SAS to Python Code Migration

The challenge

A technology-driven organization aimed to modernize legacy analytics systems by migrating SAS-based codebases to Python for improved scalability, maintainability, and integration with modern technology ecosystems. The objective was to build an intelligent migration framework capable of converting legacy scripts while preserving business logic, dependencies, and execution context. The solution was designed to streamline legacy modernization, reduce manual conversion effort, and enable seamless integration with modern analytics, deployment, and automation pipelines.

Key challenges

• Managing large and complex SAS codebases with multiple dependencies
• Preserving business logic and execution flow during migration
• Handling context-sensitive conversions for complex scripts and modules
• Ensuring compatibility with modern deployment and CI/CD pipelines
• Reducing manual effort while maintaining code accuracy and reliability

Technology used

• Programming Language: Python (Pandas, LangChain, OpenAI API)
• Frontend: Streamlit for user interaction and migration workflows
• LLM Models: GPT-family models for intelligent code conversion and transformation
• Development & Deployment: GitHub and CI/CD pipelines for integration and automation
• Migration Framework: Context-aware chunking and LLM-assisted code refactoring

What we built

• Data Extraction: Identifies SAS code, dependencies, and lineage for migration planning
• Direct Code Conversion: Converts simple SAS scripts directly into Python equivalents
• Chunk-Based Migration: Breaks down complex modules for intelligent LLM-driven conversion
• Context-Aware Transformation: Maintains execution history and business context during conversion
• Code Merge & Refactoring: Combines converted modules and optimizes code quality
• Interactive Interface: Provides a Streamlit-based environment for migration, testing, and validation

Objectives

• Convert legacy SAS codebases into Python for modern technology stack adoption
• Reduce complexity associated with large codebases, dependencies, and business logic
• Enable seamless integration with modern development and deployment ecosystems
• Improve maintainability and scalability of enterprise analytics workflows
• Accelerate migration timelines through intelligent automation
• Ensure accurate and context-aware code transformation

Outcomes

• Structured and reliable SAS-to-Python migration with reduced manual effort
• Improved conversion accuracy through context-aware LLM engineering
• Faster modernization and deployment through automated workflows
• Enhanced compatibility with modern analytics and CI/CD ecosystems
• Flexible interface for testing, validation, and migration readiness
• Reduced maintenance cost and improved long-term scalability for enterprise applications