[PR #2358] [CLOSED] Add Quix Streams (stream processing w Kafka) #13117

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GiteaMirror · 2026-04-27T02:32:22-05:00

GiteaMirror commented

2026-04-27 02:32:22 -05:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/2358
Author: @merlin-lacuna
Created: 2/20/2023
Status: ❌ Closed

Base: master ← Head: master

📝 Commits (1)

94e0579 Add Quix Streams (stream processing w Kafka)

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

What is this Python project?

Quix Streams is a library for developing real-time streaming applications focused on time-series data and high-performance. It was developed at McLaren for processing huge sensor data streams from F1 racing cars, and is designed to be used for high-frequency telemetry services when you need to process high volumes of time-series data with up to nanosecond precision. It uses a message broker such as Apache Kafka (instead of a database) so you can process time-series data with high performance and resource savings.

Using Quix Streams, you can:

Produce time-series and event data to a Kafka Topic.
Consume time-series and event data from a Kafka Topic.
Process data by consuming it from one Kafka Topic, process it, and then producing the results back to another Kafka Topic.
Attach metadata to streams and group streams by metadata attributes.

What's the difference between this Python project and similar ones?

What's distinct about Quix Streams is its processing API—although it’s a client library, it’s incredibly powerful.

On the feature spectrum, it lies somewhere between Kafka client libraries such as Faust and kafka-python (which are easy to use but have a basic feature set) and full-fat stream processing engines like Flink and Spark (which are powerful but have a steep learning curve).
Includes many built in functions and data types such as ParameterData (for time-series data) and EventData, buffering and state management for rolling window calculations, serialization support for video and audio streams, and more.
The main difference is that processing is done in Python rather than Java, and it runs in a client environment rather than a server-side engine.

Anyone who agrees with this pull request could submit an Approve review to it.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/2358 **Author:** [@merlin-lacuna](https://github.com/merlin-lacuna) **Created:** 2/20/2023 **Status:** ❌ Closed **Base:** `master` ← **Head:** `master` --- ### 📝 Commits (1) - [`94e0579`](https://github.com/vinta/awesome-python/commit/94e057903b72ac8eeb5593016f74f012729211eb) Add Quix Streams (stream processing w Kafka) ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description ## What is this Python project? [Quix Streams](https://github.com/quixio/quix-streams) is a library for developing real-time streaming applications focused on time-series data and high-performance. It was developed at McLaren for processing huge sensor data streams from F1 racing cars, and is designed to be used for high-frequency telemetry services when you need to process high volumes of time-series data with up to nanosecond precision. It uses a message broker such as Apache Kafka (instead of a database) so you can process time-series data with high performance and resource savings. Using Quix Streams, you can: * Produce time-series and event data to a Kafka Topic. * Consume time-series and event data from a Kafka Topic. * Process data by consuming it from one Kafka Topic, process it, and then producing the results back to another Kafka Topic. * Attach metadata to streams and group streams by metadata attributes. ## What's the difference between this Python project and similar ones? What's distinct about Quix Streams is its processing API—although it’s a client library, it’s incredibly powerful. * On the feature spectrum, it lies somewhere between Kafka client libraries such as Faust and kafka-python (which are easy to use but have a basic feature set) and full-fat stream processing engines like Flink and Spark (which are powerful but have a steep learning curve). * Includes many built in functions and data types such as `ParameterData` (for time-series data) and `EventData`, buffering and state management for rolling window calculations, serialization support for video and audio streams, and more. * The main difference is that processing is done in Python rather than Java, and it runs in a client environment rather than a server-side engine. -- Anyone who agrees with this pull request could submit an *Approve* review to it. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-27 02:32:22 -05:00

GiteaMirror closed this issue

2026-04-27 02:32:23 -05:00

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Reference: github-starred/awesome-python#13117