Event streams in action : real-time event systems with Kafka and Kinesis / Alexander Dean, Valentin Crettaz.

Format:

Book

Author/Creator:

Dean, Alexander G., author.

Crettaz, Valentin, author.

Language:

English

Subjects (All):

Data logging.

Kafka (Electronic resource).

Kinesis (Electronic resource).

Physical Description:

1 online resource (344 pages)

Edition:

1st edition

Place of Publication:

Shelter Island, New York : Manning Publications, [2019]

System Details:

text file

Summary:

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.

Contents:

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

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

Chapter 4. Event stream processing with Amazon Kinesis

4.1. Writing events to Kinesis

4.2. Reading from Kinesis

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

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

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

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

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

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

Chapter 11. Analytics-on-write

11.1. Back to OOPS

11.2. Building our Lambda function

11.3. Running our Lambda function

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.

Notes:

Description based on print version record.

Includes index.

ISBN:

9781638355830

1638355835

OCLC:

1257077942