Script modeling

• Detail panel (left-aligned) where you can provide the script name and optional description.
• Versioning widget embedded within the Detail panel for managing the script versions.
• Code editor (center pane) for writing the script code.
• Context and Configuration panel (right-aligned panel) containing these components:
  • Input Variables and Output Variables table displaying their names, data types and description.
  • Library dependencies widget to add libraries to the script.
  • Performance Metrics showing details of a test performance.
• Documentation tab for writing rich-text documentation for the script.
• Console showing the results of python print () function

We recommend this workflow pattern for modeling scripts:

• Define the script name.
• Define the Input Variables and the Output Variables names, data types and optionally description.
• Add the library dependencies that are required for the script to work.
• Write the script code.
• Write the script documentation.
• Test your script by running it with sample data in Testing tab.
• When the script produces the expected output, approve the script.
• Script can be used now in activated data flows.

A script represents a Python function with one -or multiple parameters, that responds with one -or multiple value when called.

When the script is called from data flow, the incoming data is passed to the function parameter.

The incoming data and the response can be used in the code by referencing the variable names that are declared for the script.