Who should learn Spatial SQL?
Teams working with data in SQL based databases or spatially enabled data warehouses
Spatial data scientists or data scientists using spatial data
GIS users looking for more flexibility in their analysis and workflows
Developers creating map-centric apps using spatial data
What are the advantages of Spatial SQL?
Work with larger scale Big Data in SQL enabled data warehouses
Ensure more efficiency in your analytics workflows
Enable more cross-functional collaboration, avoiding silos due to different languages being used
Find more repeatability inside and outside of your organization
Which types of data are typically analyzed with Spatial SQL?
Spatial data can be anything from addresses & latitude/longitude coordinates, to points, lines & polygons. You can also create spatial data with place names & administrative units such as countries and states.
However, as well as using internal data from their organizations (such as CRM, loyalty card, ecommerce), organizations also regularly gather publically available Open Data to enrich their analysis.
More & more premium spatial data streams such as Financial, Human Mobility, Road Traffic, Points of Interest, Weather, Climate & Housing are also being used.
How can you get started using Spatial SQL?
With more organizations looking to carry out spatial analysis, the number of resources available for those looking to get started with Spatial SQL is growing rapidly. There are a wide range of podcasts, webinars, tutorials, events and communities to get involved in - some of which can help you get started and some of which are more advanced.
