What's the best cloud platform for your GIS project?

1 Amazon Web Services (AWS)

AWS is one of the most widely used cloud platforms for GIS, and for good reasons. AWS offers a comprehensive range of services and tools for GIS, such as Amazon EC2 for computing, Amazon S3 for storage, Amazon RDS for databases, Amazon SNS for notifications, and Amazon Lambda for serverless functions. AWS also has a dedicated service for GIS, called Amazon Location Service, which provides maps, geocoding, routing, tracking, and geofencing capabilities. AWS is known for its reliability, security, and innovation, and it supports many open-source and proprietary GIS software and formats. However, AWS can also be complex, expensive, and overwhelming for beginners, and it requires a lot of configuration and management.

2 Google Cloud Platform (GCP)

GCP is another popular cloud platform for GIS, and it has some unique features and advantages. GCP offers a similar range of services and tools for GIS as AWS, such as Google Compute Engine for computing, Google Cloud Storage for storage, Google Cloud SQL for databases, Google Cloud Pub/Sub for messaging, and Google Cloud Functions for serverless functions. GCP also has a specialized service for GIS, called Google Maps Platform, which provides maps, geocoding, directions, places, and roads APIs. GCP is known for its simplicity, performance, and integration, and it supports many open-source and proprietary GIS software and formats. However, GCP can also be limited, inconsistent, and restrictive for some use cases, and it requires a lot of dependency and compatibility.

3 Microsoft Azure

Azure is another major cloud platform for GIS, and it has some strong points and drawbacks. Azure offers a similar range of services and tools for GIS as AWS and GCP, such as Azure Virtual Machines for computing, Azure Blob Storage for storage, Azure SQL Database for databases, Azure Service Bus for messaging, and Azure Functions for serverless functions. Azure also has a specific service for GIS, called Azure Maps, which provides maps, geocoding, routing, traffic, spatial operations, and time zone APIs. Azure is known for its compatibility, diversity, and support, and it supports many open-source and proprietary GIS software and formats. However, Azure can also be costly, unstable, and confusing for some scenarios, and it requires a lot of documentation and troubleshooting.

4 Other Cloud Platforms

Besides AWS, GCP, and Azure, there are also other cloud platforms that offer GIS capabilities, such as IBM Cloud, Oracle Cloud, Alibaba Cloud, and Heroku. These cloud platforms have their own pros and cons, and they may suit different needs and preferences. For example, IBM Cloud has a service called IBM Cloud Pak for Data, which provides data and AI services, including geospatial analytics. Oracle Cloud has a service called Oracle Spatial and Graph, which provides spatial and graph data management and analysis. Alibaba Cloud has a service called Amap, which provides maps, geocoding, navigation, and location-based services. Heroku has a service called Heroku Postgres, which supports PostGIS, a spatial extension for PostgreSQL. These cloud platforms may have lower costs, higher availability, or better performance than the big three, but they may also have less features, less support, or less compatibility than them.

5 How to Choose the Best Cloud Platform for Your GIS Project

There is no definitive answer to which cloud platform is the best for your GIS project, as it depends on various factors, such as your project's goals, requirements, budget, skills, and preferences. However, there are some general steps you can take to make an informed decision. First, you should define your project's scope and specifications, such as the type, size, and frequency of your data, the complexity and functionality of your analysis, the level and quality of your output, and the duration and frequency of your project. Second, you should compare the features and costs of different cloud platforms, such as the services and tools they offer, the pricing and billing models they use, the security and reliability they guarantee, and the support and documentation they provide. Third, you should test and evaluate different cloud platforms, such as by creating a prototype, running a trial, or conducting a benchmark. Finally, you should choose the cloud platform that best meets your project's needs and expectations, and that you feel comfortable and confident with.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?