Assuming you ignore or amortize the time necessary to create the table in the first place, of course.

This is the basis for rainbow tables: precomputed tables for mapping hashes to passwords, with a space-saving “hash chaining” trick to effect a constant factor reduction in table size. Such tables are the reason why passwords must be hashed with a unique salt when stored in a database.

I’m not sure this definition of Big-O for space complexity is universal. When I’ve seen/used it, the size of the initial data wasn’t relevant, it was more about the additional memory required for the algorithm.

For example, an in-place algorithm like Bubble Sort would have a O(1) space complexity, because it requires no extra memory (and 0 memory is a constant). Merge sort on the other hand is O(n) because it always uses additional memory for its intermediate stages, and that additional memory scales with n.

Doing a quick google, the first few sites I find seem to use a similar understanding https://www.geeksforgeeks.org/time-and-space-complexity-anal...

Why couldn’t it?