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I'm excited to share our intent to acquire Immerok! Together, we’ll build a cloud-native service for Apache Flink that delivers the same simplicity, security, and scalability that you expect from Confluent for Kafka.
When you encounter a problem with Apache Kafka®—for example, an exploding number of connections to your brokers or perhaps some wonky record batching—it’s easy to consider these issues as something to be solved in and of themselves...
Wildlife monitoring is critical for keeping track of population changes of vulnerable animals. As part of the Confluent Hackathon ʼ22, I was inspired to investigate if a streaming platform could […]
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 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!