Skip to main content

A stand-alone test framework that allows to write unit tests for Data Factory pipelines on Microsoft Fabric and Azure Data Factory.

Project description

Data Factory - Testing Framework

A stand-alone test framework that allows to write unit tests for Data Factory pipelines on Microsoft Fabric and Azure Data Factory.

Features

The framework evaluates pipeline and activity definitions which can be asserted. It does so by providing the following features:

  1. Evaluate expressions by using the framework's internal expression parser. It supports all the functions and arguments that are available in the Data Factory expression language.
  2. Test an activity with a specific state and assert the evaluated expressions.
  3. Test a pipeline run by verifying the execution flow of activities for specific input parameters and assert the evaluated expressions of each activity.

The framework does not support running the actual pipeline. It only gives you the ability to test the pipeline and activity definitions.

High-level example

Given a WebActivity with a typeProperties.url property containing the following expression:

@concat(pipeline().globalParameters.baseUrl, variables('JobName'))

A simple test to validate that the concatenation is working as expected could look like this:

    # Arrange
    activity = pipeline.get_activity_by_name("webactivity_name")
    state = PipelineRunState(
        parameters=[
            RunParameter(RunParameterType.Global, "BaseUrl", "https://example.com"),
        ],
        variables=[
            PipelineRunVariable("Path", "some-path"),
        ])

    # Act
    activity.evaluate(state)

    # Assert
    assert "https://example.com/some-path" == activity.type_properties["url"].result

Why

Data Factory does not support unit testing, nor testing of pipelines locally. Having integration and e2e tests running on an actual Data Factory instance is great, but having unit tests on top of them provides additional means of quick iteration, validation and regression testing. Unit testing with the Data Factory Testing Framework has the following benefits:

  • Runs locally with immediate feedback
  • Easier to cover a lot of different scenarios and edge cases
  • Regression testing

Concepts

The following pages go deeper into different topics and concepts of the framework to help in getting you started.

Basic

  1. Repository setup
  2. Installing and initializing the framework
  3. State
  4. Activity testing
  5. Pipeline testing

If you are a not that experienced with Python, you can follow the Getting started guide to get started with the framework.

Advanced

  1. Debugging your activities and pipelines
  2. Development workflow
  3. Overriding expression functions
  4. Framework internals

Examples

More advanced examples demonstrating the capabilities of the framework:

Fabric:

  1. Batch job example

Azure Data Factory:

  1. Copy blobs example
  2. Batch job example

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Disclaimer

This unit test framework is not officially supported. It is currently in an experimental state and has not been tested with every single data factory resource. It should support all activities out-of-the-box but has not been thoroughly tested, please report any issues in the issues section and include an example of the pipeline that is not working as expected.

If there's a lot of interest in this framework, then we will continue to improve it and move it to a production-ready state.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

File details

Details for the file data_factory_testing_framework-0.1.0a171.tar.gz.

File metadata

File hashes

Hashes for data_factory_testing_framework-0.1.0a171.tar.gz
Algorithm Hash digest
SHA256 f71050980eaad9b5cd1b5dea98a7275146ece7e426fd8d6f0165cf82142ec0c8
MD5 502993ae41093986eb91e956940169cb
BLAKE2b-256 a6cb7db2dff4ccbef768a1377b3fe8b39d8029549d5b877674937f84cdc725d7

See more details on using hashes here.

File details

Details for the file data_factory_testing_framework-0.1.0a171-py3-none-any.whl.

File metadata

File hashes

Hashes for data_factory_testing_framework-0.1.0a171-py3-none-any.whl
Algorithm Hash digest
SHA256 4214b55ac34c148e026d176e638e5c0248e65e56f531b4e92d755ddc1042b474
MD5 d2b6927cd073106ca0c7d9ddd570f7e7
BLAKE2b-256 ed6779ce6e381eaffc66ec17a102d34f868b93457e627ef5b04ab6fdd8e02272

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page