The Events Pipeline team is responsible for plumbing some of New Relic’s core data streams-specifically, event data. These are fine-grained nuggets of monitoring data that record a single event at a particular moment in time. For example, an event could be an error thrown by an application, a page view on a browser, or an e-commerce shopping cart transaction.
In this post, we show how we built our Kafka pipeline so that it stitches together microservices and serves as a changelog and “durable cache,” all with the idea of processing data streams as smoothly and effectively as possible at our scale. In an upcoming post, we’ll share thoughts on how we manage topic partitions in this pipeline.
If you’re wondering if Kafka might be right for you, check out this post for several scenarios which fit.