Event Streams in Action: Real-time event systems with Kafka and Kinesis

Summary

Event Streams in Action is a foundational book introducing the ULP paradigm and presenting techniques to use it effectively in data-rich environments.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

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.

About the Book

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.

What’s inside

• Validating and monitoring event streams
• Event analytics
• Methods for event modeling
• Examples using Apache Kafka and Amazon Kinesis

About the Reader

For readers with experience coding in Java, Scala, or Python.

About the Author

Alexander Dean developed Snowplow, an open source event processing and analytics platform. Valentin Crettaz is an independent IT consultant with 25 years of experience.

Table of Contents

PART 1 – EVENT STREAMS AND UNIFIED LOGS
Chapter 1. Introducing event streams
Chapter 2. The unified log 24
Chapter 3. Event stream processing with Apache Kafka
Chapter 4. Event stream processing with Amazon Kinesis
Chapter 5. Stateful stream processing

PART 2- DATA ENGINEERING WITH STREAMS
Chapter 1. Schemas
Chapter 2. Archiving events
Chapter 3. Railway-oriented processing
Chapter 4. Commands

PART 3 – EVENT ANALYTICS
Chapter 1. Analytics-on-read
Chapter 2. Analytics-on-write