Stream Processing with Apache Flink Scala
By Toby Weston, Software Engineer, data Artisans
By Toby Weston, Software Engineer, data Artisans and Vasiliki Kalavri, PhD candidate, Fellow of the Erasmus Mundus Joint Doctorate in Distributed Computing
Note: this preview download contains chapters 2 and 3 of Stream Processing with Apache Flink
Get started with Apache Flink, the open source framework that enables you to process streaming data - such as user interactions, sensor data, and machine logs - as it arrives. With this practical guide, you’ll learn how to use Apache Flink's stream processing APIs to implement, continuously run, and maintain real-world applications.
Authors Fabian Hueske, one of Flink’s initial authors, and post-doctoral rearcher Vasia Kalavri explain the fundamental concepts of parallel stream processing and shows you how streaming analytics differs from traditional batch data analysis. Software engineers, data engineers, and system administrators will learn the basics of Flink’s DataStream API, including the structure and components of a common Flink streaming application.
- Solve real-world problems with Apache Flink's DataStream API
- Set up an environment for developing stream processing applications for Flink
- Design streaming applications and migrate periodic batch workloads to continuous streaming workloads
- Learn about windowed operations that process groups of records
- Ingest data streams into a DataStream application and emit a result stream into different storage systems
- Implement stateful and custom operators common in stream processing applications
- Operate, maintain, and update continuously running Flink streaming applications
- Explore several deployment options, including the setup of highly available installations
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