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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
https://github.com/jupyter/enhancement-proposals/pull/103#is...
Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/
https://github.com/jupyter/enhancement-proposals/pull/103#is...
Papermill is one tool for running Jupyter notebooks as reports; with the date in the filename. https://papermill.readthedocs.io/en/latest/
I think it depends a lot on what your git repository is.
If it's specifically source code for anything that's intended to run, then avoiding including the outputs is a smart move. But then, if that's the case, there's a good chance you'd just be committing a .py file.
I like notebooks because they include output alongisde input. For example, Peter Norvig's Pytudes are all brilliant, quick notebooks that solve a particular puzzle[0]. The code itself might not be that interesting to run (unless you really want to confirm his strategy for wordle checks out) but reading through the notebooks makes for a great experience of simultaneously understanding his thought process, and seeing the solution.
I do a bunch of generative art stuff and have recently been experimenting with using notebooks as quick sketches[1]. I really like the workflow and end up with something like a journal that isn't necessarily intended to be ran repeatedly, but read over, where I can see the visual output created, as well as the method for it.
[0] Norvig's extremely cool pytudes, wordle example: https://github.com/norvig/pytudes/blob/main/ipynb/Wordle.ipy...
[1] My not anywhere near as cool as Norvig's pytudes example: https://github.com/benrutter/jupyter-sketches