May 10, 2021

Quix: The in-memory data stream processing platform for Python professionals

Announcing Quix, the first in-memory data stream processing platform for Python professionals looking to build real-time data applications.

Introducing Quix colorful banner.
Quix brings DataFrames and the Python ecosystem to stream processing. Stateful, scalable and fault tolerant. No wrappers. No JVM. No cross-language debugging.

Our library is open source—support the project by starring the repo.

Announcing Quix

Today we  announced that we raised a £2.3M Seed round led by Project A Ventures, with participation from Passion Capital and a host of prominent angel investors including Frank Sagnier (CEO, Codemasters), Ian Hogarth (Co-founder, Songkick), Chris Schagen (CMO, Contentful) and Michael Schrezenmaier (CEO, Pipedrive). With our Seed investment, Sam Cash joins our board of directors and Malin Posern (Passion Capital) and Leo Lerach (Project A) join as board observers.

At Quix, we believe that it will soon be essential for every organization to automatically action data within milliseconds of it being created. Whether it’s building hyper-personalized experiences, automating mobility and industrial machinery, deploying smart wearables in healthcare, or detecting fraud faster, the ability to run complex machine learning on live streams of data and immediately respond to rapidly changing environments is critical to delivering better experiences and outcomes to people.

While the past decade has seen a surge in big data technologies, they are too difficult to use and too slow to respond to be useful for streaming applications. Current systems are all architected around a database, with teams working to combine multiple separate technology components into platforms, which can extract this data and serve it to a model for production. We know from experience that the database is in the way of teams who want to build low-latency data-driven applications.

Quix is the first complete streaming analytics platform architected natively around a message broker. Developers use a suite of APIs to stream data in and out of our fully managed Kafka topics and work with our Python client library and serverless compute environment to deploy real-time ML models directly to the bleeding edge of live data in the broker. Finally, we provide a data catalog that records every bit of data in the exact context as it was when live streamed, this helps data scientists simulate live environments when training models to ensure they work right the first time, every time.

We are focused on Python since it’s the language for data science and is fast becoming the de-facto language among a growing community of citizen developers the world over. These developers are most in need of enabling platforms. Quix provides the platform, out of the box, that helps organizations operationalize their real-time data science initiatives, faster.

Quix was built by experts in streaming data. The founders – Michael Rosam (CEO), Tomas Neubauer (CTO), Peter Nagy (Head of Platform) and Patrick Mira Pedrol (Head of Software) – worked together at the bleeding edge of real-time data processing in McLaren Technology Group where they developed and commercialized systems that now help F1 teams process huge volumes of data in-flight, live during the race.

Starting with a clean sheet, the team was able to build the no-compromise streaming analytics platform that lets every developer build streaming applications, faster. Today, together with our funding announcement, we are excited to announce the public beta launch of the Quix Portal, providing developers with free access to a streaming analytics platform that removes all barriers to building and operationalizing real-time ML & AI applications.

Our rapidly growing team is accelerating the development of our ambitious roadmap, including an open-source community library of models and services, and multi-cloud and multi-region support. We’re also excited to continue integrating with existing data-science tools and third-party data components like message brokers, databases and data lakes. If you’re interested in working on any of these challenges, Quix is hiring across the board.

And finally, while it is exciting to build a new company, it has been even more exciting to see how the Quix Portal is being used to build applications that previously would have required years of investment in complex infrastructure, using just a few lines of Python code. We’ve been honored to witness the explosion of streaming ML & AI applications from our early adopters, from racing cars and electric vehicles to COVID testing and wearable health tech, right through to personalized financial services and smart factories, no industry will be left unaffected by the streaming analytics revolution.

If you’re as excited about Quix as we are, we’d love to hear from you. Sign up for a free Quix account, build an integration to your data source, join the Slack community and say hello, or apply to join our growing team today!

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