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Quix vs ksqlDB

The pure Python ksqlDB alternative

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Why Quix?

Work in Python from start to finish

Experience a truly unified approach that keeps you in the Python ecosystem throughout the entire development process. Move seamlessly from data exploration in Jupyter notebooks to production-ready code, without tedious language conversions.

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Do faster feature engineering

Say goodbye to UDFs and SQL-Python mashups. Write sophisticated processing functions purely in Python using any ML or data science library you like. Optimize performance with unlimited vertical and horizontal scaling with no fixed limit on the number of processes.

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Integrate ML models more efficiently

Eliminate the need to run your ML models behind REST APIs or in UDFs. Deploy ML models with the same one-click workflow as your feature calculation functions and run your ML models in the same environment as the rest of your pipeline.

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Quix vs ksqlDB

Feature
Quix
ksqlDB
Native integration to any Kafka
✅
✅
Pure Python for creating, producing and consuming streams
✅
⛔️
Full control over your logic
✅
⛔️
Ability to integrate any external Python library like Pandas and NumPy
✅
⛔️
Ability to move seamlessly from Jupyter to application code
✅
⛔️
Performant and fault-tolerant checkpointing for fast failure recovery
✅
⛔️
Fast shuffle sorting and data partitioning
✅
⛔️
Ability to use a custom state store for stateful processing
✅
⛔️
Quix has the best CI/CD processes in place to simplify deployment and development for engineers. Way better than any other platform I've used.
Carey McLean

Carey McLean

Senior Software Engineer at TIGER 21

Quix Streams is one of the more interesting options because it's more or less Faust 2.0 - pure Python with the annoying bits handled.
Ben Gamble

Ben Gamble

Field CTO at Ververica

Quix saved us from hiring a whole data engineering team to build a real-time predictive maintenance application.
Jonathan Wilkinson

Jonathan Wilkinson

CTO at Airedale

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