Coforge's Data Cosmos Code Conversion Agent
Coforge Limited
Code Conversion Agent is Coforge's multi-agent AI system engineered to accelerate large-scale data platform modernization. It automatically converts DDL, DML, SQL stored procedures, and transformation code from legacy platforms to modern cloud-native stacks, preserving business logic with high fidelity and delivering full auditability. Part of Coforge Data Cosmos™ — the innovation backbone combining platforms, agentic accelerators, and services for end-to-end data engineering, BI, governance, and analytics.
Source Platforms: SSIS, Informatica PowerCenter, Oracle PL/SQL, SQL Server T-SQL, and all other RDBMS and EDW platforms.
Target Platforms (20+): dbt on Snowflake/Databricks/BigQuery, PySpark on Azure Databricks/Apache Spark, and native SQL for Snowflake/BigQuery/Azure Synapse.
4-Stage Multi-Agent Pipeline: • Lineage Agent – generates end-to-end data flow diagrams showing source-to-target transformations, dependencies, and touchpoints • Conversion Agent – translates source code into target-platform syntax, preserving business logic, naming, and data types • Validation Agent – validates correctness, completeness, and logic fidelity with confidence scores per conversion unit • AutoFix Loop – automatically retries and patches validation failures until validation passes or escalates for manual review
Multi-LLM support (Google Gemini, OpenAI GPT-4o, Anthropic Claude, local Ollama) with customizable prompts means no vendor lock-in. Output includes source vs. target code, validation reports, lineage diagrams, and confidence scores.
Key Use Cases: • Banking – convert Oracle PL/SQL risk procedures to PySpark on Databricks with audit-ready lineage mapping • Insurance – migrate Informatica ETL to dbt on Snowflake for claims analytics with 95%+ logic fidelity • Travel – convert SSIS packages to PySpark, batch-processing hundreds of packages in parallel • Healthcare – migrate SQL Server stored procedures to Synapse SQL with AutoFix for HIPAA-specific logic
The 8-week implementation engagement covers: discovery and source-code profiling, platform deployment on Azure, multi-agent pipeline configuration, LLM selection and prompt customization, batch conversion setup, validation and AutoFix enablement, lineage/reporting configuration, and knowledge transfer.
Target Audience: Data Engineering & Modernization Teams, Cloud Migration Leads, Data Architects, ETL/Data Platform Engineers, and Analytics & Governance Leaders.