faker

> As a person who worked on systems with thousands of tests. I cannot stress enough that there should never be any randomness added to the automatic tests. To take a counter position, I'm a person who has also worked on many systems with many thousands of tests. Every single test suite I've seen pulling static fixtures as test data was brittle and near worthless. Introducing randomness to the data you test against is the only way to actually test your product with near-realistic inputs. To this end, packages like Faker[0] are worth their weight in gold. [0] https://github.com/joke2k/faker

Creating realistic fake data is useful in lower environments and for load testing. Outside of SQL I like faker: https://github.com/joke2k/faker

I've used faker [0] for stuff like this. I think originally a perl package, has similar packages in other languages as well. I've used the python implementation and enjoy it, along with it's localization feature. It looks like 1.0 was just released as well :D [0] https://github.com/joke2k/faker/releases

I've found the faker library [1] useful. The fake data that I've bothered to model are weighted age ranges. Fortunately, as of Python3.6, you can access it from random.choices [2] in the stdlib [1] https://github.com/joke2k/faker [2] https://docs.python.org/3/library/random.html#random.choices example: https://gist.github.com/Dowwie/8409d871ddae913e44c61bc4d47ce...

Hi! Looks interesting. I usually use faker for this (https://github.com/joke2k/faker). How does fake2db compare to it?

Actually, it looks like this doc is out of date. The README on Github shows that method naming has been corrected, and CC numbers have been added: https://github.com/joke2k/faker