Bulk SQL Scripts to PySpark Conversion

Client Problem Statement

• Manually converting thousands of SQL scripts to PySpark is time-consuming and labor-intensive.
• Ensuring consistent coding standards and best practices during conversion is difficult.
• Manual conversion introduces errors, leading to inefficiencies and delays.
• Lack of resources for bulk script conversion slows down data modernization efforts.

Project Overview

• Automates the conversion of large batches of SQL scripts into PySpark code.
• Uses advanced AI models to enforce coding standards and best practices.
• Adds code comments and formatting to enhance readability and maintainability.
• Saves time and minimizes errors associated with manual conversion.
• Provides a scalable, efficient solution for data migration and modernization projects.

Inputs from the Client

• Thousands of SQL script files, ranging from simple queries to complex stored procedures.

AI Engine

• Converts SQL scripts into optimized PySpark code.
• Enforces naming conventions and coding standards for consistency.
• Adds detailed code comments for easier understanding and future maintenance.
• Formats code for readability and integrates custom logic where needed.
• Bulk processes scripts to ensure high efficiency and scalability.

Output

• PySpark scripts ready for unit testing and integration into the data pipeline.

Benefits

• Significantly reduces the time and effort needed for script conversion.
• Ensures high-quality, consistent PySpark code aligned with best practices.
• Reduces errors and enhances code maintainability with automated documentation.
• Enables rapid modernization of data systems with scalable bulk conversion.

Highlights

• Reusable framework adaptable for various SQL to PySpark conversion needs.
• High-speed bulk processing for large volumes of scripts.
• Enhances productivity and accelerates data modernization initiatives.

Technology Stack

• Google Vertex AI
• Gemini Pro LLM
• Custom code transformation algorithms