Confluent Blog

For event streaming application developers, it is important to continuously update the streaming pipeline based on the need for changes in the individual applications in the pipeline. It is also […]

At TokenAnalyst, we are building the core infrastructure to integrate, clean, and analyze blockchain data. Data on a blockchain is also known as on-chain data. We offer both historical and […]

Following part 1 and part 2 of the Spring for Apache Kafka Deep Dive blog series, here in part 3 we will discuss another project from the Spring team: Spring […]

Red Pill Analytics was recently engaged by a Fortune 500 e-commerce and wholesale company that is transforming the way they manage inventory. Traditionally, this company has used only a few […]

With the release of Apache Kafka® 2.1.0, Kafka Streams introduced the processor topology optimization framework at the Kafka Streams DSL layer. This framework opens the door for various optimization techniques […]

So you’ve convinced your friends and stakeholders about the benefits of event-driven systems. You have successfully piloted a few services backed by Apache Kafka®, and it is now supporting business-critical […]

Event-driven architecture means just that: It’s all about the events. In a microservices architecture, events drive microservice actions. No event, no shoes, no service. In the most basic scenario, microservices […]

Apache Kafka® based applications stand out for their ability to decouple producers and consumers using an event log as an intermediate layer. One result of this is that producers and […]

Back in May 2017, we laid out why we believe that Kafka Streams is better off without a concept of watermarks or triggers, and instead opts for a continuous refinement […]

On the heels of part 1 in this blog series, Spring for Apache Kafka – Part 1: Error Handling, Message Conversion and Transaction Support, here in part 2 we’ll focus […]