Confluent Blog

Back in May 2017, we laid out why we believe that Kafka Streams is better off without a concept of watermarks or triggers, and instead opts for a continuous refinement

On the heels of part 1 in this blog series, Spring for Apache Kafka – Part 1: Error Handling, Message Conversion and Transaction Support, here in part 2 we’ll focus

Here at Zenreach, we create products to enable brick-and-mortar merchants to better understand, engage and serve their customers. Many of these products rely on our capability to quickly and reliably

Stream processing is a data processing technology used to collect, store, and manage continuous streams of data as it’s produced or received. Stream processing (also known as event streaming or

The phrase time value of data has been used to demonstrate that the value of captured data diminishes by time. This means that the sooner the data is captured, analyzed

The principle of least privilege dictates that each user and application will have the minimal privileges required to do their job. When applied to Apache Kafka® and its Streams API,

Zenreach provides a platform that leverages Wi-Fi and enables merchants to better understand and engage with their customers. Most of our product features depend on one basic building block: real-time

We are happy to announce that our paper Streams and Tables: Two Sides of the Same Coin is published and available for free download. The paper was presented at the

Confluent’s own Bill Bejeck has recently completed Kafka Streams in Action, a book about building real-time applications and microservices with the Kafka Streams API. The book is scheduled to be available

Adam Warski is one of the co-founders of SoftwareMill, where he codes mainly using Scala and other interesting technologies. He is involved in open-source projects, such as sttp, MacWire, Quicklens,