Hi HN, Dan, Hussain and Daniel here… After years of struggling with data pipelines that worked in notebooks but failed in production, we decided to do something about it. We created xorq to eliminate the constant headaches of SQL/pandas impedance mismatch, runtime debugging, wasteful recomputations and unreliable research-to-production deployments that plague traditional pandas-style pipeline workflows. xorq is built on Ibis and DataFusion.

We’d love your feedback and contributions. xorq is [Apache 2.0 licensed](https://github.com/letsql/xorq/blob/main/LICENSE) to encourage open collaboration.

Repo: https://github.com/letsql/xorq

Docs: https://docs.xorq.dev

Roadmap Issues: https://github.com/letsql/xorq

You can get started `pip install xorq`.

Or, if you use nix, you can simply run `nix run github:xorq-labs/xorq` and drop into an IPython shell.

Demo video: https://youtu.be/jUk8vrR6bCw

Here are some vignettes to look into next:

1. MCP Server + Flight + XGBoost: https://docs.xorq.dev/vignettes/mcp_flight_server

2. 1 DuckDB + 2 Writers + 1 Reader: https://docs.xorq.dev/vignettes/duckdb_concurrent

3. OpenAI UDF: https://docs.xorq.dev/tutorials/hn_data_prep

Some features to note:

- Ibis-based multi-engine expression system: effortless engine-to-engine streaming

- Cache expressions with `.cache` operator

- Portable DataFusion-backed UDF engine with first class support for pandas dataframes

- Serialize Expressions to and from YAML

- Easily build Flight end-points by composing UDFs

thanks for checking this out, and we’re here to answer any questions!