Every data quality framework eventually forces you into a choice. Great Expectations gives you a rich ecosystem, but you're managing suites, checkpoints, and a data context — it's a platform, not a library. Soda pushes you toward SodaCL and SodaCloud, with the open-source layer getting thinner every release. Pandera is excellent for schema and type validation, but stops there — no aggregation rules, no SQL engines, no row-level tagging.
Sumeh takes a different position: it's a library, not a platform. It does one thing — run validation rules against data — and does it across every major engine with the same API and the same return type.
from sumeh import pandas
from sumeh.core.rules.rule_model import RuleDefinition
rules = [
