Confluent Blog

This post was originally published at the Codecentric blog with a focus on “old” join semantics in Apache Kafka versions 0.10.0 and 0.10.1. Version 0.10.0 of the popular distributed streaming

At The New York Times we have a number of different systems that are used for producing content. We have several Content Management Systems, and we use third-party data and

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

I’m thrilled that we have hit an exciting milestone the Apache Kafka® community has long been waiting for: we have  introduced exactly-once semantics in 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