Recent advances in computational resources have accelerated the development of inverse design approaches for structures and materials. In particular, data-driven strategies that leverage machine learning are increasingly shaping modern design workflows. However, constructing and maintaining large material-response databases remains challenging in practice. Typical challenges include data management, efficient parallel computing, and the integration of third-party simulation or analysis software. Because many applied fields remain conservative in openly sharing data and software, researchers often spent substantial time re-implementing standard procedures, hindering reproducibility, benchmarking, and adherence to FAIR principles. In this work, we introduce f3dasm (framework for data-driven design & analysis of structures and materials), a general and user-friendly open-source package that supports the full data-driven design pipeline. The framework aims to streamline research workflows, facilitate the replication of existing studies, and encourage the sharing of new experiments and results. f3dasm emphasizes flexibility, interoperability, and modularity. In particular, it enables researchers to integrate different software packages and computational tools across the key stages of a data-driven design loop: (1) design of experiments; (2) data generation; (3) machine learning and surrogate modeling; and (4) optimization. Since its first public release, f3dasm has been expanded to support MPI-based parallelization in high-performance clusters, enabling efficient large-scale data generation and simulation workloads. Furthermore, f3dasm adopts array-centric data structures that avoid breaking the computational graph when interfacing with automatic differentiation libraries, facilitating gradient-based learning and optimization. By lowering technical barriers and promoting reproducibility, f3dasm contributes to democratizing data-driven design for researchers and practitioners in structural and material engineering.