The last two things I want to cover in this chapter are Spark Streaming and GraphX, which are two technologies built on top of Spark. Spark Streaming handles continually incoming real-time data, say from a series of web logs from web servers that are running all the time. GraphX offers some tools for network analysis, kind of like the social network analysis that we were doing back when we were looking at superheroes and their relationships to each other. I'm going to cover Spark Streaming and GraphX at kind of a hand-wavey high level, because they're currently incomplete. Neither one really has good support for Python yet, right now they only work in Scala and they're in the process of being ported to Java and Python. However, by the time you read this book they might very well be available. So, all we can do is talk about them at this point, but I think you'll get the idea. So, follow along and let's see what's there.
Frank Kane's Taming Big Data with Apache Spark and Python
By :
Frank Kane's Taming Big Data with Apache Spark and Python
By:
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.
Related Content you might be interested in
Current Title:
Frank Kane's Taming Big Data with Apache Spark and Python
Table of Contents (13 chapters)
Title Page
Credits
About the Author
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Spark
Spark Basics and Spark Examples
Advanced Examples of Spark Programs
Running Spark on a Cluster
SparkSQL, DataFrames, and DataSets
Other Spark Technologies and Libraries
Where to Go From Here? – Learning More About Spark and Data Science
Customer Reviews