In an elastic mapping, you can use the Python transformation to define transformation functionality using the Python programming language. The Python transformation can be an active or passive transformation.
You can use the Python transformation to define simple or complex transformation functionality. You can also use the Python transformation to implement machine learning. For example, you can load a pre-trained model through a resource file and use the model to classify input data or to create predictions.
To create a Python transformation, you write the following types of Python code snippets:
Pre-partition Python code that runs one time before it processes any input rows.
Main Python code that runs when the transformation receives an input row.
Post-partition Python code that runs after the transformation processes all input rows.
To use the Python transformation, your organization must have the appropriate licenses.
You cannot use the Python transformation with a Graviton-enabled cluster. For more information on a Graviton-enabled cluster, see
Data Integration Elastic Configuration
When you create a Python transformation, ensure that you review the Python code to verify that it is free from potentially unsafe active content such as queries, remote scripts, or data connections before you run the code in a mapping task.