Purely the power law. That would be an interesting thing to figure out though. Maybe a github crawl.

EDIT: I stand corrected based on github code files (which might better represent application CRUD queries versus use by analysts, more thought required!)

SELECT: 7.3M code results [0]

INSERT: 8.9M code results [1]

UPDATE: 5.5M code results [2]

DELETE: 5.0M code results [3]

[0] https://github.com/search?q=select++extension%3Asql&type=Cod...

[1] https://github.com/search?q=insert++extension%3Asql&type=Cod...

[2] https://github.com/search?q=update++extension%3Asql&type=Cod...

[3] https://github.com/search?q=delete++extension%3Asql&type=Cod...

Reads are easy to cache at various layers (query cache, application, web, etc). Inserts and updates must go to the database.

So even a read-heavy application could have more writes than reads due to caching.