Great Expectations Data Validation Pipeline

---
name: "Great Expectations Data Validation Pipeline"
slug: "great-expectations-data-validation-pipeline"
description: "Validate data quality using the Great Expectations Python library. Define expectations as unit tests for your data, run validation suites, and generate human-readable data quality reports."
github_stars: 11321
verification: "security_reviewed"
source: "https://github.com/great-expectations/great_expectations"
category: "Code Quality & Review"
framework: "Claude Code"
tool_ecosystem:
  github_repo: "great-expectations/great_expectations"
  github_stars: 11321
---

# Great Expectations Data Validation Pipeline

Validate data quality using the Great Expectations Python library. Define expectations as unit tests for your data, run validation suites, and generate human-readable data quality reports.

## Installation

Use the upstream install or setup path that matches your environment:
- pip install great_expectations

Requirements and caveats from upstream:
- [![Python Versions](https://img.shields.io/pypi/pyversions/great_expectations.svg)](https://pypi.python.org/pypi/great_expectations)
- GX Core supports Python 3.10 through 3.13.
- Experimental support for Python 3.14 and later can be enabled by setting a GX_PYTHON_EXPERIMENTAL environment variable when installing great_expectations.

Basic usage or getting-started notes:
- GX 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/).
- Run the following command to import the great_expectations module and create a Data Context:

- Source: https://github.com/great-expectations/great_expectations
- Extracted from upstream docs: https://raw.githubusercontent.com/great-expectations/great_expectations/HEAD/README.md

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/great-expectations-data-validation-pipeline/)