Confluent Blog

I’m thrilled that we have hit an exciting milestone the Kafka community has long been waiting for: we have  introduced exactly-once semantics in Apache Kafka in the 0.11 release and […]

There has been a lot of talk recently that traditional ETL is dead. In the traditional ETL paradigm, data warehouses were king, ETL jobs were batch-driven, everything talked to everything […]

In his book Design Patterns Explained, Alan Shalloway compares his car to an umbrella. After all, he uses both to stay dry in the rain. The umbrella has an advantage […]

Introduction What’s great about the Kafka Streams API is not just how fast your application can process data with it, but also how fast you can get up and running […]

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 […]

A few months ago, we announced the release of open-source Confluent Platform 3.0 and Apache Kafka 0.10, marking the availability of Kafka Streams — the new stream processing engine of […]

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.