I never did proper "data engineering" per se, but we did have casual run-ins with geodatasets and GIS, and used them to build a home solar calculator that takes a ZIP code and estimates that area's available yearly sunlight, utility rates, electricity consumption, etc. and combine it into an estimated size for a home solar system. It was like a primitive version of Google Sunroof (https://sunroof.withgoogle.com/), which is much, much better.
I too love the intersection of data and visualization/usability, turning obscure spreadsheets into useful public interfaces :) The geospatial world is just one part of that. It used to be cool, but I guess nowadays it'd probably be more about turning data into LLM-digestible training sets so that they can more directly analyze questions and answer them in plain language.
Do you think traditional DE is still worth learning, with Skynet on the horizon? What's a good way to get started?
I never did proper "data engineering" per se, but we did have casual run-ins with geodatasets and GIS, and used them to build a home solar calculator that takes a ZIP code and estimates that area's available yearly sunlight, utility rates, electricity consumption, etc. and combine it into an estimated size for a home solar system. It was like a primitive version of Google Sunroof (https://sunroof.withgoogle.com/), which is much, much better.
I too love the intersection of data and visualization/usability, turning obscure spreadsheets into useful public interfaces :) The geospatial world is just one part of that. It used to be cool, but I guess nowadays it'd probably be more about turning data into LLM-digestible training sets so that they can more directly analyze questions and answer them in plain language.
Do you think traditional DE is still worth learning, with Skynet on the horizon? What's a good way to get started?