airflow-provider-great-expectations
An Apache Airflow provider for Great Expectations
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Apache Airflow and Great Expectations to be installed and configured; Airflow DAG context needed to run operators.
- Low friction install with a pure-Python wheel.
- Actively maintained as of 2026-01-28, with recent commits and no archived status.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions, typical for Airflow ecosystem packages.
last release 2026-01-28 (198 days) · last repo commit 2026-04-01 · 174 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 179,621 downloads/mo, #10,167 on PyPI
Alternatives
Verify before relying
pip install airflow-provider-great-expectations
from airflow_provider_great_expectations.operators import GreatExpectationsOperator
task = GreatExpectationsOperator(
task_id='validate_data',
checkpoint_name='my_checkpoint'
)- 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.
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 on this page. Verify before relying on it.
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
airflow-provider-great-expectations on PyPI
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.
Requires Apache Airflow and Great Expectations to be installed and configured; Airflow DAG context needed to run operators.
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'
)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 the current Python release <3.14,>3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesapache-airflowgreat-expectationsstructlog |
| Maintenance | Actively maintained 198 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 179,621 / month, #10,167 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaEnvironment :: Web EnvironmentFramework :: Apache AirflowFramework :: Apache Airflow :: ProviderIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: airflow_provider_great_expectations-1.0.0-py3-none-any.whl
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See also apache-airflow-providers-openai · apache-airflow-providers-anomalo · apache-airflow-providers-informatica · great-expectations · acryl-great-expectations · great-expectations-experimental · apache-airflow-providers-standard · apache-airflow-providers-zendesk · apache-airflow-providers-tableau · apache-airflow-providers-elasticsearch