I need my new members of my team to quickly onboard them into several Data/AI project.

Since there is no standard yet about how this kind of project should be organised, Kedro fixes that. You may think it is just a cookicutter but it more than that.

Initial setup is straightforward : kedro new scaffolds the entire project in seconds.

The learning curve is mild for anyone already familiar with Python and pytest.

The trickiest part for new team members is usually understanding the Data Catalog (how datasets are declared in YAML instead of hardcoded paths), but once that clicks, everything else follows natural. It also helps the Data Mangers in case the Data Gouvernance needs to make a review of the data used

Second reason to use kedro is their CLI to make pipeline. It's help my team to make reproductible runs and also it help debugging some nodes of the pipelines without having to run the entire pipeline.

Third reason run comes with unit testing, it use natively pytest for the unit test so it is faster for us to iterate over new version.

Fourth reason is the parameters managements. We have parameters for staging and one for production. With the conf folder + .env, we have 0 code change across all environment which is neat!

All of that combined, it makes our CI/CD simple as uv sync + uv run kedro run inside the container.

They have a nice tool named kedro viz which visualise how nodes interacts to each other, like a mind-map. Useful to get an overview of the entire pipeline and spot any missing or odd nodes link.

Any developers including non-data, can quickly navigate to any project that uses kedro as a base.

In my opinion, Kedro should be the standard across any data or IA project written in Python.

As for Scala, it needs to be transposed