Raul Estrada - Apache Kafka Quick Start Guide: Leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications

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Ral Estrada has been a programmer since 1996 and a Java developer since 2001. He loves all topics related to computer science. With more than 15 years of experience in high-availability and enterprise software, he has been designing and implementing architectures since 2003. His specialization is in systems integration, and he mainly participates in projects related to the financial sector. He has been an enterprise architect for BEA Systems and Oracle Inc., but he also enjoys web, mobile, and game programming. Ral is a supporter of free software and enjoys experimenting with new technologies, frameworks, languages, and methods.

Ral is the author of other Packt Publishing titles, such as Fast Data Processing Systems with SMACK and Apache Kafka Cookbook.

Isaac RuizGuerra has been a Java programmer since 2001 and an IT consultant since 2003. Isaac is specialized in systems integration, and has participated in projects to do with the financial sector. Isaac has worked mainly on the backend side, using languages such as Java, Python, and Elixir. For more than 10 years, he has worked with different application servers for the Java world, including JBoss, Glassfish, and WLS. Isaac is currently interested in topics such as microservices, cloud native, and serverless. He is a regular lecturer, mainly at conferences related to the JVM. Isaac is interested in the formation of interdisciplinary and high-performance teams.

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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 .

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