Fast Data Processing with Spark, 2nd Edition Front Cover

Fast Data Processing with Spark, 2nd Edition

  • Length: 184 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2015-03-31
  • ISBN-10: 178439257X
  • ISBN-13: 9781784392574
  • Sales Rank: #1514648 (See Top 100 Books)

Perform real-time analytics using Spark in a fast, distributed, and scalable way

About This Book

  • Develop a machine learning system with Spark’s MLlib and scalable algorithms
  • Deploy Spark jobs to various clusters such as Mesos, EC2, Chef, YARN, EMR, and so on
  • This is a step-by-step tutorial that unleashes the power of Spark and its latest features

Who This Book Is For

Fast Data Processing with Spark – Second Edition is for software developers who want to learn how to write distributed programs with Spark. It will help developers who have had problems that were too big to be dealt with on a single computer. No previous experience with distributed programming is necessary. This book assumes knowledge of either Java, Scala, or Python.

In Detail

Spark is a framework used for writing fast, distributed programs. Spark solves similar problems as Hadoop MapReduce does, but with a fast in-memory approach and a clean functional style API. With its ability to integrate with Hadoop and built-in tools for interactive query analysis (Spark SQL), large-scale graph processing and analysis (GraphX), and real-time analysis (Spark Streaming), it can be interactively used to quickly process and query big datasets.

Fast Data Processing with Spark – Second Edition covers how to write distributed programs with Spark. The book will guide you through every step required to write effective distributed programs from setting up your cluster and interactively exploring the API to developing analytics applications and tuning them for your purposes.

Table of Contents

Chapter 1: Installing Spark and Setting up your Cluster
Chapter 2: Using the Spark Shell
Chapter 3: Building and Running a Spark Application
Chapter 4: Creating a SparkContext
Chapter 5: Loading and Saving Data in Spark
Chapter 6: Manipulating your RDD
Chapter 7: Spark SQL
Chapter 8: Spark with Big Data
Chapter 9: Machine Learning Using Spark MLlib
Chapter 10: Testing
Chapter 11: Tips and Tricks

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