Confluent Blog

ksqlDB is the event streaming database purpose-built for stream processing applications that enables real-time data processing against Apache Kafka®. ksqlDB makes it easy to read, write and process streaming data

As your Apache Kafka® deployment starts to grow, the benefits of using a schema registry quickly become compelling. Confluent Schema Registry, which is included in the Confluent Platform, enables you

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.

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 this blog post, we’re going to get back to basics and walk through how to get started using Apache Kafka with your Python applications. We will assume some basic

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

Note: The blog post Ensure Data Quality and Data Evolvability with a Secured Schema Registry contains more recent information. If you use Apache Kafka to integrate and decouple different data

Seems that many engineers have “Learn Kafka” on their new year resolution list. This isn’t very surprising. Apache Kafka is a popular technology with many use-cases. Armed with basic Kafka

This blog post is the third in a series about the Streams API of Apache Kafka, the new stream processing library of the Apache Kafka project, which was introduced in Kafka v0.10.

This is a guest blog from Fangjin Yang. Fangjin is the co-founder and CEO of Imply, a San Francisco based technology company, and one of the main committers of the Druid