- Understand how Spark can be distributed across computing clusters
- Develop and run Spark jobs efficiently using Python
- A hands-on tutorial by Frank Kane with over 15 real-world examples teaching you Big Data processing with Spark
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.
What you will learn
- Find out how you can identify 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 continuous 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
About the Author
My name is Frank Kane. I spent nine years at Amazon and IMDb, wrangling millions of customer ratings and customer transactions to produce things such as personalized recommendations for movies and products and “people who bought this also bought.” I tell you, I wish we had Apache Spark back then, when I spent years trying to solve these problems there. I hold 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, I left to start my own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.
Table of Contents
- Getting Started with Spark
- Spark Basics and Simple 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