Includes index

"Many high-profile applications, like LinkedIn and Netflix, deliver nimble, responsive performance by reacting to user and system events as they occur. In large-scale systems, this requires efficiently monitoring, managing, and reacting to multiple event streams. Tools like Kafka, along with innovative patterns like unified log processing, help create a coherent data processing architecture for event-based applications. "Event streams in action" teaches you techniques for aggregating, storing, and processing event streams using the unified log processing pattern. In this hands-on guide, you'll discover important application designs like the lambda architecture, stream aggregation, and event reprocessing. You'll also explore scaling, resiliency, advanced stream patterns, and much more! By the time you're finished, you'll be designing large-scale data-driven applications that are easier to build, deploy, and maintain."--

Part 1.

Event streams and unified logs. Introducing event streams -- Unified log -- Event stream processing with Apache Kafka -- Event stream processing with Amazon Kinesis -- Stateful stream processing --

Part 2.

Data engineering with streams. Schemas -- Archiving events -- Railway-oriented processing -- Commands --

Part 3.

Event analytics. Analytics-on-read -- Analytics-on-write