Overview

Teaching: 15 min
Exercises: 0 min

Questions

• How can cloud computing help

Objectives

• learn about our overall computing framework for geohackweek

Cloud computing fundamentals

Benefits of cloud computing

• On-demand, shared and configurable pool of resources
• Rapidly provisioned
• Highly scalable
• Cattle not pets

** Think about using cloud resources for data sharing and collaboration **

Types of cloud computing services

** Terminology/definitions becoming less relevant **

• Infrastructure as a service (IaaS)
  • E.g. virtual machines with operating systems, like traditional servers
• Platform as a service (PaaS)
  • No need to maintain hardware or operating systems
  • E.g. Google App Engine, web applications
• Software as a service (SaaS)
  • E.g. ArcGIS on the cloud, Google Docs, email
• Data as a service
  • Collected data is shared in human readable form

Definitions are becoming less relevant

Important things to think about

• How do I choose a cloud provider? (e.g. AWS, Azure, Google Cloud Platform)
• Costing
• Pervasiveness of vendor in field
• Continuous, integrated delivery (build-test-share)
  • Use Github for collaboration and coordination

Examples of Applications on the Cloud with a Geospatial focus

• Web frameworks with API (AralDIF)
  • Elastic Beanstalk. Django (Python) for webframework
  • API building
• Geoserver for data sharing (Nanoos)
  • Open-source geospatial server written in Java that allows users to share, process and edit geospatial data
  • Leaflet frontend
• Jupyterhub

Getting on board

Google: Tim has credits, no CC necessary AWS: AWS for Research Credits ($100) Azure: Azure for Research ($200)

Key Points

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