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.
