Apache Spark 2.x Cookbook

Rishi Yadav

  • 出版商: Packt Publishing
  • 出版日期: 2017-05-31
  • 售價: $1,600
  • 貴賓價: 9.5$1,520
  • 語言: 英文
  • 頁數: 294
  • 裝訂: Paperback
  • ISBN: 1787127265
  • ISBN-13: 9781787127265
  • 相關分類: Spark

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Apache Spark 2.x Cookbook-preview-1

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商品描述

Key Features

  • Contains recipes on solving real-time data-processing problems with Apache Spark
  • Utilize core Spark modules such as Spark SQL, Spark MLlib, Spark Streaming, and GraphX processing
  • A practical guide to help you master Apache Spark as your single big data computing platform

Book Description

While Apache Spark 1.x gained lot of traction and adoption in the early years, Spark 2.0 delivers very notable improvements in the areas of API, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data.

Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Furthermore, you will be introduced to working with RDD's, Data Frames to operate on data with schemas, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning, recommendation engines, deep learning algorithms, and GPU implementations on Spark.

Last but not the least, the final few chapters will help you delve more deeply into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.

What you will learn

  • Install and configure Apache Spark with various cluster managers
  • Set up a development environment for Apache Spark
  • Learn to operate on data in Spark with schemas
  • Get to grips with real-time streaming analytics using Spark Streaming
  • Master supervised learning and unsupervised learning using MLlib
  • Build a recommendation engine using MLlib
  • Use Tensorframes to manipulate Spark's DataFrames with TensorFlow programs for deep learning
  • Develop a set of common applications or project types, and solutions that solve complex big data problems