Confluent Blog

In this post I’m going to show what streaming ETL looks like in practice. We’re replacing batch extracts with event streams, and batch transformation with in-flight transformation. But first, a

Apache Kafka and Apache Mesos are very well-known and successful Apache projects. A lot has happened in these projects since Confluent’s last blog post on the topic in July 2015.

This blog post is the third and last in a series about the exactly-once semantics for Apache Kafka®. See Exactly-once Semantics are Possible: Here’s How Kafka Does it for the

Bobby Calderwood is a Distinguished Engineer at Capital One, where he influences the technical direction of Capital One and engages the broader community via speaking and open-source contributions. In the

I am very excited to announce the general availability of Confluent Platform 4.0, the enterprise distribution of Apache Kafka 1.0. This release includes a number of significant improvements, including enhancements

In a previous blog post, we introduced exactly-once semantics for Apache Kafka®. That post covered the various message delivery semantics, introduced the idempotent producer, transactions, and the exactly-once processing semantics

Today, we invariably operate in ecosystems: groups of applications and services which together work towards some higher level business goal. When we make these systems event-driven they come with a

It has been seven years since we first set out to create the distributed streaming platform we know now as Apache Kafka®. Born initially as a highly scalable messaging system,

This guest blog post is the second in a series about the use of Apache Kafka’s Streams API by Zalando, Europe’s largest online fashion retailer. See Ranking Websites in Real-time

This article is by Hunter Kelly, Technical Architect at Zalando. Hunter enjoys using technology, and in particular machine learning, to solve difficult problems. He’s a graduate of the University of