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

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.