Event Streams in Action /

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...

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Detalles Bibliográficos
Autores principales: Crettaz, Valentin (Autor), Dean, Alexander (Autor)
Autor Corporativo: Safari, an O'Reilly Media Company
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Manning Publications, 2019.
Edición:1st edition.
Temas:
Kafka (Electronic resource)
Kinesis (Electronic resource)
Data logging.
Data mining.
Application logging (Computer science)
Journalisation applicative.
Exploration de données (Informatique)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Copyright
  • Brief Table of Contents
  • Table of Contents
  • Preface
  • Acknowledgments
  • About this book
  • About the authors
  • About the cover illustration
  • Part 1. Event streams and unified logs
  • Chapter 1. Introducing event streams
  • 1.1. Defining our terms
  • 1.2. Exploring familiar event streams
  • 1.3. Unifying continuous event streams
  • 1.4. Introducing use cases for the unified log
  • Summary
  • Chapter 2. The unified log
  • 2.1. Understanding the anatomy of a unified log
  • 2.2. Introducing our application
  • 2.3. Setting up our unified log
  • Summary
  • Chapter 3. Event stream processing with Apache Kafka
  • 3.1. Event stream processing 101
  • 3.2. Designing our first stream-processing app
  • 3.3. Writing a simple Kafka worker
  • 3.4. Writing a single-event processor
  • Summary
  • Chapter 4. Event stream processing with Amazon Kinesis
  • 4.1. Writing events to Kinesis
  • 4.2. Reading from Kinesis
  • Summary
  • Chapter 5. Stateful stream processing
  • 5.1. Detecting abandoned shopping carts
  • 5.2. Modeling our new events
  • 5.3. Stateful stream processing
  • 5.4. Detecting abandoned carts
  • 5.5. Running our Samza job
  • Summary
  • Part 2. Data engineering with streams
  • Chapter 6. Schemas
  • 6.1. An introduction to schemas
  • 6.2. Modeling our event in Avro
  • 6.3. Associating events with their schemas
  • Summary
  • Chapter 7. Archiving events
  • 7.1. The archivist's manifesto
  • 7.2. A design for archiving
  • 7.3. Archiving Kafka with Secor
  • 7.4. Batch processing our archive
  • Summary
  • Chapter 8. Railway-oriented processing
  • 8.1. Leaving the happy path
  • 8.2. Failure and the unified log
  • 8.3. Failure composition with Scalaz
  • 8.4. Implementing railway-oriented processing
  • Summary
  • Chapter 9. Commands
  • 9.1. Commands and the unified log
  • 9.2. Making decisions
  • 9.3. Consuming our commands.
  • 9.4. Executing our commands
  • 9.5. Scaling up commands
  • Summary
  • Part 3. Event analytics
  • Chapter 10. Analytics-on-read
  • 10.1. Analytics-on-read, analytics-on-write
  • 10.2. The OOPS event stream
  • 10.3. Getting started with Amazon Redshift
  • 10.4. ETL, ELT
  • 10.5. Finally, some analysis
  • Summary
  • Chapter 11. Analytics-on-write
  • 11.1. Back to OOPS
  • 11.2. Building our Lambda function
  • 11.3. Running our Lambda function
  • Summary
  • Appendix. AWS primer
  • A.1. Setting up the AWS account
  • A.2. Creating a user
  • A.3. Setting up the AWS CLI
  • Index
  • List of Figures
  • List of Tables
  • List of Listings.

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