Apache Spark 2.x for Java Developers: Explore big data at scale using Apache Spark 2.x Java APIs

Sourav Gulati, Sumit Kumar

  • 出版商: Packt Publishing
  • 出版日期: 2017-07-27
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 350
  • 裝訂: Paperback
  • ISBN: 1787126498
  • ISBN-13: 9781787126497
  • 相關分類: Java 程式語言Spark

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Key Features

  • Perform Big Data processing with Spark-without having to learn Scala!
  • Use the Spark Java API to implement efficient enterprise-grade applications for data processing and analytics
  • Go beyond the mainstream data processing by adding querying capability, machine learning, and graph processing using Spark

Book Description

Apache Spark is the buzzword in the Big Data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone.

The book starts with introduction to the Apache Spark ecosystem, followed by explaining the Spark installation and configuration, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near real-time processing with Spark streaming, machine learning analytics with Spark MLlib, and graph processing with GraphX using the various Java packages.

By the end of the book, you will have a solid foundation in implementing the components in the Spark framework in Java to build fast, real-time applications

What you will learn

  • Process data using different file formats such as XML, JSON, CSV, and plain and delimited text using Spark core Library
  • Perform analytics on data from various data sources such as Kafka, Flume, and Twitter using Spark Streaming Library
  • Learn SQL schema creation and analysis of structured data using various SQL functions including Windowing functions of Spark SQL Library
  • Explore the Spark Mlib APIs while implementing machine learning techniques to solve real-world problems
  • Get to know Spark GraphX so you understand various Graph-based analytics that can be performed with Spark