Flink jobs, Python experiments, and Java packages
How PyFlink and Java TA-Lib jobs fit different parts of the streaming workflow.
Why this mattered
PyFlink is fast for exploration and close to notebooks, but Java packaging is clearer when the job becomes an operational artifact.
This belongs in the development timeline because RPi Kubernetes is not a single feature. It is a hybrid k3s homelab with an Ubuntu control plane, four Raspberry Pi 5 workers, Cloudflare Tunnel, and a data platform made from Kafka, Flink, Redis Stack, MinIO, DataHub, Airbyte, Polaris, and observability services. The project only became useful once its infrastructure decisions were written down well enough to be repeated.
Design decision
The platform keeps both lanes visible so research can become a session job without losing the learning path.
The practical stack around this decision includes k3s, Kustomize, Helm, Strimzi Kafka, Flink Operator, Redis Stack, RAGFlow, DataHub, Airbyte, Polaris, MinIO, Prometheus, Grafana, Loki, OpenTelemetry, Cloudflare Tunnel, FastAPI, Next.js. I try to keep the interfaces small: configuration describes intent, runtime code owns behavior, and operational notes explain what a future maintainer should check first.
What I would repeat
The split also makes ARM compatibility and image contents easier to reason about.
The repeatable pattern is to make the boring path explicit. For this project that means clear repository boundaries, documented setup, predictable deployment commands, and enough observability to know whether the system is healthy or merely quiet.
Reader takeaway
If you are building something similar, start with the workflow you need to repeat every week. Then add only the platform pieces that make that workflow easier to recover, explain, and extend.