Blog
Reducing latency is better for you, better for your customers and cheaper to operate
Data stream processing delivers faster results and a better user experience — powered by ML and automation.
Real time stream processing with Python
Python is the primary language for machine learning applications, but it isn’t easily compatible with streaming data — until now. Introducing the Quix SDK made for Python developers.
Using streaming data to personalize everything from house hunting to healthcare
How in-memory data processing is improving personalized customer experiences across industries
Benchmarking stream processing client libraries
We tested Apache Spark vs Apache Flink vs Quix Streams on performance and flexibility. The results surprised us.
A very detailed comparison of Python stream processing libraries
Dive deep into the performance and limitations of Python client libraries to choose the best stream processing solution for your data.
Implementing stream processing: My experience using Python libraries
I tested three Python client libraries — Apache Spark, Apache Flink, and Quix — on performance, scalability and ease of use. Here’s what I learned.