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Distributed Computing with Python

You're reading from   Distributed Computing with Python Harness the power of multiple computers using Python through this fast-paced informative guide

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781785889691
Length 170 pages
Edition 1st Edition
Languages
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Author (1):
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Rasheedh B Rasheedh B
Author Profile Icon Rasheedh B
Rasheedh B
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Table of Contents (10) Chapters Close

Preface 1. An Introduction to Parallel and Distributed Computing 2. Asynchronous Programming FREE CHAPTER 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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