I’m continuing my series on running the test suite for each Pull Request on Kubernetes. In the previous post, I laid the groundwork for our learning journey: I developed a basic JVM-based CRUD app, tested it locally using Testcontainers, and tested it in a GitHub workflow with a GitHub service container. This week, I will raise the ante to run the end-to-end test in the target Kubernetes environment.

Recently, I designed a simple metrics-tracking system. A Python script queries different providers' APIs for metrics, e.g., Twitter, GitHub, etc. The idea is to run this script each day, store them in Google BigQuery and provide an excellent data visualization in Google Data Studio. I’m a big fan of automation, so I’m using GitHub Actions. Accessing Google Cloud with a Service Account I query the different APIs with different Python libraries.