Great Expectations
Judgement: Situational
Python-first DQ; powerful but heavy.
Python framework for declaring and validating expectations on data — nulls, ranges, distributions, distinctness.
Open source and useful, but the spec is governed by one company, so it sits in the same vendor-driven bucket as dbt and SodaCL.
Why it counts as a standard
The expectations DSL and the JSON validation-result schema GX defines have become the reference vocabulary for declarative data quality in Python. Other tools embed or interoperate with Great Expectations rather than reinvent the same primitives. The standard part is the expectation language and its result format — not the company behind it.
At a glance
- Category
- Data Quality
- Governance
- GX Labs (vendor-driven open source)
- Status
- Widely adopted; vendor-driven
- First released
- 2018
Links
Related standards
Other standards in Data Quality.
See Great Expectations in context
Open the interactive Data Landscape to compare Great Expectations against every other open standard, or grab the raw JSON. Spotted something wrong? Open an issue.