A prominent Swiss financial institution is seeking to improve its data management capabilities in response to the growing volume and complexity of data. The existing system struggles with scalability and the ability to manage diverse data types, including structured, semi-structured, and unstructured data. To support effective decision-making and ensure compliance with regulatory requirements, the institution recognizes the urgent need for a robust, scalable, and secure data architecture.

The thesis investigates the concept of a data lake. The research includes a comprehensive literature review, the development of a conceptual framework for data ingestion, storage, processing, and access using Azure services. Expert interviews were conducted to validate the feasibility and effectiveness of the proposed architecture, providing insights from experienced professionals in data management and IT infrastructure.

The study shows that a data lake implementation is feasible and beneficial for the Swiss financial institution, enhancing scalability, flexibility, and data handling. Key findings support cloud-based solutions for managing diverse data types and extensive storage needs. The proposed architecture, using Azure services, offers a scalable, secure, and efficient data management framework. Expert interviews emphasized the importance of robust security, including a private cloud and 'Bring Your Own Key (BYOK)' strategy to protect sensitive data. The study also addresses challenges like data migration complexities, the need for effective change management, and vendor lock-in. Adopting this data lake architecture will improve data integration, support advanced analytics, and enhance decision-making. It aligns with the institution’s strategic goals, offering a future-proof solution that fosters innovation and operational efficiency. The research provides a solid foundation for advancing data management and maintaining competitiveness in the financial sector.