How to: Data processing
* [amv-dev/yata](https://github.com/amv-dev/yata) - high performance technical analysis library [![Build Status](https://img.shields.io/github/workflow/status/amv-dev/yata/Rust?branch=master)](https://github.com/amv-dev/yata/actions?query=workflow%3ARust)
* [bluss/ndarray](https://github.com/rust-ndarray/ndarray) - N-dimensional array with array views, multidimensional slicing, and efficient operations
* [cocoindex](https://github.com/cocoindex-io/cocoindex) - ETL framework to build fresh index
* [datafusion](https://github.com/apache/datafusion) - DataFusion is a very fast, extensible query engine for building high-quality data-centric systems in Rust, using the Apache Arrow in-memory format.
* [GoPlasmatic/datalogic-rs](https://github.com/GoPlasmatic/datalogic-rs) [[datalogic-rs](https://crates.io/crates/datalogic-rs)] - High-performance, type-safe JSONLogic evaluation engine in Rust, ideal for business rules and dynamic filtering.
* [kernelmachine/utah](https://github.com/kernelmachine/utah) - Dataframe structure and operations
* [pathwaycom/pathway](https://github.com/pathwaycom/pathway) - Performant open-source Python ETL framework with Rust runtime, supporting 300+ data sources.
* [pg_analytics](https://github.com/paradedb/paradedb/tree/dev/pg_analytics) - PostgreSQL extension that accelerates analytical query processing inside Postgres to a performance level comparable to dedicated OLAP databases.
* [pg_lakehouse](https://github.com/paradedb/paradedb/tree/dev/pg_lakehouse) - PostgreSQL extension that transforms Postgres into an analytical query engine over object stores like AWS S3/GCS and table formats like Delta Lake/Iceberg.
* [pola-rs/polars](https://github.com/pola-rs/polars) - Fast feature complete DataFrame library [![Lint Rust](https://github.com/pola-rs/polars/actions/workflows/lint-rust.yml/badge.svg)](https://github.com/pola-rs/polars/actions)
* [weld-project/weld](https://github.com/weld-project/weld) - High-performance runtime for data analytics applications