Confluent Blog

The Google Dataflow team has done a fantastic job in evangelizing their model of handling time for stream processing. Their key observation is that in most cases you can’t globally […]

This post was co-written with Damian Guy, Engineer at Confluent, Michael Noll, Product Manager at Confluent and Neha Narkhede, CTO and Co-Founder at Confluent. We are excited to announce Interactive […]

Event sourcing as an application architecture pattern is rising in popularity. Event sourcing involves modeling the state changes made by applications as an immutable sequence or “log” of events. Instead […]

This blog post is written jointly by Stephan Ewen, CTO of data Artisans, and Neha Narkhede, CTO of Confluent. Stephan Ewen is PMC member of Apache Flink and co-founder and CTO […]

This blog post is the third in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10.

This blog post is the second in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10. Current […]

This blog post is the first in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10. Current […]

In previous blog posts we introduced Kafka Streams and demonstrated an end-to-end Hello World streaming application that analyzes Wikipedia real-time updates through a combination of Kafka Streams and Kafka Connect. […]

At the beginning of June several Confluent team members attended Berlin Buzzwords 2016, where we gave three talks focused on stream processing and distributed computing. These talks, which we summarize […]

Important: The information in this article is outdated. With recent Kafka versions the integration between Kafka Connect and Kafka Streams as well as KSQL has become much simpler and easier. […]