Confluent Blog

While Self-Balancing Clusters (SBC) perform effectively in balancing Apache Kafka® clusters, one of the common themes we hear from our users is that they would love some visibility into the

Users with business-critical data locked in mainframes want to exploit this data by using Confluent to deploy in either a public cloud, multicloud, on-premises, or hybrid infrastructure. In collaboration with

This blog post is the second in a four-part series that discusses a few new Confluent Control Center features that are introduced with Confluent Platform 6.2.0. This blog post focuses

Managing Apache Kafka® clusters can be tricky sometimes. To solve this problem, Confluent Control Center helps you easily manage and monitor your clusters and interact with other Confluent components, such

There is a class of applications that cannot afford to be unavailable—for example, external-facing entry points into your organization. Typically, anything your customers interact with directly cannot go down. As

I previously showed how to install and set up Apache Kafka® on Windows in minutes by using the Windows Subsystem for Linux 2 (WSL 2). From there, it’s only a

Most companies who have adopted event streaming are running multiple Apache Kafka® environments. For example, they may use different Kafka clusters for testing vs. production or for different use cases.

Persisting data in multiple regions has become crucial for modern businesses: They need their mission-critical data to be protected from accidents and disasters. They can achieve this goal by running

How do you process IoT data, change data capture (CDC) data, or streaming data from sensors, applications, and sources in real time? Apache Kafka® and Apache Spark® are widely adopted

At Zendesk, Apache Kafka® is one of our foundational services for distributing events among different internal systems. We have pods, which can be thought of as isolated cloud environments where