Using a cloud-based GPU database like MapD Core, and the Immerse visualization studio will pay dividends when designing and implementing a GIS. It offers speed and cloud reliability to both tabular and spatial queries and allows the data to be shared in interactive dashboards (which rely on JavaScript technologies such as D3.js and MapBox GL JavaScript) that are simple to create and publish. 

With the MapD Python module, pymapd, cloud data can become an integrated part of a query engine. Data can be pushed to the cloud or pulled down to use locally. Analyses can be performed rapidly, using the power of GPU parallelization. It's worth installing MapD on a virtual server in the cloud, or even locally, to test out the potential of the software.

In the next chapter, we will explore the use of Flask, SQLAlchemy, and GeoAlchemy2 to create an interactive web map with a PostGIS geodatabase backend.