Accelerator Overview
Agentic Orchestration Framework
Coordinates multiple LLM-powered agents (Parser, Lineage, Converter, Validator) for fully automated SAS-to-PySpark migration. Built on Databricks Jobs with LangGraph-based agent routing and dependency control. Feedback-driven prompt refinement for self-learning and accuracy improvement.
Benefit: 50% faster migration with 95%+ code accuracy and continuous model learning.
Unity Catalog– Integrated Lineage & Governance
Extracts SAS dataset dependencies and maps them directly to Unity Catalog entities. Block-level lineage and schema mapping to ensure referential integrity. Unified metadata and access control across migrated workloads.
Benefit: Delivers full transparency, auditability, and compliance from source to Lakehouse.
GenAI SAS to PySpark Conversion Engine
Transforms SAS logic to PySpark using rule-driven and LLM contextual translation. Prompt DB and Delta-based embedding store for reusable conversion intelligence. Self-healing logic corrections via Code Quality and Auto-Fix agents.
Benefit: Generates production-ready, optimized PySpark with minimal manual rework.
Validation & CI/CD Automation
Automates testing, deployment, and validation pipelines natively on Databricks. Schema, row-count, and data-hash comparisons ensure output parity. Automated notebook deployment and rollback workflows via DevOps pipelines and Terraform.
Benefit: Ensures 100% validated, reproducible results across environments.
Agentic UI & Feedback Loop
Interactive interface for reviewing conversions, lineage graphs, and quality metrics. Real-time user feedback captured for prompt optimization and error correction. Power BI dashboards connected to Databricks SQL for visual tracking and reporting using REST APIs.
Benefit: Enables collaborative governance and faster iterative improvement.
