Fall NEARC 2024

Python and the pandas library are great for processing data, but if you're working with a large database, SQL is often faster and more straightforward. While working on a large spatial data processing project, we've replaced much of our Python code with SQL while still using Python to run the data pipeline. We store our SQL code in a Git repository and use Python to test it, modify it using Jinja templates, and send it to the database. We've worked out a system in which each language does what it does best.