The Quix blog

Data stream processing paired with machine learning delivers big public benefits — with applications in healthcare, food security, public safety and transportation.

Here at Quix, we use … Quix! How we built a customer-centric user journey pipeline to automate real time analytics.

An in-depth tutorial on how one data scientist created a real-time predictor of bike availability — a use case you can apply to any fleet or mobility solution.

The October 2021 edition of The Stream: covering this month in stream processing on the internet.

Take a machine learning project using streaming data from idea to production deployment. It takes just 15 minutes with this step-by-step blog and video tutorial.

Data stream processing delivers faster results and a better user experience — powered by ML and automation.

We tested Apache Spark vs Apache Flink vs Quix Streams on performance and flexibility. The results surprised us.

Dive deep into the performance and limitations of Python client libraries to choose the best stream processing solution for your data.

I tested three Python client libraries — Apache Spark, Apache Flink, and Quix — on performance, scalability and ease of use. Here’s what I learned.

Playing catch-up to online fraud? Monitor your data in real time. AI + streaming data analytics stops cyberthreats faster.

Streaming data is a rapidly evolving field. In this article, we answer the most frequently asked questions about why, how and when to use data streaming technology.

Start processing real-time data in Python in 10 minutes with Quix. This quick start guide with source code shows how. 

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.

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.

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.

Understand key Kafka concepts and how it delivers unparalleled speed and capabilities for real time data stream processing

Handling streaming data is not for the faint of heart or thin of wallet. In this post, Quix CTO Tomáš Neubauer digs into why streaming data can be so difficult to set up, complicated to manage, and costly for teams.

How to become a data scientist (or develop your skills if you're already one). Our senior data scientist shares his thoughts on what it takes to start a career in this area.

Financial fraud is now the most prevalent crime in the UK, costing the industry £190 billion per year. Globally, this figure was an eye-watering $1.45 trillion in 2019. In this case study I show you how I lost thousands and how banks could have avoided most of this through real-time fraud detection.

Today, my co-founders Tomas Neubauer, Peter Nagy, Patrick Mira Pedrol and I are excited to introduce Quix, the cloud data platform we started developing just over a year ago.

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