Preface

Since 2011, Kafka's been exploding in terms of growth. More than one third of Fortune 500 companies use Apache Kafka. These companies include travel companies, banks, insurance companies, and telecom companies.

Uber, Twitter, Netflix, Spotify, Blizzard, LinkedIn, Spotify, and PayPal process their messages with Apache Kafka every day.

Today, Apache Kafka is used to collect data, do real-time data analysis, and perform real-time data streaming. Kafka is also used to feed events to Complex Event Processing (CEP) architectures, is deployed in microservice architectures, and is implemented in Internet of Things (IoT) systems.

In the realm of streaming, there are several competitors to Kafka Streams, including Apache Spark, Apache Flink, Akka Streams, Apache Pulsar, and Apache Beam. They are all in competition to perform better than Kafka. However, Apache Kafka has one key advantage over them all: its ease of use. Kafka is easy to implement and maintain, and its learning curve is not very steep.

This book is a practical quick start guide. It is focused on showing practical examples and does not get involved in theoretical explanations or discussions of Kafka's architecture. This book is a compendium of hands-on recipes, solutions to everyday problems faced by those implementing Apache Kafka.

Who this book is for

This book is for data engineers, software developers, and data architects looking for a quick hands-on Kafka guide.

This guide is about programming; it is an introduction for those with no previous knowledge about Apache Kafka.

All the examples are written in Java 8; experience with Java 8 is the only requirement for following this guide.

What this book covers

Chapter 1, Configuring Kafka, explains the basics for getting started with Apache Kafka. It discusses how to install, configure, and run Kafka. It also discusses how to make basic operations with Kafka brokers and topics.

Chapter 2, Message Validation, explores how to program data validation for your enterprise service bus, covering how to filter messages from an input stream.

Chapter 3, Message Enrichment, looks at message enrichment, another important task for an enterprise service bus. Message enrichment is the process of incorporating additional information into the messages of your stream.

Chapter 4, Serialization, talks about how to build serializers and deserializers for writing, reading, or converting messages in binary, raw string, JSON, or AVRO formats.

Chapter 5, Schema Registry, covers how to validate, serialize, deserialize, and keep a history of versions of messages using the Kafka Schema Registry.

Chapter 6, Kafka Streams, explains how to obtain information about a group of messages – in other words, a message stream – and how to obtain additional information, such as that to do with the aggregation and composition of messages, using Kafka Streams.

Chapter 7, KSQL, talks about how to manipulate event streams without a single line of code using SQL over Kafka Streams.

Chapter 8, Kafka Connect, talks about other fast data processing tools and how to make a data processing pipeline with them in conjunction with Apache Kafka. Tools such as Apache Spark and Apache Beam are covered in this chapter.

To get the most out of this book

The reader should have some experience of programming with Java 8.

The minimum configuration required for executing the recipes in this book is an Intel ® Core i3 Processor, 4 GB of RAM, and 128 GB of disk space. Linux or macOS is recommended, as Windows is not fully supported.

Download the example code files

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We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The --topic parameter sets the name of the topic; in this case, amazingTopic."

A block of code is set as follows:

{
   "event": "CUSTOMER_CONSULTS_ETHPRICE",
   "customer": {
         "id": "14862768",
         "name": "Snowden, Edward",
         "ipAddress": "95.31.18.111"
   },
   "currency": {
         "name": "ethereum",
         "price": "RUB"
   },
   "timestamp": "2018-09-28T09:09:09Z"
}

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

dependencies {
    compile group: 'org.apache.kafka', name: 'kafka_2.12', version:                                                             
                                                          '2.0.0'
    compile group: 'com.maxmind.geoip', name: 'geoip-api', version: 
                                                          '1.3.1'
    compile group: 'com.fasterxml.jackson.core', name: 'jackson-core', version: '2.9.7'
}

Any command-line input or output is written as follows:

> <confluent-path>/bin/kafka-topics.sh --list --ZooKeeper localhost:2181

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "To differentiate among them, the events on t1 have one stripe, the events on t2 have two stripes, and the events on t3 have three stripes."

Warnings or important notes appear like this.

Tips and tricks appear like this.

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