SQL to PySpark
Executive Summary
Modernizing enterprise data platforms is never easy. For many organizations, the biggest hurdle lies in migrating legacy SQL scripts into modern platforms like Spark and Databricks. With thousands of scripts powering ETL pipelines, manual rewrites can take months, drain engineering capacity, and still result in mismatches. In this blog, we share how we built an agentic application powered by LLMs that converts thousands of SQL scripts into PySpark DataFrame code - with inbuilt validation, accuracy scoring, and actionable feedback for engineers.
Challenge
Scale
Rewriting thousands of scripts manually is painfully slow.
Accuracy
Small mismatches between SQL and PySpark outputs can break downstream systems.
Cost
Skilled engineers spend time on repetitive work instead of innovation.
Solution Overview
We developed a single intelligent conversion agent equipped with a modular set of tools. This agent orchestrates the end-to-end workflow - from reading SQL scripts, generating PySpark, validating outputs, refining mismatches, and streaming results back to the engineer.
How it Works
- Upload & Process - Engineers upload SQL scripts through an API endpoint.
- Agent Orchestration - The agent:
- Runs the original SQL and saves outputs.
- Converts SQL into PySpark DataFrame code.
- Executes the generated PySpark.
- Compares outputs for validation.
- Refinement Loop – If outputs don’t match, the agent automatically retries and refines the code up to three times.
- Results Streaming – The agent returns PySpark code, a confidence score, complexity rating, completion percentage, and prioritized action items.
Bulk upload, Explain code, Validation scripts
Code complexity, Agentic conversion with Confidence score and Completion percentage.
Built-in Code validation by comparing outputs from original and converted scripts.
Compare Report
Key Benefits
Key Outcomes with SQL to Pyspark
70% Faster Migration
Thousands of scripts processed in a fraction of the time.
Reduced Errors
Built-in validation ensured parity before production deployment.
Efficient Resource Use
Engineers focused on high-value, complex cases instead of repetitive rewrites.
Transparency & Trust
Confidence scores and explain-code features built trust in automated outputs.
Technical Foundation
API Layer
FastAPI endpoint
Agent Framework
Built with Agno, powered by LLMs (OpenRouter/OpenAI)
Integrated Tools
run_sql_script , run_pyspark_job_from_string , compare_verification_csvs
Execution Runtime
PySpark with automated session management.
Output Format
JSON with code, confidence, complexity, completion %, and action items.
Extensibility
Supports future conversions like Teradata BTEQ, HiveQL, SnowSQL, Synapse, Redshift, and more.
Conclusion
Data modernization doesn’t have to be a bottleneck. By leveraging agentic AI applications with modular tools, enterprises can accelerate migrations, reduce risk, and maximize engineering efficiency. Our SQL-to-PySpark Conversion Application is just one example of how AI-driven automation can tackle large-scale migration challenges - paving the way for faster adoption of modern cloud platforms like Snowflake, Databricks, Fabric, Synapse, Redshift, and BigQuery.
