A modern business operates 24/7 and generates data continuously. Shouldn’t we process it continuously too?
A rich ecosystem of real-time data-processing frameworks, tools, and systems has been forming around Apache Kafka that allows data to be processed continuously as it occurs. Jay Kreps introduces Kafka and explains why it has become the de facto standard for streaming data. Jay draws on practical experience building stream-processing applications to discuss the difference between architectures and the challenges each presents. Jay then outlines Kafka Streams, which offers new stream processing functionality in Kafka, and explains how it helps to tame some of the complexity in real-time architectures.
Jay Kreps is the cofounder and CEO of Confluent, a company focused on Apache Kafka. Previously, Jay was one of the primary architects for LinkedIn, where he focused on data infrastructure and data-driven products. He was among the original authors of a number of open source projects in the scalable data systems space, including Voldemort (a key-value store), Azkaban, Kafka (a distributed messaging system), and Samza (a stream processing system).
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