Stream Processing with Apache Spark
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€68.99
Before you can build analytics tools to gain quick insights, you first need to know how to process data in real time. With this practical guide, developers familiar with Apache Spark will learn how to put this in-memory framework to use for streaming data. You’ll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.
Authors Gerard Maas and François Garillot help you explore the theoretical underpinnings of Apache Spark. This comprehensive guide features two sections that compare and contrast the streaming APIs Spark now supports: the original Spark Streaming library and the newer Structured Streaming API.
Learn fundamental stream processing concepts and examine different streaming architectures
Explore Structured Streaming through practical examples; learn different aspects of stream processing in detail
Create and operate streaming jobs and applications with Spark Streaming; integrate Spark Streaming with other Spark APIs
Learn advanced Spark Streaming techniques, including approximation algorithms and machine learning algorithms
Compare Apache Spark to other stream processing projects, including Apache Storm, Apache Flink, and Apache Kafka Streams
Francois Garillot worked on Scala's type system in 2006, earned his PhD from the French Ecole Polytechnique in 2011, and worked at Typesafe, after a brief stint in Internet advertising. He's worked on interactive interfaces to the Scala compiler, while nourishing a strong enthusiasm for data analytics in his spare time, until Apache Spark let him fullfill this passion as his main job. He received the first Spark Certification in November 2014, and worked in London and Philadelphia, among other places. In his spare time, he can be found practicing one of a half-dozen ways of making coffee, climbing up or skiing down a not-necessarily-Alpine mountain, or sailing a not-necessarily coastal course.