Data assimilation applications with large-scale numerical models exhibit extreme requirements on computational resources. Good scalability of the assimilation system is necessary to make these applications feasible. Sequential data assimilation methods based on ensemble forecasts, like ensemble-based Kalman filters, provide such good scalability, because the forecast of each ensemble member can be performed independently. However, this parallelism has to be combined with the parallelization of both the numerical model and the data assimilation algorithm. In order to simplify the implementation of scalable data assimilation systems based on existing numerical models, the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) has been developed. PDAF provides support for implementing a data assimilation system with parallel ensemble forecasts and parallel numerical models. Further, it includes several optimized parallel filter algorithms, like the Ensemble Transform Kalman Filter. We will discuss the philosophy behind PDAF as well as features and scalability of data assimilation systems based on PDAF on the example of data assimilation with the finite element ocean model FEOM.