If you have streaming data as a source, I built a side project to write streaming data to s3 in iceberg format:
https://sql-flow.com/docs/tutorials/iceberg-sink
https://github.com/turbolytics/sql-flow
I realize it's not quite what you asked for but wanted to mention it. I'm surprised at lack of native iceberg write support in these tools.
Pyiceberg though was quite easy to use, arrow-based API was very helpful as well.
However, my issue is the need to introduce one more tool. I feel that without a single tool to read and write to Iceberg, I would not want to introduce it to our team.
Spark is cool and all but it requires quite a bit of effort to properly work. And Spark seems to be the only thing right now that can read and write to Iceberg natively with a SQL like interface.
Also no cluster, no JVM. Just `pip install daft` and go. Runs locally (as fast as DuckDB for a lot of workloads; faster, if you have S3 cloud data access) and also runs distributed if you have a Ray cluster you can point it at
(Disclaimer: I work on it)