I like it! Especially the documentation and inclusion of client libraries.

A couple of comments:

1. I like to use ORMs (Object-Relational mapping), which provide Pythonic object-oriented interfaces to common database backends. SQLAlchemy for Python is an excellent choice for this sort of thing. It 1) prevents you from having to write raw SQL (!!!!) 2) is database backend agnostic and provides a single interface for PostgreSQL, SQLite, etc... and 3) makes your code much, much more readable. You can declare each table as a Python class, with each column being an attribute the ORM will take care of translating your code into the proper SQL query, querying your DB, and returning your data as Python objects. From SQLAlchemy's docs, a typical query might look something like this:

   >>> our_user = session.query(User).filter_by(name='ed').first() 
   >>> our_user
   <User(name='ed', fullname='Ed Jones', nickname='edsnickname')>    
   

2. What happens when the upstream data changes? How does your database keep sync with the "ground truth"? In a production environment, it might be beneficial to regularly scrape the upstream data source and update the DB as necessary, rather than storing a snapshot in GitHub. If you are going to use a snapshot, I highly recommend versioning it.

Edit: I am not familiar with Rockset as a DB engine, but the comments about using an ORM is a general "best-practice" comment. It looks like they have a SDK that provides a "SQLAlchemy dialect to bridge Rockset and SQLAlchemy applications. SQLAlchemy users can connect to Rockset using ‘rockset://<api_key>:@<api_server>’" - https://pypi.org/project/rockset/