In this context, the warehouse measures, though not necessarily numerical, remain the indicators for analysis, and analysis is still performed following different perspectives represented by dimensions. Large data volumes and their dating are other arguments in favor of this approach (Darmont et al., 2003). Data warehousing can also support various types of analysis, such as statistical reporting, on-line analysis (OLAP) and data mining. The aim of this article is to present an overview of the existing data warehouses for biomedical data and to discuss the issues and future trends in biomedical data warehousing. We illustrate this topic by presenting the design of an innovative, complex data warehouse for personal, anticipative medicine.