Since 2011, Kafka's growth has exploded. More than one-third of all Fortune 500 companies use Apache Kafka. These companies include the top 10 travel companies, 7 of the top 10 banks, 8 of the top 10 insurance companies, and 9 of the top 10 telecom companies.
LinkedIn, Uber, Twitter, Spotify, Paypal, and Netflix process with Apache Kafka, each one with a total of four-comma (1,000,000,000,000) messages in a single day.
Nowadays, Apache Kafka is used for real-time data streaming, to collect data, or to do real-time data analyses. In other contexts, Kafka is used in microservice architectures to improve durability. It can also be used to feed events to Complex Event Processing (CEP) architectures and IoT automation systems.
Today we live in the middle of a war, a streaming war. Several competitors (Kafka Streams, Spark Streaming, Akka Streaming, Apache Flink, Apache Storm, Apache Beam, Amazon Kinesis, and so on) are immersed in a competition where there are many factors to evaluate, but mainly the winner is the one with the best performance.
Much of the current adoption of Apache Kafka is due to its ease of use. Kafka is easy to implement, easy to learn, and easy to maintain. Unlike most of its competitors, the learning curve is not so steep.
This book is practical; it is focused on hands-on recipes and it isn't just stop at theoretical or architectural explanations about Apache Kafka. This book is a cookbook, a compendium of practical recipes that are solutions to everyday problems faced in the implementation of a streaming architecture with Apache Kafka. The first part of the book is about programming, and the second part is about Apache Kafka administration.
Chapter 1, Configuring Kafka, explains the basic recipes used to get started with Apache Kafka. It discusses how to install, configure, and run Kafka. It also discusses how to do basic operations with a Kafka broker.
Chapter 2, Kafka Clusters, covers how to make three types of clusters: single-node single-broker cluster, single-node multiple-broker cluster, and multiple-node multiple-broker cluster.
Chapter 3, Message Validation, in this chapter having an enterprise service bus, one of the tasks is related to data validation, this is filtering some events from an input message stream. This chapter is about the programming of this validation.
Chapter 4, Message Enrichment, details how the next task of an enterprise service bus is related to message enrichment, which means having an individual message, obtaining additional information, and incorporating it into the message stream.
Chapter 5, The Confluent Platform, shows how to operate and monitor a Kafka system with the Confluent Platform. It also explains how to use the Schema Registry, the Kafka REST Proxy, and Kafka Connect.
Chapter 6, Kafka Streams, explains how to obtain information about a group of messages (a message stream) and additional information such as aggregation and composition of messages using Kafka Streams.
Chapter 7, Managing Kafka, talks about the command-line tools developed by the authors of Kafka to make a sysadmin team's life easier when debugging, testing, and running a Kafka cluster.
Chapter 8, Operating Kafka, explains the different operations that can be done on a Kafka cluster. These tools cannot be used daily, but they help the DevOps team manage Kafka clusters.
Chapter 9, Monitoring and Security, has a first half that talks about various statistics, how they are exposed, and how to monitor them with tools such as Graphite and Ganglia. Its second part is about security—in a nutshell, how to implement SSL authentication, SASL/Kerberos authentication, and SASL/plain authentication.
Chapter 10, Third-Party Tool Integration, talks about other real-time data processing tools and how to use Apache Kafka to make a data processing pipeline with them. Tools such as Hadoop, Flume, Gobblin, Elastic, Logstash, Spark, Storm, Solr, Akka, Cassandra, Mesos, and Beam are covered in this chapter.
The reader should have some experience in programming with Java and some experience in Linux/Unix operating systems.
The minimum configuration needed to execute the recipes in this book is: Intel ® Core i3 processor, 4 GB RAM, and 128 GB of disks. It is recommended to use Linux or Mac OS. Windows is not fully supported.
This book is for software developers, data architects, and data engineers looking for practical Kafka recipes.
The first half of this cookbook is about programming; this is introductory material for those with no previous knowledge of Apache Kafka. As the book progresses, the difficulty level increases.
The second half of this cookbook is about configuration; this is advanced material for those who want to improve existing Apache Kafka systems or want to better administer current Kafka deployments.
In this book, you will find several headings that appear frequently (Getting ready, How to do it…, How it works…, There's more…, and See also). To give clear instructions on how to complete a recipe, we use these sections as follows.
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Finally, run the apt-get update to install the Confluent Platform."
A block of code is set as follows:
consumer.interceptor.classes=io.confluent.monitoring.clients.interceptor.MonitoringConsumerInterceptor producer.interceptor.classes=io.confluent.monitoring.clients.interceptor.MonitoringProducerInterceptor
Any command-line input or output is written as follows:
> bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic SNSBTopicNew terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "From Kafka Connect, click on the SINKS button and then on the New sink button."
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