Parameters for Data Factory in Microsoft Fabric

This article shows you how to use parameters in your Data Factory pipelines in Fabric so you can build flexible, reusable workflows.

Use parameters, expressions, and functions in Data Factory pipelines

This section walks you through the basics of creating parameterized pipelines in Data Factory for Fabric, using clear examples along the way. Parameters and dynamic expressions save time and let you build flexible Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) solutions. They reduce hard coding and let you reuse objects and processes, which makes pipelines easier to maintain and lets you ship new features faster.

Parameter and expression concepts

You can use parameters to pass external values into your pipelines. After you set a parameter, its value stays the same throughout the run. Parameters let you reuse the same pipeline with different values each time. You can use parameters on their own or inside expressions, with values that are either fixed or calculated when the pipeline runs.

Expressions can go anywhere in a string value and always return another string value. For example, if you use @password, the pipeline treats password as a parameter. If the value is an expression, remove the @ to get the actual content. To use a string that starts with @, escape it by typing @@. The following examples show how this works in practice.

Expressions can also appear inside strings through string interpolation, where you wrap expressions in @{ ... }. For example, the following string includes parameter values and literal string values:

"First Name: @{pipeline().parameters.firstName} Last Name: @{pipeline().parameters.lastName}"

String interpolation always returns a string. For example, if you define myNumber as 42 and myString as foo:

Parameter expression examples

Create and use parameters

To create parameters, select the background of the pipeline editor canvas, and then select the Parameters tab of the properties window at the bottom. Select + New to add a parameter to the pipeline, and give it a name, a data type, and a default value:

You can then use the parameter anywhere your pipeline supports dynamic content. In this example, the parameter dynamically provides the name of a lakehouse data store on the Source tab of a Copy activity's property pages.

The Add dynamic content window opens. Use it to specify any kind of dynamic content, including parameters, system variables, functions, or pipeline variables. In this example, you select the parameter you defined earlier, and the dynamic content window automatically populates with the correct expression to reference it.

Parameterize connections

To parameterize connections in pipelines, use the globally unique identifier (GUID) of the connection you want to replace dynamically.

1. Before you dynamically modify the connection in your pipeline, get the GUID of the connection you want to set.
2. Go to Settings > Manage connections and gateways.
3. Find the connection name and select the ellipsis (...) next to it.
4. Select Settings and copy the Connection ID.
5. Paste the GUID into a string parameter, and reference that parameter in your dynamic expression.

Complex expression example

The following expression references a deep subfield of activity output. To reference a pipeline parameter that evaluates to a subfield, use [] syntax instead of the dot (.) operator, as with subfield1 and subfield2.

@activity('*activityName*').output.*subfield1*.*subfield2*[pipeline().parameters.*subfield3*].*subfield4*

Dynamic content editor

The dynamic content editor automatically escapes characters in your content when you finish editing. For example, the following content in the content editor is a string interpolation with an expression function:

@{toUpper('myData')}

The dynamic content editor converts the previous content to the following expression:

MYDATA

Use functions and variables in expressions

You can call functions and use variables within expressions. The following sections provide information about the functions that can be used in an expression.

Pipeline scope variables

You can reference these system variables anywhere in the pipeline JSON.

String functions

To work with strings, use these string functions and also some collection functions. String functions work only on strings.

Collection functions

To work with collections such as arrays, strings, and dictionaries, use these collection functions.

Logical functions

Use these functions inside conditions to evaluate any type of logic.

Conversion functions

Use these functions to convert between each of the native types in the language:

• string
• integer
• float
• boolean
• arrays
• dictionaries

Math functions

Use these functions with both types of numbers: integers and floats.

Date functions

Related content

• Expression language