{"categories":[{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/6"}],"enrichment":{"capability":"Provides a unit testing framework for Databricks notebooks, allowing you to run notebook code locally with mocked Databricks objects like spark, dbutils, and display for pytest-based testing.","skillfed_tags":["databricks","notebook-testing","abandoned"],"use_cases":["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."],"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\u2014spark, dbutils, display\u2014into 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.\n\nThe 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.","worth_installing":"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."},"id":"databricks-test","links":{"html":"https://skillfed.io/packages/databricks-test","md":"https://skillfed.io/packages/databricks-test.md","pypi":"https://pypi.org/project/databricks-test/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2020-03-04","license_spdx":null,"license_treatment":"permissive","name":"databricks-test","python_support":"unspecified","summary":"Unit testing and mocking for Databricks"},"popularity":{"monthly_downloads":95678,"position":13255,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.4"}