Confluent Blog

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

Here at Zenreach, we create products to enable brick-and-mortar merchants to better understand, engage and serve their customers. Many of these products rely on our capability to quickly and reliably

Stream processing is a data processing technology used to collect, store, and manage continuous streams of data as it’s produced or received. Stream processing (also known as event streaming or