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--- title: Pipeline parameters and variables titleSuffix: Azure Data Factory & Azure Synapse description: Learn about pipeline parameters and variables in Azure Data Factory and Azure Synapse Analytics. author: whhender ms.author: whhender ms.subservice: orchestration ms.custom: synapse ms.topic: concept-article ms.date: 07/25/2025 --- # Pipeline parameters and variables in Azure Data Factory and Azure Synapse Analytics [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)] This article helps you understand the difference between pipeline parameters and variables in Azure Data Factory and Azure Synapse Analytics and how to use them to control your pipeline behavior. ## Pipeline parameters Parameters are defined at the pipeline level, and can't be modified during a pipeline run. Pipeline parameters can be used to control the behavior of a pipeline and its activities, such as by passing in the connection details for a dataset or the path of a file to be processed. ### How to define a pipeline parameter To define a pipeline parameter, follow these steps: - Select your pipeline to view its configuration tabs. - Select the "Parameters" tab, and select the "+ New" button to define a new parameter. - Enter a name and description for the parameter, and select its data type from the dropdown menu. Data types can be String, Int, Float, Bool, Array, Object, or SecureString. - Optionally, you can also assign a default value to the parameter. After defining a pipeline parameter, you can access its value during a pipeline run by using the ```@pipeline().parameters.<parameter name>``` expression in a pipeline activity. For example, if you defined a parameter called account_name of type String, you can access its value in an activity by using the expression ```@pipeline().parameters.account_name```.  Before each pipeline run, you can assign a new value to your parameter in the right panel, otherwise the pipeline uses the default or the value previously defined. ## Pipeline variables Pipeline variables are values that can be set and modified during a pipeline run. Unlike pipeline parameters, which are defined at the pipeline level and can't be changed during a pipeline run, pipeline variables can be set and modified within a pipeline using a [Set Variable](control-flow-set-variable-activity.md) activity. Pipeline variables can be used to store and manipulate data during a pipeline run, such as by storing the results of a computation or the current state of a process. > [!NOTE] > Variables are currently scoped at the pipeline level. This means that they aren't thread safe and can cause unexpected and undesired behavior if they're accessed from within a parallel iteration activity such as a foreach loop, especially when the value is also being modified within that foreach activity. To define a pipeline variable, follow these steps: - Select your pipeline to view its configuration tabs. - Select the "Variables" tab, and select the "+ New" button to define a new variable. - Enter a name and description for the variable, and select its data type from the dropdown menu. Data types can be String, Bool, or Array. - Optionally, you can also assign a default value to the variable. This value will be used as the initial value of the variable at the start of a pipeline run. After defining a pipeline variable, you can access its value during a pipeline run by using the ```@variables('<variable name>')``` expression in a pipeline activity. For example, if you defined a variable called flag of type Array, you can access its value in an activity by using the expression ```@variables('flag')```. You can also use a "Set Variable" activity to modify the value of the variable within a pipeline.  ## Related content See the following tutorials for step-by-step instructions for creating pipelines with activities: - [Build a pipeline with a copy activity](quickstart-create-data-factory-powershell.md) - [Build a pipeline with a data transformation activity](tutorial-transform-data-spark-powershell.md) How to achieve CI/CD (continuous integration and delivery) using Azure Data Factory - [Continuous integration and delivery in Azure Data Factory](continuous-integration-delivery.md)