In this example, you have three
|D s item|
|Environment Name||S3 Bucket Name|
In your Dev workspace, you can create an environment parameter called the following:
NOTE: Matching file path patterns in a large directory can be slow. Where possible, avoid using multiple patterns to match a file pattern or scanning directories with a large number of files. To increase matching speed, avoid wildcards in top-level directories and be as specific as possible with your wildcards and patterns.
Tip: For best results when parameterizing directories in your file path, include the trailing slash (
- You can choose to search nested folders for files that match your specified pattern.
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.
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:
For each of the following types of parameter, you can apply override values as needed.
|dataset parameters||When you run a job, you can apply override values to variables for your imported datasets. See Run Job Page.|
At the flow level, you can apply override values to flow parameters. These values are passed into the recipe and the rest of the flow for evaluation during recipe development and job execution.
|output parameters||When you define your output objects in Flow View, you can apply override values to the parameterized output paths on an as-needed basis when you specify your job settings. See Run Job Page.|
Order of Parameter Evaluation