People at conferences and meetups often ask me what I would recommend to learn X or Y. And I’m always happy to give some suggestions depending on the experience level of the person that asked. Unfortunately, this doesn’t scale very much, so here are my general recommendations on learning something very effective. This time: Python.
A graph(ical) approach towards Bounded Contexts
In this blog post, I want to show how you can get a first impression on how you can cut a monolithic application into separated components that make sense from a business’ perspective. This method can help you to identify meaningful Bounded Contexts…
Visualize Developer Contributions with Stream Graphs
In this blog post, I want to show you how you can visualize the contributions of developers to your code base over time. I came across the Stream Graph visualization and it looks like it would fit quite nicely for this purpose…
Generate fake data for Spring PetClinic with Pandas and Faker
In preparation for a talk about performance optimization, I needed some monstrous amounts of fake data for a system under test. I choose the Spring Pet Clinic project as my “patient” because there are some typical problems that this application does wrong. But this application comes with round about 100 database entries. This isn’t enough at all…
A simple demo on how to use Python Pandas with jQAssistant / Neo4j
I’m a huge fan of the software analysis framework jQAssistant. It’s a great tool for scanning and validating various software artifacts. But I also love Python Pandas as a powerful tool in combination with Jupyter notebook for reproducible Software Analytics.
Combining these tools is near at hand. So I’ve created a quick demonstration for “first contact” 🙂