Thanks for your input. We are planning to also support an API and/or CLI (besides the GUI) to set up the pipelines through scripting. Depending on what we hear from the community we will definitely add this sooner rather than later.

You make a great point for the use of monitoring the progress of a run. During pipeline runs the visual pipeline interface (also in view only mode during or after running pipeline runs as part of an experiment) can be helpful as a visual indication of where the pipeline failed, how long pipeline steps took to execute and whether certain pipeline steps are still running.

Lastly, for team collaboration we think the visual interface is indeed helpful to explain (especially to the less technical people) the structure of the code, what exactly the pipeline sets out to solve and how the pipeline solves it. In addition, the possibility to add intermediary pipeline steps that serve as dashboards can be helpful for collaboration.

There's a large tradition of data analysis/BI tools using visual programming/ "no-code" tools, think Talend, etc. They're popular since data analysis typically contains a number of people without coding backgrounds. Even among data science teams, many don't have the engineering background of data engineers (and certainly there's a segment of data scientists who don't care as much about building the data pipelines and the cleaning that's necessary).

Low code tools _can_ (but not necessarily will) effectively empower data teams.