# Great Expectations

> Test and document the quality of your data

Great Expectations defines data quality through explicit checks called expectations. Engineers use GX Core from environments such as Python and Jupyter to state what a dataset should contain and run validations against it. Those checks give collaborators a shared description of acceptable data.

Data Docs presents results in readable reports and visualizations, letting stakeholders inspect what passed or failed. Orchestration integrations run the checks inside a data pipeline, and configured actions can notify a team or keep failed data from progressing downstream. GX Core is open source under Apache 2.0, so teams can inspect and adapt the framework alongside their own validation logic.

## Listing details

- Canonical BetaNeon page: https://betaneon.com/products/great-expectations
- Official website: https://greatexpectations.io/
- Category: Data & analytics / Data quality & governance
- Status: Launched
- Topics: Collaboration, Data pipelines, Data quality, Open source, Python
- Listing updated: 2026-10-30T16:51:25.094Z
- Information source: https://greatexpectations.io/

This is a product introduction, not an endorsement. Confirm current availability, features, and prices on the official website. Community upvotes on BetaNeon are not star ratings.