Install the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function. This approach is cost-effective but may not fully leverage the Kubeflow Pipelines SDK's capabilities.

Employ the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there. This method is straightforward but may not be the most scalable or cost-effective for large-scale data processing tasks.

Use the func_to_container_op function to construct custom components from the Python code. This approach efficiently integrates Python code into the SDK by creating custom components, leveraging the SDK's orchestration capabilities.

Enclose the custom Python code into Docker containers, and apply the load_component_from_file function to import the containers into the pipeline. This method offers flexibility and scalability but requires additional Docker expertise.

Combine the use of func_to_container_op for simple integrations and Docker containers for more complex scenarios to achieve both simplicity and scalability. This hybrid approach leverages the strengths of both methods.