Apache Kafka

Ever dealt with a misbehaving consumer group? Imbalanced broker load? This could be due to your consumer group and partitioning strategy!

Learn about the role of batch.size and linger.ms in Kafka message compression.

Looking to install Kafka on Windows? This step-by-step guide will show you how to set it up and run it thanks to the Windows Subsystem for Linux 2.

Apache Kafka (the basis for the Confluent Platform) delivers an advanced stream processing platform for streaming data across AWS, GCP, and Azure at scale, used by thousands of companies. Amazon...

Get a high-level overview of source connector tuning: What can and cannot be tuned, and tuning methodology for any and all source connectors.

Learn the basics of what an Apache Kafka cluster is and how they work, from brokers to partitions, how they balance load, and how they handle replication, and leader and replica failures.

When developing streaming applications, one crucial aspect that often goes unnoticed is the default partitioning behavior of Java and non-Java producers. This disparity can result in data mismatches and inconsistencies, posing challenges for developers.

Learn when to consider expanding to multiple Apache Kafka clusters, how to manage the operations for large clusters, and tools and resources for efficient operations.

The term “event” shows up in a lot of different Apache Kafka® arenas. There’s “event-driven design,” “event sourcing,” “designing events,” and “event streaming.” What is an event, and what is the difference between the role an event has to play in each of these contexts?

We are proud to announce the release of Apache Kafka® 3.5.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent features.

Companies are looking to optimize cloud and tech spend, and being incredibly thoughtful about which priorities get assigned precious engineering and operations resources. “Build vs. Buy” is being taken seriously again. And if we’re honest, this probably makes sense. There is a lot to optimize.

Operating Kafka at scale can consume your cloud spend and engineering time. And operating everyday tasks like scaling or deploying new clusters can be complex and require dedicated engineers. This post focuses on how Confluent Cloud is 1) Resource Efficient, 2) Fully Managed, and 3) Complete.

In part 2 of our blog series on understanding and optimizing your Kafka costs, we dive into how to estimate costs stemming from the development and operations personnel needed to self-manage Kafka.

It's hard to properly calculate the cost of running Kafka. In part 1 of 4, learn to calculate your Kafka costs based on your infrastructure, networking, and cloud usage.