{"data":{"slug":"fivetran-great-expectations","name":"great_expectations","tagline":"Always know what to expect from your data","github_url":"https://github.com/fivetran/great_expectations","owner":"fivetran","repo":"great_expectations","owner_avatar_url":"https://avatars.githubusercontent.com/u/2722259?v=4","primary_language":"Python","stars":11690,"forks":1790,"topics":["cleandata","data-engineering","data-profilers","data-profiling","data-quality","data-science","data-unit-tests","datacleaner","datacleaning","dataquality","dataunittest","eda","exploratory-analysis","exploratory-data-analysis","exploratorydataanalysis","mlops","pipeline","pipeline-debt","pipeline-testing","pipeline-tests"],"archived":false,"github_pushed_at":"2026-08-02T02:39:28+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/fivetran-great-expectations","markdown_url":"https://www.graphcanon.com/tools/fivetran-great-expectations.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/fivetran-great-expectations","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=fivetran-great-expectations","description":"Always know what to expect from your data.","homepage_url":"https://docs.greatexpectations.io/","license":"Apache-2.0","open_issues":39,"watchers":100,"ai_summary":"GX Core is a Python-based toolset for validating and testing data quality using expectations that serve as unit tests for data.","readme_excerpt":"<img align=\"right\" src=\"./docs/docusaurus/static/img/gx-mark-160.png\">\n\n## About GX Core\n\nGX Core combines the collective wisdom of thousands of community members with a proven track record in data quality deployments worldwide, wrapped into a super-simple package for data teams.\n\nIts powerful technical tools start with Expectations: expressive and extensible unit tests for your data. Expectations foster collaboration by giving teams a common language to express data quality tests in an intuitive way. You can automatically generate documentation for each set of validation results, making it easy for everyone to stay on the same page. This not only simplifies your data quality processes, but helps preserve your organization’s institutional knowledge about its data.\n\nLearn more about how data teams are using GX Core in our featured [case studies](https://greatexpectations.io/case-studies/).\n\n## Integration support policy\n\nGX Core supports Python `3.10` through `3.13`.\nExperimental support for Python `3.14` and later can be enabled by setting a `GX_PYTHON_EXPERIMENTAL` environment variable when installing `great_expectations`.\n\nFor data sources and other integrations that GX supports, see the [compatibility reference](https://docs.greatexpectations.io/docs/help/compatibility_reference) for additional information.\n\n## Get started\n\nGX recommends deploying GX Core within a virtual environment. For more information about getting started with GX Core, see [Introduction to GX Core](https://docs.greatexpectations.io/docs/core/introduction/).\n\n1. Run the following command in an empty base directory inside a Python virtual environment to install GX Core:\n\n\t```bash title=\"Terminal input\"\n\tpip install great_expectations\n\t```\n2. Run the following command to import the `great_expectations module` and create a Data Context:\n\n\t```python\n\timport great_expectations as gx\n\n\tcontext = gx.get_context()\n\t```\n\n## Get support from GX and the community\n\nThey are listed in the order in which GX is prioritizing the support issues:\n\n1. Issues and PRs in the [GX GitHub repository](https://github.com/great-expectations)\n2. Questions posted to the [GX Core Discourse forum](https://discourse.greatexpectations.io/c/oss-support/11)\n3. Questions posted to the [GX Slack community channel](https://greatexpectationstalk.slack.com/archives/CUTCNHN82)\n\n## Contribute\nWe truly value the contributions of our community and always welcome pull requests. PRs are encouraged for both bug fixes and new features. For feature requests, we ask that you first open an issue for discussion to ensure the feature fits within the vision for GX Core and to align on the approach so that your time and effort are well spent. Thank you for being a crucial part of GX Core!\n\nSee [CONTRIBUTING.md](./CONTRIBUTING.md) for details on how to propose a change, claim an issue, and submit a pull request.\n\n## Code of conduct\nEveryone interacting in GX Core project codebases, Discourse forums, Slack channels, and email communications is expected to adhere to the [GX Community Code of Conduct](https://discourse.greatexpectations.io/t/gx-community-code-of-conduct/1199).","github_created_at":"2017-09-11T00:18:46+00:00","created_at":"2026-07-11T23:15:05.782507+00:00","updated_at":"2026-08-02T06:00:50.825051+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"}],"tags":[{"slug":"data-engineering","name":"data-engineering"},{"slug":"data-quality","name":"data-quality"},{"slug":"exploratory-data-analysis","name":"exploratory-data-analysis"},{"slug":"mlops","name":"mlops"}],"trust":{"provenance":{"is_fork":false,"github_id":103071520,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-02T06:00:50.111Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":8,"days_since_push":0,"last_release_at":"2026-07-24T13:50:56Z"},"security_summary":{"status":"findings","scanner":"osv@v1","low_count":51,"high_count":0,"last_scan_at":"2026-07-11T23:15:11.249Z","medium_count":0,"scan_profile":"deps","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-02T06:00:50.550Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-02T06:00:50.550Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-02T06:00:50.550Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"requires_docker":false},"when_to_use":["When you need detailed and automated documentation for each set of validation results to simplify your data quality processes while preserving institutional knowledge.","If your team values a common language and intuitive way to express data quality tests, which fosters collaboration within the team."],"when_not_to_use":["For environments that strictly require adherence to Python versions 3.9 or lower, since Great Expectations supports only 3.10 through 3.13 natively.","If your data integration requirements are not compatible with those listed in the Great Expectations compatibility reference."],"source":"enrich:decision_facts","observed_at":"2026-07-17T12:22:00.134Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Supports Python versions 3.10 through 3.13, with experimental support for Python 3.14 and later via an environment variable."},{"label":"Adopt for","value":"Great Expectations is a Python library that helps maintain data quality through unit testing mechanisms known as expectations."},{"label":"License detail","value":"Great Expectations is available under the Apache-2.0 license."}]}}