We’re excited to share the latest and greatest features on Confluent Cloud, in our first launch of 2024. This Cloud Launch comes to you from Kafka Summit London, where we talked about the latest updates highlighted in our launch, including serverless Apache Flink®, some exciting pricing changes, updates to connectors, and more! We also shared our vision for a future offering, Tableflow.

Last year, we announced our plan to build a cloud-native Apache Flink® service to meet the growing demand for scalable and efficient stream processing solutions in the cloud. Today, we're thrilled to announce the general availability of Confluent Cloud for Apache Flink across all three major clouds. This means that you can now experience Apache Kafka® and Flink as a unified, enterprise-grade platform to connect and process your data in real time, wherever you need it.

We’re excited to talk about our vision for Tableflow, which makes it push-button simple to take Apache Kafka® data and feed it directly into your data lake, warehouse, or analytics engine as Apache Iceberg® tables. Making operational data accessible to the analytical world is traditionally a complex, expensive, and brittle process and we believe we can do better to unify the operational and analytical estates.

The Apache Flink® community unveiled Apache Flink version 1.19 this week! This release is packed with numerous new features and enhancements. In this blog post, we'll spotlight some of the standout additions. For a comprehensive rundown of all updates, don't forget to review the release notes.

Since we launched the Open Preview of our serverless Apache Flink® service during last year’s Current, we’ve continued to add new capabilities to the product that make stream processing accessible and easy to use for everyone. In this blog post, we will highlight some of the key features added this year.

We are proud to announce the release of Apache Kafka® 3.7.0. This release contains many new features and improvements. This blog post will highlight some of the more prominent features. For a full list of changes, be sure to check the release notes. See the Upgrading to 3.7.0 from any version 0.8.x through 3.6.x section in the documentation for the list of notable changes and detailed upgrade steps.

Change data capture (CDC) has long been one of the most popular, reliable, and quickest ways to connect your database tables into data streams. It is a powerful pattern and one of the most common and easiest ways to bootstrap data into Apache Kafka®. But it comes with a relatively significant drawback—it exposes your database’s internal data model to the downstream world.

With the increasing importance of real-time data in modern businesses, companies are leveraging distributed streaming platforms to process and analyze data streams in real time. Many companies are also transitioning to the cloud, which is often a gradual process that takes several years and involves incremental stages. During this transition, many companies adopt hybrid cloud architectures, either temporarily or permanently.

Apache Kafka® cluster administrators often need to solve problems like how to onboard new teams, manage resources like topics or connectors, and maintain permission control over these resources. In this post, we will demonstrate how to use Confluent for Kubernetes (CfK) to enable GitOps with a CI/CD pipeline and delegate resource creation to groups of people without distributing admin permission passwords to other people in the organization.