[Webinaire] La reprise après sinistre des systèmes basés sur Kafka | Inscrivez-vous dès maintenant
Stream Processing
Data Products, Data Contracts, and Change Data Capture
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...
Introducing Confluent Cloud Freight Clusters
Learn how the latest innovations in Kora enable us to introduce new Confluent Cloud Freight clusters, which can save you up to 90% at GBps+ scale. Confluent Cloud Freight clusters are now available in Early Access.
Contributing to Apache Kafka®: How to Write a KIP
Learn how to contribute to open source Apache Kafka by writing Kafka Improvement Proposals (KIPs) that solve problems and add features! Read on for real examples.
Building Event Streaming Applications in .NET
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...
A (Stream Processing) Recipe for Thankfulness
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!
Debugging of a Stream-Table Join: Failing to Cross the Streams
Joining two topics to aggregate the data is one of the fundamental operations in stream processing. But that's not to say that it's simple. Let me show you what can go wrong!
Announcing ksqlDB 0.28.2 and Improvements to ksqlDB in Confluent Cloud
We are excited to announce the ksqlDB 0.28.2 release as well as new cloud-specific improvements! This release simplifies the getting started experience, helps to run and monitor critical pipelines
Serverless Stream Processing with Apache Kafka, Azure Functions, and ksqlDB
Serverless stream processing with Apache Kafka® is a powerful yet often underutilized field. Microsoft’s Azure Functions, in combination with ksqlDB and Confluent’s sink connector, provide a powerful and easy-to-use set […]
Announcing ksqlDB 0.27.1
We are excited to announce the ksqlDB 0.27.1 release! This iteration brings forth new functionality for dynamic data access against multi-schema topics, a host of improvements relating to working with […]
Announcing ksqlDB 0.26
We are excited to bring forward yet another ksqlDB release with ksqlDB 0.26. This time we have focused on improving your experience with aggregate functions, JOINs, and schemas. We have […]
Stream Processing vs. Batch Processing: What to Know
With more data being produced in real time by many systems and devices than ever before, it is critical to be able to process it in real time and get […]
Announcing ksqlDB 0.25.1
We are thrilled to announce ksqlDB 0.25! It comes with a slew of improvements and new features. In particular, we improved how UDAFs work with complex types like Structs and […]
Introducing Stream Processing Use Case Recipes Powered by ksqlDB
From fraud detection and predictive analytics, to real-time customer experiences and cyber security, stream processing has countless benefits for use cases big and small. By unlocking the power of continuous […]
Announcing ksqlDB 0.24.0
We are excited to announce ksqlDB 0.24! It comes with a slew of improvements and new features. Access to Apache Kafka® record headers will enable a whole host of new […]
Announcing ksqlDB 0.23.1
We’re pleased to announce ksqlDB 0.23.1! This release allows you to now perform pull queries on streams, which makes it much easier to find a given record in a topic. […]
Serverless Stream Processing with Apache Kafka, AWS Lambda, and ksqlDB
It seems like now more than ever developers are surrounded by a sea of terminology—but what does it really all mean? Here, we will take some often heard terms—some considered […]
A Guide to Stream Processing and ksqlDB Fundamentals
Event streaming applications are a powerful way to react to events as they happen and to take advantage of data while it is fresh. However, they can be a challenge […]