Razavi and Michael Johns walk us through examples of processing geospatial data with Databricks:
Earlier, we loaded our base data into a DataFrame. Now we need to turn the latitude/longitude attributes into point geometries. To accomplish this, we will use UDFs to perform operations on DataFrames in a distributed fashion. Please refer to the provided notebooks at the end of the blog for details on adding these frameworks to a cluster and the initialization calls to register UDFs and UDTs. For starters, we have added GeoMesa to our cluster, a framework especially adept at handling vector data. For ingestion, we are mainly leveraging its integration of JTS with Spark SQL which allows us to easily convert to and use registered JTS geometry classes. We will be using the function st_makePoint that given a latitude and longitude create a Point geometry object. Since the function is a UDF, we can apply it to columns directly.
Looks like they have some pretty good functionality here, and they have shared the demos in notebook form.