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The following sections document best practices for using the cloud to store, manage, and access imagery for visualization and analysis. This includes a discussion of cloud storage options for imagery, recommended cloud-based image management workflows, and guidance for deploying ArcGIS Image Server in the cloud.

What is the cloud?

The cloud is a network of remote servers hosted on the internet that gives you the ability to store, manage, and process data instead of using your local servers or personal computers. Rather than implement and manage your own infrastructure on-premises, you essentially rent equipment in someone else’s infrastructure and use it remotely.

How is the cloud different from on-premises architecture?

Implementation and management of cloud-based infrastructure is different from what you have on-premises in several important ways:

  • Storage model—The storage model is different. In the cloud, you don’t use a file system (they exist, but it’s a good idea to not use them). Instead, object storage (accessed via HTTPS) is an inexpensive storage solution for large quantities of imagery.
  • Elasticity and pay-for-use cost model—The cloud is elastic and scalable. Scale up and scale down as required; you don’t have to purchase the infrastructure, just rent it. You only pay for the storage and computing power you use.
  • Security—Security in the cloud involves different considerations from local or enterprise storage. Cloud storage is designed to be accessed anytime, anywhere, and supports a broader set of users and applications. Given this, there are many security models to protect your data and limit access.
  • Test and scale—One way to take advantage of the cloud’s elasticity is to test and scale. In an enterprise environment, you often plan your infrastructure implementation, deciding up front how many servers, and so on, you’ll need and purchasing them. In the cloud, you start out small, test, and then increase capacity if you want to make it bigger (or decrease to make it smaller).

It’s important to remember that the cloud is not a good solution for all organizations. It will improve some things and make others more complicated.

The cloud is generally appropriate for the following cases:

  • You’re looking for an inexpensive way to store large collections of imagery.
  • Your own infrastructure is getting expensive and hard to maintain.

Many organizations are transitioning to cloud-first policies, meaning they prefer to store and access data directly from the cloud instead of managing data on their own infrastructure.

Read this GeoNet blog post for a good overview of general considerations before moving your GIS to the cloud.

Esri-supported cloud platforms

When it comes to image management—how to store and disseminate imagery to make it accessible to end users and applications—a number of cloud-based solutions are available:

  • ArcGIS OnlineArcGIS Online is a cloud-based SaaS offering that allows you to upload tile cache into the cloud and serve it back as a basemap or elevation surface.
  • Amazon Web Services and Microsoft Azure—Esri supports both of these commercial cloud platforms with implementation tools to streamline a complete or partial cloud-based ArcGIS implementation.
  • Other cloud platforms—Implement ArcGIS manually on most cloud platforms.

Components of a cloud-based imagery solution

There are several components of a complete imagery solution in the cloud. You can implement all of them in the cloud, or just parts. It’s important to think about which aspects are relevant for your organization and which make sense to perform in the cloud, if any.

  • Storage—How are you going to store the original imagery you received? The optimized imagery? You may store them in the same place, or you may decide you want to store the original data locally and the optimized data in the cloud.
  • Management—If you have collections of imagery, you'll need to create mosaic datasets to manage them. Where are you going to author these mosaic datasets: locally or in the cloud? (It’s best to author mosaic datasets wherever you’re storing your imagery.)
  • Sharing—What will end users (or applications) need to do with the imagery: visualization or analysis? Is it enough to serve tile cache, or will users need dynamic image services to do on-the-fly processing or perform analytics?
  • Perform analytics—Will you want to analyze your data using distributed cloud-based raster analytics? You may want to share the results of analysis rather than the original data.
  • Access control—Are you accessing the final product through ArcGIS Online or your own ArcGIS Enterprise portal? Will the imagery be used internally, requiring secure access, or be available publicly?

Processing, including serving data or any analytics, should happen in the same infrastructure where the images are stored; otherwise, the large data transfer will be slow and could incur a high egress cost.