Description 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, youll learn how to use Apache Flinks stream processing APIs to implement, continuously run, and maintain real-world applications.
Authors Fabian Hueske, one of Flinks creators, and Vasia Kalavri, a core contributor to Flinks 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 Flinks DataStream API, including the structure and components of a common Flink streaming application.
Solve real-world problems with Apache Flinks 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 About the author Fabian Hueske is involved with Apache Flink since its very beginnings in 2009 as a research project called Stratosphere at TU Berlin.
He is one of the three initial authors of the system and worked on it as part of his PhD research.
Fabian is one of the initial committers and a PMC member of Apache Flink.
He is a co-founder of data Artisans, a Berlin-based start-up devoted to foster Flink, where he works as a software engineer and contributes to Apache Flink.
Fabian wrote several well-received blog posts about Flink and its internals (published at the Apache Flink blog http: //flink.
apache.
org/blog, data Artisans blog http: //data-artisans.
com/blog, MapR blog, and Elastic blog).
Vasiliki Kalavri is a PhD student at KTH, Stockholm, and UCL, Belgium, and an EMJD-DC fellow.
She does research in distributed data processing and large-scale graph analysis.
She is a committer and PMC member of Apache Flink, focusing her efforts on its graph processing library and API, Gelly.
