This content is also available at learn.palantir.com ↗ and is presented here for accessibility purposes.
As you've seen, geospatial data makes its way into tabular and Ontology data the same way as other columns and properties. Consequently, your data engineer colleagues will generally apply the same lifecycle processes and health monitoring to geospatial data that they do for other Foundry data.
Your colleagues may choose, however, to apply specialized, code-based health checks capable of detecting malformed geoJSON or geohashes using a Foundry capability known as Data Expectations. Using Data Expectations, for example, your team can define a health check on a geoJSON column that stops the build if any row has fewer { than }, or on a geohash column where decimal values of specific links aren't delimited by a ,.
What happens if I'd like to use a dataset for analysis but the geospatial data isn't formatted the way I want?
Foundry data transformation applications like Code Repositories provide data engineers with common geospatial libraries for transforming raster and vector data. If you'd like to learn more about the available geospatial libraries and functions that can be used in data transformations, take a look at the following articles: