Confluent Blog

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,

In this post I’m going to show what streaming ETL looks like in practice. We’re replacing batch extracts with event streams, and batch transformation with in-flight transformation. But first, a

Apache Kafka and Apache Mesos are very well-known and successful Apache projects. A lot has happened in these projects since Confluent’s last blog post on the topic in July 2015.

This blog post is the third and last in a series about the exactly-once semantics for Apache Kafka®. See Exactly-once Semantics are Possible: Here’s How Kafka Does it for the