Dylan Scott – författare

Kafka in Action is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this book probes Kafka's most common use cases, ranging from simple logging through managing streaming data systems for message routing, analytics, and more.

 

In systems that handle big data, streaming data, or fast data, it's important to get your data pipelines right. Apache Kafka is a wicked-fast distributed streaming platform that operates as more than just a persistent log or a flexible message queue.

 

Key Features

·   Understanding Kafka's concepts

·   Implementing Kafka as a message queue

·   Setting up and executing basic ETL tasks

·   Recording and consuming streaming data

·   Working with Kafka producers and consumers from Java applications

·   Using Kafka as part of a large data project team

·   Performing Kafka developer and admin tasks

 

Written for intermediate Java developers or data engineers. No prior knowledge of Kafka is required.

 

About the technology

Apache Kafka is a distributed streaming platform for logging and streaming data between services or applications. With Kafka, it's easy to build applications that can act on or react to data streams as they flow through your system. Operational data monitoring, large scale message processing, website activity tracking, log aggregation, and more are all possible with Kafka.

 

Dylan Scott is a software developer with over ten years of experience in Java and Perl. His experience includes implementing Kafka as a messaging system for a large data migration, and he uses Kafka in his work in the insurance industry.

Master the wicked-fast Apache Kafka streaming platform through hands-on examples and real-world projects.In Kafka in Action you will learn:     Understanding Apache Kafka concepts     Setting up and executing basic ETL tasks using Kafka Connect     Using Kafka as part of a large data project team     Performing administrative tasks     Producing and consuming event streams     Working with Kafka from Java applications     Implementing Kafka as a message queue Kafka in Action is a fast-paced introduction to every aspect of working with Apache Kafka. Starting with an overview of Kafka's core concepts, you'll immediately learn how to set up and execute basic data movement tasks and how to produce and consume streams of events. Advancing quickly, you’ll soon be ready to use Kafka in your day-to-day workflow, and start digging into even more advanced Kafka topics. About the technology Think of Apache Kafka as a high performance software bus that facilitates event streaming, logging, analytics, and other data pipeline tasks. With Kafka, you can easily build features like operational data monitoring and large-scale event processing into both large and small-scale applications. About the book Kafka in Action introduces the core features of Kafka, along with relevant examples of how to use it in real applications. In it, you’ll explore the most common use cases such as logging and managing streaming data. When you’re done, you’ll be ready to handle both basic developer- and admin-based tasks in a Kafka-focused team. What's inside     Kafka as an event streaming platform     Kafka producers and consumers from Java applications     Kafka as part of a large data project About the reader For intermediate Java developers or data engineers. No prior knowledge of Kafka required. About the author Dylan Scott is a software developer in the insurance industry. Viktor Gamov is a Kafka-focused developer advocate. At Confluent, Dave Klein helps developers, teams, and enterprises harness the power of event streaming with Apache Kafka. Table of Contents PART 1 GETTING STARTED 1 Introduction to Kafka 2 Getting to know Kafka PART 2 APPLYING KAFK 3 Designing a Kafka project 4 Producers: Sourcing data 5 Consumers: Unlocking data 6 Brokers 7 Topics and partitions 8 Kafka storage 9 Management: Tools and logging PART 3 GOING FURTHER 10 Protecting Kafka 11 Schema registry 12 Stream processing with Kafka Streams and ksqlDB