> With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Each rule describe

> With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Each rule describes a step in an analysis defining how to obtain output files from input files. Dependencies between rules are determined automatically. This appears to be a step backwards. Over a long career, I've seen the benefits of functional programming and dataflow languages for data processing. Sure, there is a time and place for intermediate files, but they are not a wise choice for a fundamental compositional principle. Update: I can see why Snakemake is preferable over manual file processing pipelines. How can improvement be bad? Well, decisions are relative. Why choose X framework if there are better designs available? I think it is possible to solve more than one problem at once -- I don't see why the whole framework should be built around files as intermediate storage locations. Generally speaking, it is often possible to design a framework that solves incremental problems while also leapfrogging antiquated design choices.