You learned the individual tools. You learned the deployment strategies. Now Chapter 11 of Data Engineering with Python by Paul Crickard puts it all together. This is the chapter where you build a complete, production-grade data pipeline from start to finish.
You built a pipeline. It works on your machine. It runs on a schedule. Data goes in, data comes out. Ship it, right?
In Part 1, we covered how Funderburk moves from Jupyter notebooks to a production-ready project structure. Docker, uv, SuperComponents, dual Elasticsearch. Now comes the part that actually tells you if your RAG pipeline is any good: systematic evaluation with RAGAS and continuous monitoring with Weights and Biases.
Chapter 6 is where the book shifts gears. Hard. Funderburk basically says: “Cool, you built a RAG pipeline. It works on your laptop. Now what?”