Kafka Streams in Python: Faust vs. Quix Streams

Apache Kafka Streams processes Kafka records as they arrive. It can transform and join records, calculate results over time, and restore processing state after a restart. Kafka Streams does not provide a Python API. For similar stream-processing tasks in Python, two Kafka-focused options are Faust Streaming and Quix Streams.

This guide compares verified capabilities and package versions, then uses GitHub repository metrics as a separate risk check. If your application only needs to produce or consume records, see Kafka Python Clients 2026: Which Should You Choose?.

Quick decision

When to use a stream-processing framework

Stream processing means handling events continuously as they arrive instead of waiting for a complete batch. A Kafka consumer can handle simple record-by-record work; a stream-processing framework adds state, windows, joins, and recovery.

Use a framework when a result depends on earlier events, event time, or another stream—for example rolling metrics, deduplication, or combining two event streams. If every record can be processed independently, a Kafka Python client is usually enough.

Python stream-processing tools generally fall into three categories:

This article focuses on Faust Streaming and Quix Streams because their Kafka-focused library model is the closest match to Kafka Streams.

Faust Streaming vs. Quix Streams

Capabilities verified against official documentation and package artifacts: 13 Aug 2026. For Faust Streaming, the comparison uses the installable PyPI version 0.13.2; v0.14.1 is currently available only on GitHub.

Both default to at-least-once processing and support Kafka exactly-once processing.

Faust Streaming

Faust Streaming is the active community fork of Robinhood's Faust, an asyncio-based stream-processing library for Kafka. It processes records with async agents and stores state in Tables.

Capabilities and limitations

Tables recover from Kafka changelogs and support time windows. RocksDB is recommended for production state. In 0.13.2, built-in table joins are not implemented, while Schema Registry support and connectors require custom code. Kafka exactly-once does not cover HTTP or database writes.

When to choose Faust Streaming

Choose Faust Streaming when the processor is built around asyncio and needs to await HTTP, database, or other network calls directly.

Quix Streams

Quix Streams is a Python stream-processing library for Kafka. It uses a DataFrame-style API to build processing pipelines.

Capabilities and limitations

Quix Streams includes windows, joins, Schema Registry serializers, and pre-built Source/Sink connectors, which are currently beta. Its StreamingDataFrame callbacks are synchronous, and exactly-once processing does not cover writes to external sinks.

When to choose Quix Streams

Choose Quix Streams for a new stateful Kafka pipeline that needs joins, Schema Registry, or built-in connectors.

GitHub repository metrics

The tables below compare both libraries' GitHub activity, maintenance, and public usage using the same metrics and assessment rules. Snapshot: 13 Aug 2026.

Project overview

Activity

Release figures cover GitHub Releases, not PyPI.

Maintenance

PR merge distribution counts non-bot mergedBy accounts, so automated merges may undercount human reviewers. Issue closure rates use issues opened 90–180 days before the snapshot, giving each issue a full 90-day window.

Public usage and interest

GitHub dependents are approximate public-repository counts.

Overall repository signals

• Faust Streaming is active and merges new PRs quickly, but two people handled all recorded merges in the past year and one account handled 98%.
• Quix Streams has broader maintainer participation, but its 15 open PRs have a median age of 275 days.

Both libraries show mixed public usage and interest. Choose by technical fit first, then use these repository signals to assess maintenance risk.

Test a streaming pipeline with Kafma

A stream-processing test should verify both sides of the pipeline: publish a few test records, then inspect the result written to Kafka.

We use Kafma—the desktop Kafka UI to publish three orders and inspect their aggregated result. The example uses Quix Streams to match this guide's starting point for a new stateful pipeline and to demonstrate its built-in count-window API. It sums each customer's orders in groups of three.

Run the pipeline

Install Quix Streams:

python -m pip install "quixstreams==3.25.0"

Save this pipeline as order_totals.py:

from quixstreams import Application
from quixstreams.dataframe.windows import Sum

app = Application(
    broker_address="127.0.0.1:9092",
    consumer_group="kafma-order-totals-v1",
    auto_offset_reset="latest",
)

input_topic = app.topic("order-events", value_deserializer="json")
output_topic = app.topic(
    "customer-order-totals",
    key_serializer="str",
    value_serializer="json",
)

sdf = app.dataframe(input_topic)
sdf = sdf.group_by("customer_id")
sdf = (
    sdf.tumbling_count_window(count=3)
    .agg(total_amount=Sum("amount"))
    .final()
)
sdf = sdf.print(metadata=True)
sdf.to_topic(output_topic)

if __name__ == "__main__":
    app.run()

Start the pipeline before publishing the test records:

python order_totals.py

Publish records with Kafma

In Kafma, publish these three JSON records to order-events. Use the listed Kafka key for each record:

Inspect the output with Kafma

After you publish the third record, the pipeline prints the total in the terminal and writes it to customer-order-totals.

Open customer-order-totals in Kafma. The result should have key c_10, "total_amount":36, and the window's start and end timestamps.

Frequently asked questions

Is Faust the same as Faust Streaming?

No. This guide compares Faust Streaming, the community-maintained fork installed as faust-streaming. Its Python import is still faust; do not install the original faust package for the versions and capabilities compared here.

Can Quix Streams await HTTP or database calls in the pipeline?

StreamingDataFrame callbacks are synchronous. They can call HTTP or database clients, but the call blocks message processing until it returns; they cannot directly use await. Use a Quix sink for batched destination writes, or choose Faust Streaming when async per-record I/O is central to the processor.

Does exactly-once processing cover HTTP or database writes?

No. In this comparison, exactly-once processing applies to Kafka input offsets, framework state, and Kafka output records. HTTP calls and database writes need destination-specific idempotency or transactional handling.

Are Faust Streaming or Quix Streams drop-in replacements for Kafka Streams?

No. They provide similar concepts, including windows, state, and Kafka-backed recovery, but their APIs, state formats, configuration, and processing behavior differ. Migrating an existing Kafka Streams application requires porting the topology and rebuilding or replaying its state.

Conclusion

For a new stateful Kafka pipeline, start with Quix Streams, especially when you need joins, Schema Registry, or built-in connectors. Choose Faust Streaming for asyncio-based processors that need direct network I/O and do not require built-in Schema Registry support or connectors.

Choose by technical fit first, then use repository activity and maintenance signals to compare long-term risk. If records can be processed independently and you do not need framework-managed state, windows, or joins, compare Python Kafka clients instead.

This guide is maintained by the team behind Kafma.