quantifiable data points, enabling systematic arrangement and analysis of Big Data for informed decisions. This Dissertation explores Data Warehousing with a focus on the Dimensional Fact Model. It examines a project based on this model, detailing the company, Data Warehouse architecture, ETL process, and technologies used. The theoretical part covers Business Intelligence, transitioning from Databases to Data Warehouses, and delves deeper into the DFM formalism. The final chapter provides a practical implementation of the Data Warehouse through the dimensional fact model. It explores how dimensions and metrics are modeled and connected, offering a clear view of business information and unveiling new perspectives on enterprise information resource analysis and utilization.