databricks-test
Unit testing and mocking for Databricks
Decision gist · record as of 2026-08-14
No. The project is archived and abandoned since 2021-01-26 with no maintenance since then. While it has low install friction and permissive licensing, the lack of updates means it is unlikely to work reliably with current Databricks APIs, PySpark versions, or modern Python environments. Only consider it if you are locked into an older Databricks setup that has not changed since 2020.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires pytest setup in your code project outside Databricks; only supports .py notebook format, not .ipynb or .dbc.
- Installation is straightforward with no runtime dependencies, but the project is archived and abandoned since 2021-01-26 with no updates since then.
- Use only if your Databricks setup and notebook patterns remain compatible with that era.
License · maintenance · safety
permissive license (permissive) — Licensed under MIT (permissive), so you can use and modify freely, but the lack of active maintenance means no updates to licensing terms or legal review.
last release 2020-03-04 (2354 days) · last repo commit 2021-01-26 · 49 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,678 downloads/mo, #13,255 on PyPI
Alternatives
Verify before relying
pip install databricks_test
# In your notebook:
if 'dbutils' not in locals():
import databricks_test
databricks_test.inject_variables()
# In your test file:
import databricks_test
def test_notebook():
with databricks_test.session() as dbrickstest:
dbrickstest.run_notebook(".", "notebook_name")
# assertions here- Whether the package works with current Databricks notebook APIs and PySpark versions given the last release was 2020-03-04.
- Compatibility with modern Python versions beyond what the classifier 'Programming Language :: Python :: 3' indicates.
- Whether mocking of newer Databricks extensions and connectors is supported.
What it is and what it does
databricks_test is a unit testing framework designed to let you test Databricks notebooks locally using pytest, without needing to run them on a Databricks cluster. It works by injecting mocked versions of Databricks-specific objects—spark, dbutils, display—into your notebook code when run locally, while those objects remain untouched when the notebook runs in Databricks itself. The framework supports PySpark operations including UDFs and Pandas UDFs, and lets you mock connectors to external systems like Azure Storage, S3, and SQL Data Warehouse.
The typical workflow is to add an instrumentation cell at the top of your notebook that conditionally loads databricks_test only when running locally (detected by checking if dbutils exists), then write test cases that instantiate a databricks_test session, configure mocks for external dependencies, run the notebook, and assert on the results. This allows you to validate ETL logic, data transformations, and notebook behavior in a fast, local test environment before deploying to Databricks.
Use it for
- Test ETL notebooks that read CSV and write Parquet locally before running on a cluster.
- Mock widget inputs and dbutils calls to test parameterized notebook logic without Databricks.
- Validate PySpark transformations and UDFs in isolation using local temporary files.
- Mock external connectors (Azure Storage, S3, SQL DW) to test data pipeline integration points.
- Run pytest on notebook code as part of a CI/CD pipeline in a code repository.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No.
The project is archived and abandoned since 2021-01-26 with no maintenance since then. While it has low install friction and permissive licensing, the lack of updates means it is unlikely to work reliably with current Databricks APIs, PySpark versions, or modern Python environments. Only consider it if you are locked into an older Databricks setup that has not changed since 2020.
Install
databricks-test on PyPI
Before you install
Installation is straightforward with no runtime dependencies, but the project is archived and abandoned since 2021-01-26 with no updates since then. Use only if your Databricks setup and notebook patterns remain compatible with that era.
Requires pytest setup in your code project outside Databricks; only supports .py notebook format, not .ipynb or .dbc.
License in practice
Licensed under MIT (permissive), so you can use and modify freely, but the lack of active maintenance means no updates to licensing terms or legal review.
Quickstart
pip install databricks_test
# In your notebook:
if 'dbutils' not in locals():
import databricks_test
databricks_test.inject_variables()
# In your test file:
import databricks_test
def test_notebook():
with databricks_test.session() as dbrickstest:
dbrickstest.run_notebook(".", "notebook_name")
# assertions hereVerify before relying
- Whether the package works with current Databricks notebook APIs and PySpark versions given the last release was 2020-03-04.
- Compatibility with modern Python versions beyond what the classifier 'Programming Language :: Python :: 3' indicates.
- Whether mocking of newer Databricks extensions and connectors is supported.
Package facts
| License | permissive license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 2,354 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 95,678 / month, #13,255 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: databricks_test-0.0.4-py3-none-any.whl
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