The person asking for repository examples has already done the hard part: they picked a modern, coherent stack and got it running. That is more than most teams manage. PyIceberg for storage, Polars for analysis, Prefect for orchestration, Marimo for visualization, these tools work together because they were built for the same generation of data problems. Legacy warehouses like BigQuery solved yesterday's questions. This stack answers today's.
What this user's search reveals, though, is something deeper than a tooling decision. The user has a working proof of concept but is now searching for "best practices" in public repositories. That search is understandable, but it points to a mismatch between how we learn data engineering and how the field actually moves. Best practices for a stack this new do not exist in a polished, canonical form yet. They are being written right now, in pull requests and personal notebooks and conference talks. The repositories that contain clean examples of PyIceberg plus Polars plus Prefect are likely three months old and already slightly outdated. That is not a flaw in the stack. It is a signal that the user is early, and early means they get to define the practices rather than inherit them.
Our opinion is plain: this user should stop looking for perfect repositories and start treating their own PoC as the reference implementation. The value of a modern, composable stack is that you can iterate on it without waiting for a vendor to ship a feature. BigQuery forced you to work inside its walls. PyIceberg and Polars give you a warehouse that lives in your object store and a dataframe engine that runs wherever you want. Prefect lets you orchestrate that without a proprietary scheduler. Marimo turns notebooks into reactive documents instead of fragile linear scripts. The user already proved the pieces connect. Now they need to prove the pieces work under their specific load, with their specific schemas, for their specific questions.
That is the real task. Find the bottlenecks. Push the join sizes. Measure how PyIceberg handles concurrent writes. See whether Polars lazy mode fits into Prefect flows without surprises. Write the integration tests that the public repos do not have. Then share what you learned. The community around this stack is hungry for exactly that kind of practical knowledge. A single blog post or a well-documented repo from someone who actually ran the stack against real data is worth more than fifty example repos that show only the happy path.
The user asked for high-quality repositories. We suggest they become one.