Technology

An Approach to combining Change Data Capture (CDC) messages from a relational database into transactional messages using Kafka Streams.

This post details how to minimize internal messaging within Confluent platform clusters. Service mesh and containerized applications have popularized the idea of control and data planes. This post applies it to the Confluent platform clusters and highlights its use in Confluent Cloud.

Using Apache Kafka to decouple microservices is a successful way to build a more resilient, flexible, and scalable architecture. However, it is very common for such microservices to pair with a database. This blog provides a real-world use case on how Kafka replaces a database with ksqlDB.

This article summarizes dynamic versus static consumer group membership in Apache Kafka. It shows how the approaches affect rebalancing in heavy state applications and teaches the user how to choose between the methods.

Learn how to avoid confusion by implementing co-partitioning.

Learn what windowing is in Kafka Streams and get comfortable with the differences between the main types.

Apache Kafka 3.4 includes early access to ZooKeeper to KRaft migrations, enabling existing Kafka clusters to migrate to KRaft mode and gain scalability and resiliency benefits. Additionally, 3.4 includes several updates to Kafka Core, Streams, Connect, and more.

Announcing the latest updates to Confluent’s cloud-native data streaming platform, centralized identity management, enhanced RBAC, Client Quotas, and more.

Confluent is pleased to announce that the Confluent CLI—the leading command-line tool for managing enterprise Kafka deployments and modern data flow—is now source available under the Confluent Community License.

Building data streaming applications, and growing them beyond a single team is challenging. Data silos develop easily and can be difficult to solve. The tools provided by Confluent’s Stream Governance platform can help break down those walls and make your data accessible to those who need it.

Change data capture (CDC) converts all the changes that occur inside your database into events and publishes them to an event stream. You can then use these events to power analytics, drive operational use cases, hydrate databases, and more. The pattern is enjoying wider adoption than ever before.

In this post, we introduce how to use .NET Kafka clients along with the Task Parallel Library to build a robust, high-throughput event streaming application...

Learn what a Kafka® consumer group ID is and how assigning one to Kafka consumers during configuration helps with detecting new data, work sharing, and data recovery.

Self-managing connectors come with major time and resource challenges and taking on unnecessary risks of downtime that shift your team’s focus away from working on more strategic projects and innovations...