Integration of complex data and data management represent major challenges in large-scale biobank-based post-genome era research projects like GenomEUtwin (an international collaboration between eight Twin Registries) with extensive amounts of genotype and phenotype data combined from different data sources located in different countries. The challenge lies not only in data harmonization and constant update of clinical details in various locations, but also in the heterogeneity of data storage and confidentiality of sensitive health-related and genetic data. Solid infrastructure must be built to provide secure, but easily accessible and standardized, data exchange also facilitating statistical analyses of the stored data. Data collection sites desire to have full control of the accumulation of data, and at the same time the integration should facilitate effortless slicing and dicing of the data for different types of data pooling and study designs. Here we describe how we constructed a federated database infrastructure for genotype and phenotype information collected in seven European countries and Australia and connected this database setting via a network called TwinNET to guarantee effortless data exchange and pooled analyses. This federated database system offers a powerful facility for combining different types of information from multiple data sources. The system is transparent to end users and application developers, since it makes the set of federated data sources look like a single system. The user need not be aware of the format or site where the data are stored, the language or programming interface of the data source, how the data are physically stored, whether they are partitioned and/or replicated or what networking protocols are used. The user sees a single standardized interface with the desired data elements for pooled analyses.