Event Streams In Action Realtime Event Systems With Kafka And Kinesis Alexander Dean by Alexander Dean, Valentin Crettaz 9781617292347, 1617292346 instant download after payment.

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
This book 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
1. Introducing event streams
2. The unified log 24
3. Event stream processing with Apache Kafka
4. Event stream processing with Amazon Kinesis
5. Stateful stream processing
PART 2- DATA ENGINEERING WITH STREAMS
6. Schemas
7. Archiving events
8. Railway-oriented processing
9. Commands
PART 3 - EVENT ANALYTICS
10. Analytics-on-read
11. Analytics-on-write