The Quix blog

The main difference between them is that Kafka is a distributed event streaming platform designed to ingest and process massive amounts of data, while ActiveMQ is a traditional message broker that supports multiple protocols and flexible messaging patterns.

Quix 2.0 is here 🚀 Designed around the concept of Infrastructure-as-Code, Quix 2.0 makes it easier to build and run reliable, powerful event-streaming applications that scale, with a single source of truth powered by Git.

The main difference between them is that Kafka is an event streaming platform designed to ingest and process massive amounts of data, while RabbitMQ is a general-purpose message broker that supports flexible messaging patterns, multiple protocols, and complex routing.

The main difference between Spark and Beam is that the former enables you to both write and run data processing pipelines, while the latter allows you to write data processing pipelines, and then run them on various external execution environments (runners). But what are the other differences between Spark and Beam, and how are they similar?

Explore the characteristics, challenges, and benefits of machine learning pipelines, and read about the steps involved in training and deploying ML models to production.

An in-depth comparison of Kafka and Pulsar, covering criteria such as architectural differences, operational attributes, developer experience, ecosystems, deployment options, and security.

Teams can now build AI applications on Confluent’s data in motion, with Quix, the AI-ready event streaming application framework.

Explore the AI applications that you can build when connecting Quix with Confluent.

What is real-time machine learning? How is it different from batch ML? What are common real-time ML use cases? What are the challenges of building real-time ML capabilities? All these questions and more are answered in this article.

Explore the evolution of new tools for real-time pipelines that aim to solve the ongoing problem of data scientists' need for more infrastructure expertise.

Should data scientists know Java? Java and Scala underpin many real-time, ML-based applications—yet data scientists usually work in Python. Someone has to port the Python into Java or adapt it to use a Python wrapper. Neither of these options is ideal, so what are some better solutions?

Explore the fundamentals of time series analysis in this comprehensive article. Learn about key concepts, use cases, and types of time series analysis, and discover models, techniques, and methods to analyze time series data.

Gain a thorough understanding of telemetry data and how it works, learn about its benefits, challenges, and applications across different industries, and discover technologies you can use to operationalize telemetry.

A look at the most common causes of Kafka's "unknown topic or partition" error along with practical steps and solutions to help you fix it.

Explore the unique features and limitations of Apache Kafka and Apache Flink and learn how these open source streaming titans can either join forces or operate independently.

A monthly round-up of the most interesting news coming out of the stream processing ecosystem

Moving code from prototype to production can be tricky—especially for data scientists. There are many challenges in deploying code that needs to calculate features for ML models in real-time. I look at potential solutions to ease the friction.

Using ksqlDB for real-time feature transformations isn't as easy as it looks. I revisit the strategy to democratize stream processing and examine what's still missing.