Hey HN! I am one of the people working on Bytewax (https://github.com/bytewax/bytewax https://github.com/bytewax/bytewax). Bytewax came out of our experience wo

Hey HN! I am one of the people working on Bytewax (https://github.com/bytewax/bytewax https://github.com/bytewax/bytewax). Bytewax came out of our experience working with ML infrastructure at GitHub. We wanted to use Python because we could move fast, the team was very fluent in it, and the rest of our tooling was Python-native already. We didn't want to introduce JVM-based solutions into our stack because of the lack of experience and the friction we had trying to get Python-centric tooling working with existing solutions like Flink. In our research, we found Timely Dataflow (https://timelydataflow.github.io/timely-dataflow/ https://timelydataflow.github.io/timely-dataflow/, https://news.ycombinator.com/item?id=24837031 https://news.ycombinator.com/item?id=24837031) and the Naiad project (https://www.microsoft.com/en-us/research/project/naiad/ https://www.microsoft.com/en-us/research/project/naiad/) as well as PyO3 (https://github.com/PyO3/pyo3 https://github.com/PyO3/pyo3) and we thought we found a match made in heaven :). Bytewax leverages both of these projects and builds on them to provide a clean API (at least we think so) and table stakes features like connectors, state recovery, and cloud-native scaling. It has been really cool to learn about the dataflow computation model, Rust, and how to wrangle the GIL with Rust and Python :P. Would love to get your feedback :). `pip install bytewax` to get started. We have a page of guides (https://www.bytewax.io/guides https://www.bytewax.io/guides) with ready-to-run examples.