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What is a Kafka Consumer and How does it work?

Now that your data is inside your Kafka cluster, how do you get it out? In this video, Dan Weston covers the basics of Kafka Consumers: what consumers are, how they get your data flowing, and best practices for configuring consumers in a real-time data streaming system. You will also learn about offsets, consumer groups, and partition assignment.

What is the Listen to Yourself Pattern? | Designing Event-Driven Microservices

The Listen to Yourself pattern is implemented by having a microservice emit an event to a platform such as Apache Kafka, and then consuming its own events to perform internal updates. It can be used as a solution to the dual-write problem since it separates Kafka and database writes into different processes. However, it also provides added benefits because it allows microservices to respond quickly to requests by deferring processing to a later time.

Effortless Stream Processing on Any Cloud - Flink Actions, Terraform Support, and Multi-Cloud Availability

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.

Introducing Apache Kafka 3.7

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.

Apache Kafka 3.7: Official Docker Image and Improved Client Monitoring

Apache Kafka® 3.7 is here! On behalf of the Kafka community, Danica Fine highlights key release updates, with KIPs from Kafka Core, Kafka Streams, and Kafka Connect. Kafka Core: Kafka Streams: Kafka Connect: Many more KIPs are a part of this release. See the blog post for more details.

Data Products, Data Contracts, and Change Data Capture

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.

What is the Event Sourcing Pattern? | Designing Event-Driven Microservices

Event Sourcing is a pattern of storing an object's state as a series of events. Each time the object is updated a new event is written to an append-only log. When the object is loaded from the database, the events are replayed in order, reapplying the necessary changes. The benefit of this approach is that it stores a full history of the object. This can be valuable for debugging, auditing, building new models, and a variety of other situations. It is also a technique that can be used to solve the dual-write problem when working with event-driven architectures.

New with Confluent Platform: Seamless Migration Off ZooKeeper, Arm64 Support, and More

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.