Confluent Blog

When you build microservices using Apache Kafka®, the log can be used as more than just a communication protocol. It can be used to store events: messaging that remembers. This […]

This article discusses the use of Apache Kafka’s Streams API for sending out alerts to customers of Rabobank. Rabobank is based in the Netherlands with over 900 locations worldwide, 48,000 employees, […]

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 […]