My solution is not particularly automated or integrated. What I do is to write code to print out the state of the whole pipeline: input data vs output metrics. Here, standarization and annotation matters, so learn how to annotage smartly. I integrate a logger to print out each step in the pipeline (which file was loaded, method used, steps in data curation, etc). Now I have complete visibility into input-steps-output.
Now it is time to do your lab notebook. I keep my notebook in Numbers. This is perhaps the most controversial part of my methodology. I chose Numbers (esentially Excel, but much better) because it has a very clean UI, supported by a giant (Apple), fast, stable even with large datasets and large figures. Importantly, the process of writing your own notebook should not be so automated. It pays to manually organize results and write down your notes.