Introducing Streamhouse: the open data architecture for AI | Learn More
New industry initiative establishes an open category for data architectures that power real-time applications and AI agents
Learn how to migrate to Confluent Cloud in hours using Confluent’s open source Kafka Copy Paste tool. Get an in-depth introduction to the KCP tool and a walk-through of the four steps of migrating from MSK to Confluent Cloud using the tool.
Confluent's AI developer tools are now GA: an open-source local MCP server, a managed MCP server, and Agent Skills. Together they give AI coding assistants direct access to your streaming platform — the tools to act on it and the domain knowledge to build correctly.
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.
This blog post discusses the two generals problems, how it impacts message delivery guarantees, and how those guarantees would affect a futuristic technology such as teleportation.
Our new PII Detection solution enables you to securely utilize your unstructured text by enabling entity-level control. Combined with our suite of data governance tools, you can execute a powerful real-time cyber defense strategy.
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.
The blog introduces Confluent Platform 7.4 and its key features, including enhancing scalability, increasing architectural simplicity, accelerating time to market, reducing ops burden, and ensuring high-quality data streams. It also covers what's new in Apache Kafka 3.4.
If you’ve been working with Kafka Streams and have seen an “unknown magic byte” error, you might be wondering what a magic byte is in the first place, and also, how to resolve the error. This post explains the answers to both questions.