The Quix blog
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.
How machine learning and AI are improving cybersecurity
Playing catch-up to online fraud? Monitor your data in real time. AI + streaming data analytics stops cyberthreats faster.
The race to build with streaming data: Can your dev team keep pace?
Take Quix for a test drive: we built a no-coding-required driving game using Quix’s stream processing to help you experience the simplest way to handle streaming data.
Frequently asked questions about data streaming
Streaming data is a rapidly evolving field. We answer the top 14 most frequently asked questions about why, how and when to use data streaming technology.
Real time stream processing with Kafka and Python
Start processing real-time data in Python in 10 minutes with Quix. This quick start guide with source code shows how.
How to build a powerful project free with Quix
Build fast, powerful and free with Quix. We built a Twitter sentiment analysis tool that can process 4 million tweets for month free. Plus, detailed and transparent pricing for when you’re ready to go bigger.
Why data scientists can’t take full advantage of real time data streaming
Real time data streaming has obvious benefits for data scientists. However, there is a significant obstacle: most libraries come in Java and Scala, while most data scientists work exclusively in Python. Here’s why real-time data streaming has (until now) been an uphill endeavor.
The paradigm shift in streaming data processing: brokers, streams and tables
Discover the three major shifts that streaming data processing requires, and how that delivers insights faster and more efficiently than the traditional batch data processing.