# quixio/quix-streams

Python Streaming DataFrames for Kafka

Repository: https://github.com/quixio/quix-streams
Canonical: https://ross.abutalabs.com/products/quix-streams
Homepage: https://docs.quix.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: kafka, python, stream-processing, data-engineering, data-science, machine-learning, real-time-data-processing, streaming-data, streaming-data-pipelines, streaming-data-processing, time-series-data, data-intensive-applications, event-driven-architecture, stream-processor
Last push: 2026-08-26T14:20:47+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 94, longevity 98
- inputs: {"age_days": 1385, "days_push": 7, "days_rel": 40, "gap_med": 10.0, "n_releases_24m": 43}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1568, forks 112 (observed 2026-08-28T04:05:05.058152+00:00)

## What it is
Quix Streams is a pure Python framework for building real-time data pipelines and event-driven applications on Apache Kafka using a Streaming DataFrame API. It provides fault-tolerant stateful processing, windowing, joins, serialization (JSON, Avro, Protobuf), and exactly-once guarantees without requiring server-side clusters.

## Use cases
- process kafka streams in python
- build real-time data pipelines
- streaming etl from kafka topics
- stateful stream processing with windowing
- build event-driven microservices on kafka
- real-time feature engineering for machine learning
- transform kafka messages with pandas-like api

## When to choose
- you want pure Python Kafka stream processing without Java wrappers
- you need stateful operations like windowing, joins, and group-by on streams
- you want a lightweight library with no server-side cluster to manage
- you need exactly-once processing guarantees via Kafka transactions

## When to avoid
- you need a full cluster-based stream processing engine like Flink or Spark Streaming
- your data source is not Kafka and you don't want to write custom connectors
- you only need simple batch ETL rather than streaming

## Facets
- artifact type: library
- maturity: active
- function: streaming, etl, serialization, message-queue, data-science, machine-learning
- domain: big-data, analytics, machine-learning, apis
- platform: python, cross-platform
- tags: kafka, stream-processing, streaming-dataframes, event-driven-architecture, data-pipelines, stateful-processing, windowing, exactly-once, data-engineering, real-time

## Member repositories
- quixio/quix-streams (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.058152+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:58:58.120312+00:00, confidence not recorded.
  - readme: https://github.com/quixio/quix-streams (fetched 2026-08-28T04:05:05.058152+00:00, sha 4fda304b12f7)
  - homepage: https://docs.quix.io (fetched 2026-08-29T11:28:48.543113+00:00, sha 926f96d142ee)
- Data as of 2026-08-30T08:39:29.467469+00:00.