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PDAF is open-source software (https://pdaf.awi.de) providing a unified data assimilation framework for all data assimilation applications throughout the Earth system and beyond. PDAF is already coupled to a wide range of models, including all Earth system components, and is widely used for research and operational applications. With well-defined interfaces and modularization motivated by object-oriented programming, PDAF separates the forecast model, the observation handling, and the data assimilation algorithms. This structure ensures separation of concerns and allows domain experts to perform further developments of each component independently without interfering with each other. PDAF is further designed to make the coupling to models, online in memory or offline using disk files, particularly easy so that a new assimilation system can be built quickly. PDAF was recently upgraded to the new major revision 3.0. In PDAF V3, the code was modernized and restructured simplifying the procedure to add further data assimilation algorithms. New features are supported including model-agnostic incremental analysis updates, new diagnostics for observations and ensembles, and the ensemble square root filter (EnsRF) and ensemble adjustment Kalman filter (EAKF). With this, PDAF now provides the full range of algorithms from domain-localized ensemble filters and smoothers to Kalman filters with serial observation processing, particle and hybrid Kalman-nonlinear filters, and 3-dimensional variational data assimilation methods. Existing users can switch to PDAF V3 with minimal effort, while a new universal interface supporting all filters is recommended for new users. The Python-interface, pyPDAF, further allows the full implementation of an assimilation program in Python, leveraging the functionality and performance provided by PDAF. We will provide an overview of PDAF and the novelties of version 3.0.