Any job processing framework like Spark but in Rust?

This page summarizes the projects mentioned and recommended in the original post on /r/dataengineering

InfluxDB – Built for High-Performance Time Series Workloads
InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
www.influxdata.com
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Stream - Scalable APIs for Chat, Feeds, Moderation, & Video.
Stream helps developers build engaging apps that scale to millions with performant and flexible Chat, Feeds, Moderation, and Video APIs and SDKs powered by a global edge network and enterprise-grade infrastructure.
getstream.io
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  1. datafusion-ballista

    Apache DataFusion Ballista Distributed Query Engine

  2. InfluxDB

    InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

    InfluxDB logo
  3. fluvio

    🦀 event stream processing for developers to collect and transform data in motion to power responsive data intensive applications.

    What are you trying to accomplish? If you are looking for capturing data and transforming on stream and apply time bound calculations, check out: https://github.com/infinyon/flu

  4. polars

    Dataframes powered by a multithreaded, vectorized query engine, written in Rust

    For data frames built on Apache Arrow and: https://github.com/pola-rs/polars/

  5. lance

    Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..

    For Feature Stores check out: https://github.com/eto-ai/lance

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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