great-expectations-experimental
Always know what to expect from your data.
Decision gist · record as of 2026-08-14
Yes, with conditions: install only if you are already using great-expectations and want to trial experimental features in a non-production environment. The low install friction, active maintenance, and permissive license make it safe to try, but the Beta status and experimental nature mean features may change. Not recommended for production deployments or teams new to Great Expectations.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 through 3.13; experimental support for Python 3.14+ available via GX_PYTHON_EXPERIMENTAL environment variable.
- Low install friction with a single runtime dependency on great-expectations.
- Actively maintained with recent commits and a large community (11711 GitHub stars), though marked as Beta status.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for open-source data tools.
last release 2024-09-17 (696 days) · last repo commit 2026-08-14 · 11,711 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 583,318 downloads/mo, #5,895 on PyPI
Alternatives
Verify before relying
pip install great-expectations-experimental
import great_expectations as gx
context = gx.get_context()- What specific experimental features or APIs this package adds beyond the base great-expectations library
- Stability guarantees or deprecation policy for experimental features
- Whether this package is intended for production use or development/testing only
What it is and what it does
great-expectations-experimental provides early-stage and experimental extensions to Great Expectations, the data quality framework built around Expectations—expressive, extensible unit tests for data. The base framework helps teams express data quality requirements in a common language, automatically generate validation documentation, and preserve institutional knowledge about data. This experimental package wraps experimental features on top of that foundation.
As a thin wrapper around great-expectations with low install friction, it is designed for teams already using Great Expectations who want to trial new capabilities before they stabilize. The package is actively maintained and draws from a large community, but its Beta status and experimental nature mean features may change or be removed.
Use it for
- Trial new data validation features in Great Expectations before they reach stable release
- Test experimental data source integrations or validation patterns in a development environment
- Extend data quality testing workflows with early-access features while keeping production on stable versions
- Collaborate with the Great Expectations community on feedback for experimental capabilities
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions: install only if you are already using great-expectations and want to trial experimental features in a non-production environment.
The low install friction, active maintenance, and permissive license make it safe to try, but the Beta status and experimental nature mean features may change. Not recommended for production deployments or teams new to Great Expectations.
Install
great-expectations-experimental on PyPI
Before you install
Low install friction with a single runtime dependency on great-expectations. Actively maintained with recent commits and a large community (11711 GitHub stars), though marked as Beta status.
Requires Python 3.10 through 3.13; experimental support for Python 3.14+ available via GX_PYTHON_EXPERIMENTAL environment variable.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for open-source data tools.
Quickstart
pip install great-expectations-experimental
import great_expectations as gx
context = gx.get_context()Verify before relying
- What specific experimental features or APIs this package adds beyond the base great-expectations library
- Stability guarantees or deprecation policy for experimental features
- Whether this package is intended for production use or development/testing only
Package facts
| License | Apache-2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagegreat-expectations |
| Maintenance | Actively maintained 696 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 583,318 / month, #5,895 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Other AudienceIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software DevelopmentTopic :: Software Development :: Testing |
Evidence: great_expectations_experimental-0.1.20240917055-py3-none-any.whl
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See also great-expectations · acryl-great-expectations · airflow-provider-great-expectations · spark-expectations · expects · dbt-core · prefect-snowflake · whylogs · dbt-core-experimental-parser · dagster-pandera