A self driven community of Python enthusiasts spreading good vibes.

Event details

Back to the office at Codebeez

Wednesday 16 March 2022 18:30

Hello Pythonistas! After the false start last year we are back to Offline events! In a few days we are hosting the first meetup of 2022 at the Codebeez offices.

About Codebeez

Codebeez [1] is the one and only 100% Python consulting company in The Netherlands. Only that makes us already unique and very special...we eat, drink, dream, do, enjoy and love Python. Our hard working and smart Beez develop all kinds of advanced solutions using Python and some honey: Backend, Web, DevOps, Cloud, Data Engineering, Machine Learning Engineering. And we do this for a very diverse group of clients in industries like: healtcare, (public) transportation, energy, telco, retail, finance, governmental for example.

To Bee or not to Bee! Meet us at:


18:00 Doors open! let's share some food and drinks thanks to our host.
19:00 First talk: "My code is slow, what do I do?" -- Olga Sentemova
19:30 Second talk: "Elegant CICD for Databricks Notebooks: entrypoints for Python applications." -- Luuk van der Velden
20:00 Small break
20:15 Lightning talks?
20:30 Networking
21:00 Closing time

My code is slow, what do I do?

About Olga Sentemova

Python developer for more than 5 years with QA background. Work at New Motion, live in Amsterdam.


Python is not known for being the fastest language. It means that we, as developers, should be more careful when writing code. In this talk, we'll learn how to use the tools that help profile the code to find bottlenecks.

Elegant CICD for Databricks Notebooks: entrypoints for Python applications

About Luuk van der Velden

After a PhD in Neuroscience I decided to go for Python in Business. Python allows me to combine my experience as an engineer and scientist in various data driven applications for clients. I am engineering on Azure, my cloud of choice, as part of the Codebeez Python geeks. With Python on Azure I enjoy interactions between my roles as a Data/ML Engineer and Secure Application Developer. I create privacy aware big data architectures in Python and PySpark on infrastructure build with Azure DevOps and Terraform.


Notebooks are the primary runtime on Databricks from data science exploration to ETL and ML in production. This emphasis on notebooks calls for a change in our understanding of production quality code. We have to do away with our hesitancy about messy notebooks and ask ourselves:

  • How do we move notebooks into our production pipelines?
  • How do we perform unit and integration tests on notebooks?
  • Can we treat notebooks as artifacts of a DevOps pipeline?
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