MigraChat: A Mapping-Driven Self-Service Validation Framework for Enterprise Database Migration

Enterprise database migration projects involve multiple stakeholders including data migration teams, business users, management, end-to-end testing teams, and product owners who require validation capabilities tailored to their specific concerns. Traditional validation approaches create communication bottlenecks where stakeholders depend on engineering teams to answer questions about source data, target data, field mappings, and transformation correctness. This paper presents MigraChat, a mapping-driven framework that combines Model Context Protocol (MCP) with Large Language Models (LLMs) to provide self-service validation capabilities across all stakeholder groups. The framework leverages existing mapping documents stored in S3 to intelligently construct queries across source data lakes, while utilizing microservice APIs exposed by modern cloud-native target systems. MigraChat enables business users to verify their data, management to assess migration scope and completeness, migration teams to validate transformation accuracy, testing teams to perform end-to-end verification, and product teams to confirm data availability. The framework provides migration intelligence including source table lineage, column utilization metrics, row count reconciliation, and data quality indicators. This architecture extends beyond migration validation to production support, enabling continued self-service data access post go-live without provisioning individual database credentials.