The teams I've been working on have resorted to data tests instead of code tests. That means that the data produced by your code is tested against a certain set of expectations - in stark contrast to code being tested _before_ its execution.
We've written our own tool to compare different data sources against each other. This allows, for example, to test for invariants (or expected variations) between and after a transformation.
We've written our own tool to compare different data sources against each other. This allows, for example, to test for invariants (or expected variations) between and after a transformation.
The tool is open source: https://github.com/QuantCo/datajudge
We've also written a blog post trying to illustrate a use case: https://tech.quantco.com/2022/06/20/datajudge.html