Python Meetup with two talks about python usage in a data science environment and the different stages of a python package in this environment.

For the third time we have been hosting the Karlsruhe Python Meetup at Blue Yonder. Both presenters are part of my data engineering team.

A Blue Yonder Python Habitat by Bjoern Meier

This talk was about how Blue Yonder set itself up as a Python software company. In particular, Bjoern showed you how we make our applications available for execution. Questions like

• How do open source packages find their way into the company?
• How do we address the risks of open source usage?
• How do we distribute our internal packages?

have been answered within this talk.

Blue Yonder has an application execution model that is similar to what Azure and others are doing. But it is tailordered much more towards Python and data science applications.

Development Stages of Internal Python Packages by Jakob Herpel

After having learned in the first part how Blue Yonder distributes internal packages, Jakob dove into the path that written code takes to end up as an actual usable Python package. He showed how his team handles cross-cutting issues like

• code style
• testing
• vulnerability scanning

uniformly across artifacts with the help pytest, Jenkins, sentry, black, flake8 and pip-tools.