- Environment Parameters: A workspace administrator or project owner can specify parameters that are available across the environment, including default values for them.
- Dataset Parameters: You can parameterize the paths to inputs for your imported datasets, creating datasets with parameters. For file-based imported datasets, you can parameterize the bucket where the source is stored.
- Flow Parameters: You can create parameters at the flow level, which can be referenced in any recipe in the flow.
- Output Parameters: When you run a job, you can create parameters for the output paths for file- or table-based outputs.
These parameters can be defined by timestamp, patterns, wildcards, or variable values that you specify at runtime.
Project owners or workspace administrators can define parameters that apply across the project or workspace environment. These parameters can be referenced by any user in the environment, but only a user with admin access can define, modify, or delete these parameters.
Tip: When specifying an imported dataset with parameters, you should attempt to be as specific as possible in your parameter definitions.
NOTE: When importing one or more Excel files as a parameterized dataset, you select worksheets to include from the first file. If there are worksheets in other Excel files that match the names of the worksheets that you selected, those worksheets are also imported. All worksheets are unioned together into a single imported dataset with parameters. Pattern-based parameters are not supported for import of Excel worksheets.
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- You cannot create datasets with parameters from uploaded data.
- You cannot create dataset with parameters from multiple file types.
- File extensions can be parameterized. Mixing of file types (e.g. TXT and CSV) only works if they are processed in an identical manner, which is rare.
- You cannot create parameters across text and binary file types.
- For datasources that require conversion, such as Excel, PDF, or JSON files, you can create a dataset with parameters from a maximum of 500 converted files.
- Parameter and variable names can be up to 255 characters in length.
- For regular expressions, the following reference types are not supported due to the length of time to evaluate:
Backreferences. The following example matches on
cxcyet generates an error:
Lookahead assertions: The following example matches on
a, but only when it is part of an
abpattern. It generates an error:
- For some source file types, such as Parquet, the schemas between source files must match exactly.
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.
- 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.
Literal values: These values are always of String data type.
Tip: You can wrap flow parameter references in your transformations with one of the
PARSEfunctions. For more information, see Create Flow Parameter.
NOTE: Wildcards are not supported.
. For more information, see Text Matching.
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- Regular expressions.
When you are creating or editing a publishing action in the Run Jobs Job page, you can click the Parameterize destination link that appears in the right panel.
- Data sources tab: For file-based parameterized datasets, you can review the files that were matched at runtime for the specified parameters.
- Parameters tab: View the parameter names and values that were used as part of the job, including the list of matching datasets.
See Job Details Page.
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