Quix | The Airflow alternative for streaming

Accelerating test and development at:

Easy user interface and pure Python for streaming pipelines

Develop in pure Python

No Java dependencies, no more hassle debugging your Spark or Flink jobs. Use Python to create your own tasks and workflows. Import your favourite Python libraries for real-time processing and enjoy the ease of developing, testing and debugging your application in pure Python.

Quix is based on Docker so it's easy to add system dependencies or libraries to support your application. Quickly get started using pre-configured transformation and connector templates. Integrate your data with our open source plug-and-play connectors or build your own. This makes Quix easy to apply to your current infrastructure on AWS, GCP, Microsoft Azure and many other third-party services.

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Event-driven design

No need for clunky sensors or a scheduler. Quix is event-based at its core. This means workflows that respond to real-time events as soon as they happen. Move from scheduling batches to real-time processing by building asynchronous workflows with event-based triggers. An event-driven approach is more efficient than traditional batch processing because event handlers can be built to react to new published information in real time. This allows you to model complex business workflows that are highly performant and scalable with strong backup and recovery characteristics.

Quix helps you to reinforce software engineering and microservice best practices like modularity and reusability. This helps you to cut your development time and infrastructure bill.

Each task in a Quix pipeline is an individual service in your Git repo. This means that you can build up a library of templates that teams can reuse. Quix provides a visual DAG interface and also defines your pipeline in a YAML file so you have versioned pipelines as code, which ensures consistency and repeatability across development and production environments.

Serverless event streaming infrastructure

Building real-time pipelines on Apache Kafka is notoriously hard because you also need to configure additional processing infrastructure like Spark or Flink and Kafka Connect for integrating with data sources and sinks.

Now you can forget about setting up all the individual components. Quix provides serverless Apache Kafka, Kafka Connect, Docker, CI/CD, and connectors that run in a K8’s package. You can self-host Quix on your cloud or use a fully hosted and managed service built on Azure, AWS or GCP.

However you deploy your pipelines, you’ll be abstracted off of the underlying infrastructure such that building a pipeline requires just a few clicks to configure dev, staging or prod environments. And if you need more processing power you can scale each app horizontally with replicas, all without changing a line of code.

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

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

Field CTO at Ververica

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

Jonathan Wilkinson

CTO at Airedale

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Real-time data pipelines made simple