• Title: Taming Big Data with Apache Spark and Python – Hands On!
• Author: Frank Kane
• Length: 81 pages
• Edition: 1
• Language: English
• Publisher: Packt Publishing
• Publication Date: 2017-07-06
• ISBN-10: 1787287947
• ISBN-13: 9781787287945
  

Key Features

• Understand how Spark can be distributed across computing clusters
• Develop and run Spark jobs efficiently using Python
• A hands-on tutorial with over 15 real-world examples teaching you Big Data processing with Spark
  

Book Description
Apache Spark has emerged as the next big thing in the Big Data domain - quickly rising from an ascending technology to an established superstar in just a matter of years. Spark allows you to quickly extract actionable insights from large amounts of data, on a real-time basis. This book is your companion to learn Apache Spark in a hands-on manner. Start with understanding how to set up Spark on a single system or on a cluster. From analyzing large data sets using Spark RDD to developing and running effective Spark jobs quickly using Python, this course will teach you everything. Packed with over 15 interactive, fun-filled examples relevant to the real-world, the course will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.


What you will learn

• Learn how you can identify the Big Data problems as Spark problems
• Install and run Apache Spark on your computer or on a cluster
• Analyze large data sets across many CPUs using Spark's Resilient Distributed Datasets
• Implement machine learning on Spark using the MLlib library
• Process continuos streams of data in real time using the Spark streaming module
• Perform complex network analysis using Spark's GraphX library
• Use Amazon's Elastic MapReduce service to run your Spark jobs on a cluster
  

Table of Contents
Chapter 1. Getting Started with Spark
Chapter 2. Spark Basics and Spark Examples
Chapter 3. Advanced Examples of Spark Programs
Chapter 4. Running Spark on a Cluster
Chapter 5. SparkSQL, DataFrames, and DataSets
Chapter 6. Other Spark Technologies and Libraries
Chapter 7. Where to Go From Here? – Learning More About Spark and Data Science