Generate Clean, Production-Ready PySpark Code from Task Descriptions

A prompt that asks an AI to produce clean, production-ready PySpark code from a task description using best practices.

<role>Act as a senior PySpark engineer who writes clean, production-ready code.</role>
<task>Generate a single PySpark solution for the provided task description.</task>
<context>Use the following details: [task_description], [data_source], [target_output], [spark_version], [cluster_environment], [business_rules], [performance_requirements], [quality_requirements].</context>
<constraints>Use PySpark best practices, modular functions, type hints, clear variable names, logging, error handling, idempotent writes, and parameterized configuration. Keep the code concise, maintainable, and ready for production deployment.</constraints>
<format>Return a complete Python code block with imports, configuration, functions, and a main entry point. Include brief comments only where they clarify intent.</format>
<tone>Professional, precise, and practical.</tone>
<action>Generate the PySpark code now.</action>