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When you later export your flows from Dev and move them to Test and Prod, the imported flows automatically connect to the correct bucket for the target environment, since the bucket name is referenced by an environment parameter.

Example - parameterized database names

In the previous example, bucket names differed between the development and production workspaces. Similarly, there may be different values for the database to use between development and production. 


Tip: If you are creating datasets using custom SQL, this method can be used for migrating flows between workspaces. If the flows reference imported datasets that use custom SQL, you only need to update the value of the environment parameter in the target workspace and reset the connection identifier in order to reconnect the imported flow to its data sources. 

Environment NameDatabase Name

In your Dev workspace, you can create the environment parameter as follows:

Code Block

Export and Import

You can export environment parameters from one environment and import them to another. For example, you may be building your flows in a Dev workspace before they are exported and imported into a Prod workspace. If your flows make use of environment parameters from the Dev space, you may want to export the parameters and their values from the Dev workspace for migration to the Prod workspace. 


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Tip: If your imported dataset is stored in a bucket, you can parameterize the bucket name, which can be useful if you are migrating flows between environments or must change the bucket at some point in the future.

For more information, see Create Dataset with Parameters.


  • ADLS Gen2


    Tip: You can parameterize the user info, host name, and path value fields as separate parameters.

Bucket names can be parameterized as variable parameters or as environment parameters. For more information on examples of parameterized bucket names, see "Environment Parameters" above.

For more information: