great-expectations-experimental

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

Experimental extensions and early-stage features for Great Expectations, the data quality validation framework that uses Expectations as unit tests for data.

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

pip install great-expectations-experimental

uv add great-expectations-experimental

poetry add great-expectations-experimental

Installing great-expectations-experimental

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.

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()

Requires Python 3.10 through 3.13; experimental support for Python 3.14+ available via GX_PYTHON_EXPERIMENTAL environment variable.

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

Evidence: great_expectations_experimental-0.1.20240917055-py3-none-any.whl

Keywords: data, science, testing, pipeline, data, quality, dataquality, validation, datavalidation

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Further reading