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Distributed Computing with Python

You're reading from  Distributed Computing with Python

Product type Book
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Pages 170 pages
Edition 1st Edition
Languages
Toc

Table of Contents (15) Chapters close

Distributed Computing with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming 3. Parallelism in Python 4. Distributed Applications – with Celery 5. Python in the Cloud 6. Python on an HPC Cluster 7. Testing and Debugging Distributed Applications 8. The Road Ahead Index

Storing data in Amazon S3


Amazon Simple Storage Service, S3, is a web service that can be used to store and retrieve arbitrary blobs of data. Data stored in S3 can include files of any kind, up to 5 terabytes in size (at the time of writing this), and also raw bytes.

S3 is also significantly cheaper than EBS; however, it does not offer a filesystem layer but rather a REST API. Another difference is that while EBS volumes can only be attached to a single running instance at a time, S3 objects can be shared among as many instances as we want, and depending on the desired permission policy, they can be accessed from anywhere on the Internet.

Getting started with S3 is easy; you need to create a number of buckets (that is, data containers in S3 parlance) in relevant geographical areas (usually, in order to minimize access times) and then add data to them. The process is as follows; if you're not there already, log in to the AWS management console and click on the S3 icon under Storage & Content...

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