Confluent Blog

The relationship between Apache Kafka® and machine learning (ML) is an interesting one that I’ve written about quite a bit in How to Build and Deploy Scalable Machine Learning in

Static Membership is an enhancement to the current rebalance protocol that aims to reduce the downtime caused by excessive and unnecessary rebalances for general Apache Kafka® client implementations. This applies

First, what is event sourcing? Here’s an example. Consider your bank account: viewing it online, the first thing you notice is often the current balance. How many of us drill

We know that Apache Kafka® is great when you’re dealing with streams, allowing you to conveniently look at streams as tables. Stream processing engines like ksqlDB furthermore give you the

We’re excited to announce Tutorials for Apache Kafka®, a new area of our website for learning event streaming. Kafka Tutorials is a collection of common event streaming use cases, with

One of ksqlDB’s most powerful features is allowing users to build their own ksqlDB functions for processing real-time streams of data. These functions can be invoked on individual messages (user-defined

The Kafka Streams API boasts a number of capabilities that make it well suited for maintaining the global state of a distributed system. At Imperva, we took advantage of Kafka

Have you ever realized that, according to the latest FBI report, more than 80% of all crimes are property crimes, such as burglaries? And that the FBI clearance figures indicate

I’ve been using KSQL from Confluent since its first developer preview in 2017. Reading, writing, and transforming data in Apache Kafka® using KSQL is an effective way to rapidly deliver

Building off part 1 where we discussed an event streaming architecture that we implemented for a customer using Apache Kafka, KSQL, and Kafka Streams, and part 2 where we discussed