papermill VS Airflow

Pandas docs > Comparison with spreadsheets: https://pandas.pydata.org/docs/getting_started/comparison/co...

Pandas docs > I/O > Excel files: https://pandas.pydata.org/docs/user_guide/io.html#excel-file...

nteract/papermill: https://github.com/nteract/papermill :

> papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. [...]

> This opens up new opportunities for how notebooks can be used. For example:

> - Perhaps you have a financial report that you wish to run with different values on the first or last day of a month or at the beginning or end of the year, using parameters makes this task easier.

"The World Excel Championship is being broadcast on ESPN" (2022) https://news.ycombinator.com/item?id=32420925 :

> Computational notebook speedrun ideas:

There is Papermill ... https://github.com/nteract/papermill

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/

You may be interested in papermill to address the parametrized analysis problem [1]. I think (but I'm not positive) this is what the data team at a previous job used to automate running notebooks for all sorts nightly reports.

[1] https://papermill.readthedocs.io/en/latest/#

I'm using Papermill to operationalize Notebooks (https://github.com/nteract/papermill), it e.g. also has airflow support. I'm really happy with papermill for automatic notebook execution, in my field it's nice that we can go very quickly from analysis to operations -- while having super transparent "logging" in the executed notebooks.

papermill bcpandas fastapi

But you can execute your notebook using Jupyter-run or papermill.

As a first option, we will use Papermill, which has a Python API that allows us to run different notebooks using some functions:

Papermill can also target cloud storage outputs for hosting rendered notebooks, execute notebooks from custom Python code, and even be used within distributed data pipelines like Dagster (see Dagstermill). For more information, see the papermill documentation.

Papermill Source Code

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte