In a head-to-head test, Quix outperforms Spark and Flink

Find out why data stream processing works better — faster and more efficiently — on Quix, compared to industry incumbents Apache Spark and Apache Flink.

RAM comparison graph
CPU comparison graph

Results: How three client libraries handle stream processing at scale

See detailed performance metrics and code behind stream processing tasks executed on Quix, Spark and Flink. We tested performance, efficiency, ease of use and scalability.

Read the report →

A (very) detailed comparison of Quix, Spark and Flink

To successfully use stream processing, select the best architecture for your business. This report offers an exhaustive comparison of the pros and cons for each client library to help you make the most of your streaming data.

Compare the platforms →

Benchmarking stream processing client libraries
Stream processing python libraries

Our experience testing each client library

Go behind the scenes with one developer as we set up and test Quix, Spark and Flink. A key question is usability, because time wasted wrangling complex infrastructure means less time building great data-driven products.

See the insider’s view →

Try Quix for yourself with a no-code demo

Test drive Quix to see streaming data processing in action. No setup or coding required — all you need is your computer and mobile phone. With infrastructure that normally takes teams months to build, this demo was created in just a few days with Quix.
The stream processing revolution

Trending now

Why the data pipeline is changing everything

Find out why analysts and market-watchers agree that traditional data processing must be replaced by stream processing. It’s happening now as innovators embrace new opportunities for greater personalization, automation and revenue.