acryl-great-expectations
Always know what to expect from your data.
Decision gist · record as of 2026-08-14
Yes. Actively maintained with no known vulnerabilities, uses a permissive Apache-2.0 license, and installs cleanly. It solves a real problem—collaborative data quality testing—with a mature approach. The 29 dependencies are standard data-science libraries. Install it if your workflow involves validating data quality at any stage of a pipeline.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 3.14 and later available via GX_PYTHON_EXPERIMENTAL environment variable.
- Low install friction with a pure-wheel distribution.
- Actively maintained with recent commits and 11712 repository stars.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
last release 2025-05-02 (469 days) · last repo commit 2026-08-14 · 11,712 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 125,372 downloads/mo, #11,823 on PyPI
Alternatives
Verify before relying
pip install acryl-great-expectations
import great_expectations as gx
context = gx.get_context()- Specific data sources and integrations supported beyond the general compatibility reference mentioned.
- Whether the 469 days since release reflects a stable mature version or a long gap in updates.
- Performance characteristics with large datasets or high-frequency validation workloads.
What it is and what it does
GX Core is a data quality testing framework built around Expectations—a declarative way to express data validation rules as unit tests. It lets data teams define what good data looks like, run those tests against pipelines and datasets, and automatically generate documentation of validation results. The framework emphasizes collaboration by giving teams a shared language for data quality standards and helps preserve institutional knowledge about data shape and content.
The package ships with 29 runtime dependencies spanning the Jupyter ecosystem (notebook, ipywidgets, IPython), data processing (pandas, numpy, scipy), serialization (jsonschema, pydantic, ruamel.yaml), and visualization (altair). It's designed to run in virtual environments and integrates with various data sources through a compatibility matrix. The core workflow is simple: import the module, create a Data Context, define Expectations, and validate data against them.
Use it for
- Define and enforce data quality rules in ETL pipelines before data reaches downstream systems.
- Generate automated documentation of data validation results for compliance and audit trails.
- Establish shared data quality standards across teams using a common Expectations language.
- Monitor data source quality over time by running the same Expectations repeatedly.
- Catch data anomalies early by validating schema, nullability, and value ranges on ingestion.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Actively maintained with no known vulnerabilities, uses a permissive Apache-2.0 license, and installs cleanly. It solves a real problem—collaborative data quality testing—with a mature approach. The 29 dependencies are standard data-science libraries. Install it if your workflow involves validating data quality at any stage of a pipeline.
Install
acryl-great-expectations on PyPI
Before you install
Low install friction with a pure-wheel distribution. Actively maintained with recent commits and 11712 repository stars. The 29 runtime dependencies are substantial but standard data-science libraries (pandas, numpy, scipy, Jupyter ecosystem).
Requires Python 3.10 through 3.13; experimental support for 3.14 and later available via GX_PYTHON_EXPERIMENTAL environment variable.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; you must include a copy of the license and state significant changes.
Quickstart
pip install acryl-great-expectations
import great_expectations as gx
context = gx.get_context()Verify before relying
- Specific data sources and integrations supported beyond the general compatibility reference mentioned.
- Whether the 469 days since release reflects a stable mature version or a long gap in updates.
- Performance characteristics with large datasets or high-frequency validation workloads.
Package facts
| License | Apache-2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 29 packagesaltairClickcoloramacryptographyimportlib-metadataIpythonipywidgetsjinja2jsonpatchjsonschemamakefunmarshmallowmistunenbformatnotebooknumpypackagingpandaspydanticpyparsingpython-dateutilpytzrequestsruamel.yamlscipytqdmtyping-extensionstzlocalurllib3 |
| Maintenance | Actively maintained 469 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 125,372 / month, #11,823 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 :: 3.10Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software DevelopmentTopic :: Software Development :: Testing |
Evidence: acryl_great_expectations-0.15.50.1-py3-none-any.whl
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See also great-expectations · great-expectations-experimental · airflow-provider-great-expectations · spark-expectations · dbt-core · whylogs · prefect-snowflake · dagster-msteams · expects · schemathesis