fivetran/great_expectations

What is it?

Key features

• 01Declarative Expectations that act as unit tests for datasets
• 02Automatically generated documentation for validation results
• 03Shared, readable vocabulary for expressing data quality rules
• 04Installable with pip and started with a single Data Context call

Use cases

• Validating data in ETL or ELT pipelines before loading into a warehouse
• Adding data quality checks to machine learning training data pipelines
• Sharing readable data quality reports with non-engineering stakeholders

Quick start

1. 1

   Create and activate a Python virtual environment (recommended by GX)

2. 2

   Install GX Core with pip

   pip install great_expectations
3. 3

   Import GX and create a Data Context in Python

   import great_expectations as gx context = gx.get_context()

Things to know

• !Requires Python 3.10 to 3.14; newer Python versions have only experimental support.

Topics

FAQ

What is great_expectations?

Python data quality library that lets teams write declarative Expectations as unit tests for their data and generate validation reports. GX Core, the open-source core of Great Expectations, is a Python library for validating data quality. Its central concept is the Expectation, a declarative assertion about a dataset such as a column's values falling within a range or a table having no null keys. Teams use these assertions as unit tests for data, running them inside pipelines to catch bad data before it reaches downstream consumers.

Is great_expectations free to use? What license does it have?

great_expectations is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.

Is great_expectations still maintained?

Actively maintained. The last commit to great_expectations was on Oct 16, 2026.

What can I use great_expectations for?

Validating data in ETL or ELT pipelines before loading into a warehouse Adding data quality checks to machine learning training data pipelines Sharing readable data quality reports with non-engineering stakeholders

How do I install great_expectations?

Create and activate a Python virtual environment (recommended by GX) Install GX Core with pip: pip install great_expectations Import GX and create a Data Context in Python: import great_expectations as gx context = gx.get_context()

Where can I get great_expectations?

The source code and installation instructions are on GitHub: https://github.com/fivetran/great_expectations