The Quix blog

How to make data processing more resilient for mission-critical applications: it starts with better (and easier to manage) data pipelines.

The January 2022 edition of The Stream: covering this month in stream processing on the internet.

Move over, subscription pricing. Usage-based pricing maximizes the value of a service for product-led growth, building trust and transparency in the process.

Five industry experts forecast how our approach to data will be transformed. Batch vs. stream processing? On-prem vs. cloud? Data lake or data mesh?

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

Data scientists and engineers are frustrated by the challenges of scaling data infrastructure. They know what’s needed, but they lack the time, resources and expertise to implement and maintain it.‍

Learn more about the infrastructure that accelerates building data driven-products. We break it down with a super-speedy explanation and video.

Stream processing has forever changed the modern data stack. Find out how it’s revolutionizing data management, streamlining business operations and enabling companies to deliver data-driven products and services.

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

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

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

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.

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.