Stream Processing with Apache Flink: Fundamentals, Implementation, and Operation of Streaming Applications 1st Edition 21916

Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing.

Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them.

Learn concepts and challenges of distributed stateful stream processing
Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model
Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators
Read data from and write data to external systems with exactly-once consistency
Deploy and configure Flink clusters
Operate continuously running streaming applications

About the Author

Fabian Hueske is a committer to and PMC member of the Apache Flink project and has been contributing to Flink since its earliest days. Fabian is cofounder, software engineer, and community evangelist at data Artisans (now Ververica), a Berlin-based startup that fosters Flink and its community. He holds a PhD in computer science from TU Berlin.

Vasiliki (Vasia) Kalavri is a postdoctoral fellow in the Systems Group at ETH Zurich, where she uses Apache Flink extensively for streaming systems research and teaching. Vasia is a PMC member of the Apache Flink project. An early contributor to Flink, she has worked on its graph processing library, Gelly, and on early versions of the Table API and streaming SQL.

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 creators, and Vasia Kalavri, a core contributor to Flink’s graph processing API (Gelly), explains 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 for applications Flink
Design streaming applications and migrate periodic batch workloads to continuous streaming workloads
Learn about windowed operations process that 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