Integrated data analysis (IDA) pipelines-that combine data management (DM) and query processing, high-performance computing (HPC), and machine learning (ML) training and scoring-become increasingly common in practice. Interestingly, systems of these areas share many compilation and runtime techniques, and the used-increasingly heterogeneous-hardware infrastructure converges as well. Yet, the programming paradigms, cluster resource management, data formats and representations, as well as execution strategies differ substantially. DAPHNE is an open and extensible system infrastructure for such IDA pipelines, including language abstractions, compilation and runtime techniques, multi-level scheduling, hardware (HW) accelerators, and computational storage for increasing productivity and eliminating unnecessary overheads. In this paper, we make a case for IDA pipelines, describe the overall DAPHNE system architecture, its key components, and the design of a vectorized execution engine for computational storage, HW accelerators, as well as local and distributed operations. Preliminary experiments that compare DAPHNE with MonetDB, Pandas, DuckDB, and TensorFlow show promising results.