Cover image for Event Streams in Action

Event Streams in Action

Author Alexander Dean , Valentin Crettaz
Release Date: 2019/05/01
ISBN: 9781617292347
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Book Description

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.

Table of Contents

  1. Copyright
  2. Brief Table of Contents
  3. Table of Contents
  4. Preface
  5. Acknowledgments
  6. About this book
  7. About the authors
  8. About the cover illustration
  9. Part 1. Event streams and unified logs
    1. Chapter 1. Introducing event streams
      1. 1.1. Defining our terms
      2. 1.2. Exploring familiar event streams
      3. 1.3. Unifying continuous event streams
      4. 1.4. Introducing use cases for the unified log
      5. Summary
    2. Chapter 2. The unified log
      1. 2.1. Understanding the anatomy of a unified log
      2. 2.2. Introducing our application
      3. 2.3. Setting up our unified log
      4. Summary
    3. Chapter 3. Event stream processing with Apache Kafka
      1. 3.1. Event stream processing 101
      2. 3.2. Designing our first stream-processing app
      3. 3.3. Writing a simple Kafka worker
      4. 3.4. Writing a single-event processor
      5. Summary
    4. Chapter 4. Event stream processing with Amazon Kinesis
      1. 4.1. Writing events to Kinesis
      2. 4.2. Reading from Kinesis
      3. Summary
    5. Chapter 5. Stateful stream processing
      1. 5.1. Detecting abandoned shopping carts
      2. 5.2. Modeling our new events
      3. 5.3. Stateful stream processing
      4. 5.4. Detecting abandoned carts
      5. 5.5. Running our Samza job
      6. Summary
  10. Part 2. Data engineering with streams
    1. Chapter 6. Schemas
      1. 6.1. An introduction to schemas
      2. 6.2. Modeling our event in Avro
      3. 6.3. Associating events with their schemas
      4. Summary
    2. Chapter 7. Archiving events
      1. 7.1. The archivist’s manifesto
      2. 7.2. A design for archiving
      3. 7.3. Archiving Kafka with Secor
      4. 7.4. Batch processing our archive
      5. Summary
    3. Chapter 8. Railway-oriented processing
      1. 8.1. Leaving the happy path
      2. 8.2. Failure and the unified log
      3. 8.3. Failure composition with Scalaz
      4. 8.4. Implementing railway-oriented processing
      5. Summary
    4. Chapter 9. Commands
      1. 9.1. Commands and the unified log
      2. 9.2. Making decisions
      3. 9.3. Consuming our commands
      4. 9.4. Executing our commands
      5. 9.5. Scaling up commands
      6. Summary
  11. Part 3. Event analytics
    1. Chapter 10. Analytics-on-read
      1. 10.1. Analytics-on-read, analytics-on-write
      2. 10.2. The OOPS event stream
      3. 10.3. Getting started with Amazon Redshift
      4. 10.4. ETL, ELT
      5. 10.5. Finally, some analysis
      6. Summary
    2. Chapter 11. Analytics-on-write
      1. 11.1. Back to OOPS
      2. 11.2. Building our Lambda function
      3. 11.3. Running our Lambda function
      4. Summary
  12. Appendix. AWS primer
    1. A.1. Setting up the AWS account
    2. A.2. Creating a user
    3. A.3. Setting up the AWS CLI
  13. Index
  14. List of Figures
  15. List of Tables
  16. List of Listings
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