hands-on lab

Getting Started with Amazon Elastic MapReduce

Difficulty: Intermediate

Duration: Up to 1 hour and 45 minutes

Students: 4,003

Rating: 4.4/5

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Description

Amazon Elastic MapReduce (Amazon EMR) makes it easy to process vast amounts of data in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Amazon EMR uses Hadoop, an open-source framework, to distribute raw data and processing across a resizable cluster of Amazon EC2 instances.

Hadoop uses a distributed processing architecture called MapReduce in which a task is mapped to a set of servers for processing. The results of the computation performed by those servers are then reduced to a single output data set.

A high-level view of the EMR workflow is as follows:

Load the input dataset
Execute a Map-Reduce job
Store the job results in HDFS
View the job results from HDFS

The focus of this lab is configuring and launching an EMR cluster. You will be provided with sample input data sets and sample applications to process the data sets. Treating the application and data set as a "black box" will lift unneeded complexities and free you up to concentrate on the configuration component.

Please note that this lab involves creating a new Amazon EMR cluster which typically takes approximately ten minutes. Please ensure you have enough time available before starting the lab.