Data Factory - Testing Framework 🛠️

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

The framework is community supported only, hence there's no official Microsoft SLA for features or bug fixes. Please file an issue for bugs or feature requests. The framework is open source and contributions are welcome!

Features ⭐

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

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.
Test an activity with a specific state and assert the evaluated expressions.
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('Path'))

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

Getting started 🚀

Before you start writing tests, you need to set up the repository and install the framework:

If you are not that experienced with Python and prefer a step-by-step guide, use the more detailed getting started guide.

The framework allows you to write two types of tests:

Activity testing - for testing activities in isolation
Pipeline testing - for testing the entire pipeline

Concepts 📚

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

Basic 🌱

Advanced 🔬

Examples 📝

More advanced examples demonstrating the capabilities of the framework:

Fabric:

Batch job example

Azure Data Factory:

Azure Synapse Analytics:

Copy blobs 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.

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.

Release files for data-factory-testing-framework 1.4.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for data-factory-testing-framework 1.4.1
File	Size	Uploaded
data_factory_testing_framework-1.4.1.tar.gz	31.4 kB	Sep 24, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for data-factory-testing-framework 1.4.1
File	Interpreter	ABI	Platform	Reset
data_factory_testing_framework-1.4.1-py3-none-any.whl 15.1 MB Sep 24, 2026	Python 3	none	any	Details

Total release size: 15.1 MB

Release files / data_factory_testing_framework-1.4.1.tar.gz

Download URL	data_factory_testing_framework-1.4.1.tar.gz
Size	31.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`0541d33c8839739aa93a3d5de99f73867beefedb8c68da6db401b293a83856d1`
BLAKE2b-256 checksum How to use checksums	`9e1ea9c5d0afcd4b7475af6f5f1b564c604d2c0dc056044cf44700f5cbd36cb0`
Upload date	Sep 24, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Release files / data_factory_testing_framework-1.4.1-py3-none-any.whl

Download URL	data_factory_testing_framework-1.4.1-py3-none-any.whl
Size	15.1 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`e663061929db032f9dfcc7fa643de46df157aaf5e1ad4f9a2bb8d9660770219f`
BLAKE2b-256 checksum How to use checksums	`1f7451ac5aece3eef3a76f60fbcee0f14bd006377d5ea2a51d53014b269464fb`
Upload date	Sep 24, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`