nutter
A databricks notebook testing library
Decision gist · record as of 2026-08-14
Yes, if you are actively using Databricks and need a lightweight, notebook-native testing framework. However, exercise caution: the project is dormant (last release 2022-12-16), so compatibility with current Databricks APIs is unverified. Suitable for teams already committed to Databricks and willing to maintain or fork the code if needed; not recommended for new projects seeking active upstream support.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.7.0 and Databricks cluster with dbutils available; designed to run within Databricks notebooks, not as a standalone library.
- Low install friction with a pure Python wheel.
- Maintenance is dormant—last release was 2022-12-16 with no recent commits.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use, modify, and distribute the package freely in commercial and private projects without significant legal constraints.
last release 2022-12-16 (1337 days) · last repo commit 2024-04-20 · 316 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 419,694 downloads/mo, #6,794 on PyPI
Alternatives
Verify before relying
# Install on Databricks cluster via PyPI
# pip install nutter
# In a test notebook:
from nutter import NutterFixture
class MyTestFixture(NutterFixture):
def run_my_test(self):
dbutils.notebook.run('notebook_under_test', 600, {})
def assertion_my_test(self):
assert True
result = MyTestFixture().execute_tests()
print(result.to_string())- Whether the package remains compatible with current Databricks API versions and notebook environments.
- Whether the five runtime dependencies are actively maintained and free of known vulnerabilities.
- How well the CLI integrates with modern CI/CD platforms beyond Azure DevOps.
- Current compatibility with Python versions beyond 3.7.0.
What it is and what it does
Nutter is a testing framework designed to make it straightforward to test Databricks notebooks by defining test fixtures as Python classes that inherit from NutterFixture. You write test methods using a naming convention (run_*, assertion_*, before_*, after_*) to structure your test logic, then execute them either interactively within the notebook or via the Nutter CLI from your development machine or build pipeline.
The framework has two main components: the Nutter Runner, which is installed as a library on your Databricks cluster and provides the NutterFixture base class, and the Nutter CLI, which runs on your local machine or build agent to orchestrate test execution and collect results. It supports parallel test execution, lifecycle hooks (before_all, after_all), and produces test reports that can be integrated into build pipelines.
Use it for
- Write and run unit tests for data transformation notebooks within Databricks without leaving the notebook environment.
- Integrate notebook tests into build pipelines to validate data processing logic as part of CI/CD workflows.
- Execute multiple test notebooks in parallel from the CLI to speed up validation of a suite of data engineering workflows.
- Set up test fixtures with before_all and after_all hooks to manage test data setup and cleanup across multiple assertions.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively using Databricks and need a lightweight, notebook-native testing framework.
However, exercise caution: the project is dormant (last release 2022-12-16), so compatibility with current Databricks APIs is unverified. Suitable for teams already committed to Databricks and willing to maintain or fork the code if needed; not recommended for new projects seeking active upstream support.
Install
nutter on PyPI
Before you install
Low install friction with a pure Python wheel. Maintenance is dormant—last release was 2022-12-16 with no recent commits. The project shows no active development signal.
Requires Python >=3.7.0 and Databricks cluster with dbutils available; designed to run within Databricks notebooks, not as a standalone library.
License in practice
Licensed under MIT (permissive), so you can use, modify, and distribute the package freely in commercial and private projects without significant legal constraints.
Quickstart
# Install on Databricks cluster via PyPI
# pip install nutter
# In a test notebook:
from nutter import NutterFixture
class MyTestFixture(NutterFixture):
def run_my_test(self):
dbutils.notebook.run('notebook_under_test', 600, {})
def assertion_my_test(self):
assert True
result = MyTestFixture().execute_tests()
print(result.to_string())Verify before relying
- Whether the package remains compatible with current Databricks API versions and notebook environments.
- Whether the five runtime dependencies are actively maintained and free of known vulnerabilities.
- How well the CLI integrates with modern CI/CD platforms beyond Azure DevOps.
- Current compatibility with Python versions beyond 3.7.0.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesdatabricks-apirequestsfirejunit-xmlpy4j |
| Maintenance | Dormant 1,337 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 419,694 / month, #6,794 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: nutter-0.1.35-py3-none-any.whl
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See also databricks-test · databricks-connect · databricks-mcp · brickflows · testbook · fixtures · pytest-datadir · pytest-spark · pytest-mysql