Introducing Connector Private Networking: Join The Upcoming Webinar!
Change data capture is a popular method to connect database tables to data streams, but it comes with drawbacks. The next evolution of the CDC pattern, first-class data products, provide resilient pipelines that support both real-time and batch processing while isolating upstream systems...
Check out all the highlights from the Apache Flink® 1.19 release!
Apache Kafka 3.7 introduces updates to the Consumer rebalance protocol, an official Apache Kafka Docker image, JBOD support in Kraft-based clusters, and more!
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 why configuring consumer Group IDs are a crucial part of designing your consumer application. By the end of this post, you’ll understand the impact they have on three areas: work sharing, new data detection, 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...
If you’ve used Kafka for any amount of time you’ve likely heard about connections; the most common place that they come up is in regard to clients. Sure, producer and consumer clients connect to the cluster to do their jobs, but it doesn’t stop there. Nearly all interactions across a cluster...
Setting up proactive, synthetic monitoring is critical for complex, distributed systems like Apache Kafka®, especially when deployed on Kubernetes and where the end-user experience is concerned, and is paramount for healthy real-time data pipelines...
This Thanksgiving-themed blog post walks through a brand new stream processing use case recipe for analyzing survey responses in real-time and gives ideas for how to spice it up and make the recipe your own!
The call for papers for Kafka Summit London 2023 has opened, and we’re looking to hear about your experiences using and working with Kafka. Every great technical talk starts with an experience. If you’re stuck looking for ideas on what to talk about, write what you know...
Confluent Cloud hosts Apache Kafka®, Kafka Connect, ksqlDB, and more. Here’s how we re-architected the system for a new deployment platform with zero downtime...
Rebalancing comes into play in Apache Kafka® when consumers join or leave a consumer group. In either case, there is a different number of consumers over which to distribute the partitions from the topic(s), and, so, they must be redistributed and rebalanced....
It can be easy to go about life without thinking about them, but requests are an important part of Apache Kafka; they form the basis of how clients interact with data as it moves into and out of Kafka topics, and, in certain cases, too many requests can have a negative impact on your brokers...
We are pleased to announce the release of Confluent Platform 7.3. This release accelerates mainframe modernization and unlocks data from legacy systems, increases efficiency while further simplifying management tasks for Apache Kafka® operators, and makes it easier for developers to...
This blog post explores the need to implement security for your Apache Kafka® cluster, then briefly reviews the security features and advantages of using Confluent Cloud.