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:

• 
• 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.

• 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