Confluent Blog

Scalable Machine Learning in Production with Apache Kafka® Intelligent real time applications are a game changer in any industry. Machine learning and its sub-topic, deep learning, are gaining momentum because […]

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 […]

Update: KSQL is now available as a component of the Confluent Platform. I’m really excited to announce KSQL, a streaming SQL engine for Apache Kafka®. KSQL lowers the entry bar to […]

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 […]

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 […]