This post demonstrates how you can leverage any geographic dataset in GeoJson format and use it for geospatial analytics in Teradata, with just a few lines of code.
Today we be gathering reference geographical data (official maps, points of interest, etc...) form public sources and use it to support our day to day analytics.
You will learn two methods to get your GeoJson data into Teradata:
- Load it as a single document and use native Teradata Database functions to parse it into a table usable for analytics.
- Lightly transform it in native Python as we load it into Teradata to produce an analytics ready dataset.
The first method is a straig forward ELT pattern for semi-structured format processing in Teradata with a single SQL statement, the second one involves some lightweight preparation in (pure) Python and may allow more flexibility (for example to add early quality checks or optimize the load of large documents).