2023-09-02 Establishing the Need Our in-memory column-major data processing platform, tech.ml.dataset (TMD), drives the future of functional data science. When data gets large enough not to fit in memory, one can continue with TMD by operating on samples of data, or otherwise filtering to relevant subsets to fit in bounds imposed by the working environment. Moreover, one can accomplish persistence, for data small and large, with nippy, arrow, or parquet . When data becomes large enough, for example sets of .csv files on the order of ~100GB with relational aspects to them, the tools in their current state can become unwieldy. One is tempted to get involved in nonfunctional sparky cluster snafus. Of course, maintaining some level of transactional interaction and a simple disk IO model is still super-desirable. Local disks are big enough, and local chips are fast enough, no need to do anything rash.…