Stream Processing with Apache Spark

Stream Processing with Apache SparkBefore 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. Youll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.'box sizing: border box; padding: 0px; margin top: 0em; margin bottom: 1em; margin left: 1em;

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. Youll discover how Spark enables you to write streaming jobs in almost the same way you write batch jobs.'box-sizing: border-box; padding: 0px; margin-top: 0em; margin-bottom: 1em; margin-left: 1em; font-family: "Amazon Ember", Arial, sans-serif; font-size: small; background-color: rgb(255, 255, 255);'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