Kafka in the Cloud: Why it’s 10x better with Confluent | Find out more
Change data capture is a popular method to connect database tables to data streams, but it comes with drawbacks. The next evolution of the CDC pattern, first-class data products, provide resilient pipelines that support both real-time and batch processing while isolating upstream systems...
Learn how the latest innovations in Kora enable us to introduce new Confluent Cloud Freight clusters, which can save you up to 90% at GBps+ scale. Confluent Cloud Freight clusters are now available in Early Access.
Learn how to contribute to open source Apache Kafka by writing Kafka Improvement Proposals (KIPs) that solve problems and add features! Read on for real examples.
Learn how to interact with the librdkafka library when sending and how to handle errors correctly. Take a deepdive into the internal mechanics of the library.
Stepping into the world of Apache Kafka® can feel a bit daunting at first. Get started with the top resources for beginners to start building your first Kafka application!
Apache Kafka 3.6 is here! This release includes Tiered Storage (Early Access), the ability to migrate clusters from ZooKeeper to KRaft with no downtime, the addition of a grace period to stream-table joins, & more!
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 data 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.