Book Image

Frank Kane's Taming Big Data with Apache Spark and Python

By : Frank Kane
Book Image

Frank Kane's Taming Big Data with Apache Spark and Python

By: Frank Kane

Overview of this book

Frank Kane’s Taming Big Data with Apache Spark and Python is your companion to learning Apache Spark in a hands-on manner. Frank will start you off by teaching you how to set up Spark on a single system or on a cluster, and you’ll soon move on to analyzing large data sets using Spark RDD, and developing and running effective Spark jobs quickly using Python. 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, making it an essential tool in many modern businesses. Frank has packed this book with over 15 interactive, fun-filled examples relevant to the real world, and he will empower you to understand the Spark ecosystem and implement production-grade real-time Spark projects with ease.
Table of Contents (13 chapters)
Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
7
Where to Go From Here? – Learning More About Spark and Data Science

What is Spark?


According to Apache, Spark is a fast and general engine for large-scale data processing. This is actually a really good summary of what it's all about. If you have a really massive dataset that can represent anything - weblogs, genomics data, you name it - Spark can slice and dice that data up. It can distribute the processing among a huge cluster of computers, taking a data analysis problem that's just too big to run on one machine and divide and conquer it by splitting it up among multiple machines.

Spark is scalable

The way that Spark scales data analysis problems is, it runs on top of a cluster manager, so your actual Spark scripts are just everyday scripts written in Python, Java, or Scala; they behave just like any other script. Your "driver program" is what we call it, and it will run on your desktop or on one master node of your cluster. However, under the hood, when you run it, Spark knows how to take the work and actually farm it out to different computers on your...