Analyzing Big Data with Hadoop, AWS, and EMR

Hadoop is today's most pervasive technology used in Big Data for distributing the processing of massive data sets across clusters of commodity computers. With Amazon's Elastic MapReduce service (EMR), you can rent capacity through Amazon Web Services (AWS) to store and analyze data at minimal cost on top of a real Hadoop cluster.

This course shows you how to use an EMR Hadoop cluster via a real life example where you'll analyze movie ratings data using Hive, Pig, and Oozie. It focuses on practical tips for using an EMR cluster efficiently, integrating the cluster with Amazon's S3 service, and determining the right money-saving size for a cluster. You'll learn how to interact with your cluster through the Hue Web interface, from a terminal prompt, as well as through EMR steps that can execute your scripts automatically.

• Gain experience with three high value skill sets: Hadoop, AWS, and EMR
• Save time and money by learning about the undocumented "gotchas" of AWS and EMR
• See how the experts provision EMR clusters and connect to them via SSH and web UIs
• Learn to import data into a cluster and to access external data stored on Amazon's S3
• Explore three different ways to query data using Hive and Pig
• Discover the Tez engine and see how it accelerates Hive and Pig queries
• Learn how to schedule workflows using Oozie

Frank Kane spent 9 years at Amazon and IMDb developing and managing the technology that delivers product recommendations to hundreds of millions of customers. Frank holds 17 patents in the fields of distributed computing, data mining, and machine learning. He now runs Sundog Software, a software company focused on virtual reality technology and on Big Data analysis training. He is the author of multiple titles on Spark, MapReduce, Spark Streaming, and Python.