In the rapidly evolving landscape of data management and analytics, organizations are increasingly recognizing the transformative potential of migrating their data warehouses to the cloud. This thesis delves into the intricacies of implementing a data warehouse on cloud infrastructure, exploring the dynamic interplay between traditional data warehousing concepts and the agility and scalability offered by cloud computing.

As enterprises grapple with the exponential growth of data, the demand for robust and flexible data storage and analytics solutions has never been more critical. Cloud-based data warehousing emerges as a compelling answer to these challenges, promising not only the ability to store vast amounts of data but also to process and derive meaningful insights in a cost-effective and scalable manner.

This thesis aims to provide a comprehensive understanding of the considerations, methodologies, and best practices involved in the migration and implementation of a data warehouse to the cloud environment. From the initial architectural design to the selection of cloud services and the optimization of query performance, each facet of the implementation process will be meticulously examined. Additionally, the study will address the impact of cloud-native features, such as serverless computing, automatic scaling, and data lakes, on the overall efficiency and effectiveness of a cloud-based data warehouse.

Through empirical analysis and case studies, this research seeks to contribute valuable insights to practitioners, IT professionals, and decision-makers contemplating or navigating the transition from on-premises data warehousing to the cloud. By illuminating the intricacies and potential pitfalls of this migration process, this thesis aims to equip organizations with the knowledge needed to make informed decisions and leverage the full capabilities of cloud-based data warehousing for enhanced business intelligence and analytics.