Scale Your Spatial Analysis By Building It In SQL With Syntax Extensions
Data Engineering Podcast
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How Much Background Knowledge Is Necessary?
There's basically two spatial data types, if you think of that from a sequel perspective: geometry and geography. The first is when your data lives in a projected coordinate system, or the flat representation of the earth. The other one is a geography, where your data is represented as spherical cordnats on the earth. Yethars set up an infe structureo so people who are starting to work with these geo spatial data form ads can understand how much background knowledge is necessary for their analyses.
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Summary
Along with globalization of our societies comes the need to analyze the geospatial and geotemporal data that is needed to manage the growth in commerce, communications, and other activities. In order to make geospatial analytics more maintainable and scalable there has been an increase in the number of database engines that provide extensions to their SQL syntax that supports manipulation of spatial data. In this episode Matthew Forrest shares his experiences of working in the domain of geospatial analytics and the application of SQL dialects to his analysis.
Announcements
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- Your host is Tobias Macey and today I’m interviewing Matthew Forrest about doing spatial analysis in SQL
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what spatial SQL is and some of the use cases that it is relevant for?
- compatibility with/comparison to syntax from PostGIS
- What is involved in implementation of spatial logic in database engines
- mapping geospatial concepts into declarative syntax
- foundational data types
- data modeling
- workflow for analyzing spatial data sets outside of database engines
- translating from e.g. geopandas to SQL
- level of support in database engines for spatial data types
- What are the most interesting, innovative, or unexpected ways that you have seen spatial SQL used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working with spatial SQL?
- When is SQL the wrong choice for spatial analysis?
- What do you have planned for the future of spatial analytics support in SQL for the Carto platform?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
- Carto
- Spatial SQL Blog Post
- Spatial Analysis
- PostGIS
- QGIS
- KML
- Shapefile
- GeoJSON
- Paul Ramsey’s Blog
- Norwegian SOSI
- GDAL
- Google Cloud Dataflow
- GeoBEAM
- Carto Data Observatory
- WGS84 Projection
- EPSG Code
- PySAL
- GeoMesa
- Uber H3 Spatial Indexing
- PGRouting
- Spatialite
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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