great_expectations

Why we included this project

Great Expectations turns assumptions about the data feeding a warehouse or a model into explicit, runnable checks. Instead of waiting for bad rows to surface in downstream reports or training jobs, you catch them at the source. You declare that a column has no nulls, that an ID is unique, or that a metric stays within bounds, and the framework validates those expectations against the databases and file stores your team already uses. Each validation produces results that can gate a pipeline. The appeal is that data quality checks get the same versioned, reviewable rigor as application unit tests, and data engineers, analytics engineers, and ML practitioners share a common vocabulary for describing what good data looks like.