---
id: airflow-provider-great-expectations
version: "1.0.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# airflow-provider-great-expectations — An Apache Airflow provider for Great Expectations

License: permissive · Maintenance: active · Downloads: 179.6K/mo

## What it is and what it does

This package bridges Apache Airflow and Great Expectations by providing Airflow operators that wrap Great Expectations validation logic. It lets you embed data quality checks directly into Airflow DAGs, so data validation becomes a native task type rather than a custom Python wrapper. The operators accept Great Expectations checkpoints and run them as part of your orchestrated workflows.

The package is in Alpha status but actively maintained, with support for Python 3.10 through 3.13. It depends on both Apache Airflow and Great Expectations, so you need both frameworks already set up. The integration is designed to be straightforward for teams already using Airflow who want to add Great Expectations validation without building custom operators.

Use it for:

- Add data quality gates to Airflow pipelines before downstream processing or loading.
- Run Great Expectations checkpoints on a schedule as part of your DAG orchestration.
- Validate data at multiple stages in a multi-step ETL workflow within a single DAG.
- Integrate data quality monitoring into existing Airflow infrastructure without custom code.
- Fail or branch DAG execution based on Great Expectations validation results.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides Apache Airflow operators that integrate Great Expectations data validation into Airflow workflows, allowing you to define and run data quality tests as part of your DAGs.

Yes, if you use both Apache Airflow and Great Expectations. The low install friction, active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. The Alpha status means the API may change, but the package is actively developed and suitable for production use in teams already committed to both frameworks.

## Install

pip install airflow-provider-great-expectations
uv add airflow-provider-great-expectations
poetry add airflow-provider-great-expectations

## Installing airflow-provider-great-expectations

Before you install: Low friction install with a pure-Python wheel. Actively maintained as of 2026-01-28, with recent commits and no archived status. Requires Apache Airflow and Great Expectations as runtime dependencies.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for Airflow ecosystem packages.

Quickstart:

pip install airflow-provider-great-expectations

from airflow_provider_great_expectations.operators import GreatExpectationsOperator

task = GreatExpectationsOperator(
    task_id='validate_data',
    checkpoint_name='my_checkpoint'
)

Requires Apache Airflow and Great Expectations to be installed and configured; Airflow DAG context needed to run operators.

Verify before relying:

- Specific operator types and their parameters beyond the basic checkpoint-based pattern.
- Whether the package supports all Great Expectations checkpoint features or has limitations.
- Integration patterns with different Airflow versions and Great Expectations versions.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 179.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow data validation operators, great expectations airflow integration, data quality testing in airflow, airflow data quality checks, great expectations provider, airflow data validation tasks, data testing workflows, airflow-provider, data-validation, orchestration

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