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MongoDB Cookbook - Second Edition

You're reading from   MongoDB Cookbook - Second Edition Modern Database Management Made Easy

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785289989
Length 370 pages
Edition 2nd Edition
Tools
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Authors (2):
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Amol Nayak Amol Nayak
Author Profile Icon Amol Nayak
Amol Nayak
Cyrus Dasadia Cyrus Dasadia
Author Profile Icon Cyrus Dasadia
Cyrus Dasadia
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Toc

Table of Contents (12) Chapters Close

Preface 1. Installing and Starting the Server 2. Command-line Operations and Indexes FREE CHAPTER 3. Programming Language Drivers 4. Administration 5. Advanced Operations 6. Monitoring and Backups 7. Deploying MongoDB on the Cloud 8. Integration with Hadoop 9. Open Source and Proprietary Tools A. Concepts for Reference Index

Running a MapReduce job on Amazon EMR


This recipe involves running the MapReduce job on the cloud using AWS. You will need an AWS account in order to proceed. Register with AWS at http://aws.amazon.com/. We will see how to run a MapReduce job on the cloud using Amazon Elastic Map Reduce (Amazon EMR). Amazon EMR is a managed MapReduce service provided by Amazon on the cloud. Refer to https://aws.amazon.com/elasticmapreduce/ for more details. Amazon EMR consumes data, binaries/JARs, and so on from AWS S3 bucket, processes them and writes the results back to S3 bucket. Amazon Simple Storage Service (Amazon S3) is another service by AWS for data storage on the cloud. Refer to http://aws.amazon.com/s3/ for more details on Amazon S3. Though we will use the mongo-hadoop connector, an interesting fact is that we won't require a MongoDB instance to be up and running. We will use the MongoDB data dump stored in an S3 bucket for our data analysis. The MapReduce program will run on the input BSON dump...

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