Technology

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.

Confluent Platform 7.4 now includes SBOMs, which gives customers more transparency and control over their software deployments.

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.

ChatGPT and data streaming can work together for any company. Learn a basic framework for using GPT-4 and streaming to build a real-world production application.

GitOps can work with policy-as-code systems to provide a true self-service model for managing Confluent resources. Policy-as-code is the practice of permitting or preventing actions based on rules and conditions defined in code. In the context of GitOps for Confluent, suitable policies...

Amazon DynamoDB is a fully managed, serverless, key-value NoSQL database service that is highly available and scalable. It is designed to deliver single-digit millisecond query performance at any scale. It offers a fast and flexible way to store...

Take a tour of the internals of Confluent’s Apache Kafka® service, powered by Kora: the next-generation, cloud-native streaming engine.

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.

Announcing the latest updates to Confluent’s cloud-native data streaming platform: Kora Engine, Data Quality Rules, Custom Connectors, Streaming Sharing, and more.

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.