Coforge's Data Cosmos ProbeData

ProbeData is a Coforge Data Cosmos accelerator that helps customers automate the entire data validation and quality assurance lifecycle on Azure data platforms — replacing manual, multi-day effort with an AI-driven platform that generates validation rules, reconciliation tests, SQL scripts, root-cause analysis, and remediation plans in minutes. Whether it is an Azure cloud migration, data warehouse modernization to Azure Synapse or Microsoft Fabric, system integration QA, or compliance audit, ProbeData delivers an 80%+ reduction in QA effort and compresses migration timelines from weeks to minutes. Part of Coforge Data Cosmos™ — the innovation backbone combining platforms, agentic accelerators, and services for end-to-end data engineering, BI, governance, and analytics on Azure.

Manual Pain Points Addressed: weeks of manual test writing, cross-platform validation complexity, silent failures (schema mismatches, missing columns, empty-string placeholders), no root-cause guidance, and disconnected tooling.

Six Integrated Modules: • Discovery & Data Profiling – instant schema browsing across all connected systems • Mapping Specification Validation – parses multi-sheet Excel mapping specs (Excel to SQL in minutes) with dependency-ordered validation • Validation Rules Engine – Azure OpenAI–generated business rules (NULL_CHECK, RANGE_CHECK, UNIQUENESS, REFERENTIAL_INTEGRITY, FORMAT_CHECK) with dialect-aware SQL • Data Reconciliation (Quality Engine) – production-grade quality checks at scale with per-column pass/fail results • AI Recommender – structured remediation playbooks with root-cause analysis, SQL fixes, and preventive measures • Conversational AI Agent – natural language test creation via Azure OpenAI function calling

Key Value: 80%+ reduction in data validation effort, compressed migration timelines from weeks to minutes, cross-platform coverage (Azure Synapse, Microsoft Fabric, Snowflake, Databricks, PostgreSQL, MySQL), and end-to-end validation in one governed platform on Azure.

Key Use Cases: • Banking – data validation for core banking migrations to Azure Synapse with BCBS 239 regulatory reconciliation • Insurance – claims data reconciliation across Guidewire-to-Synapse migrations with AI-diagnosed quality failures • Travel – booking data validation across GDS-to-Fabric pipelines during peak-season loads • Healthcare – HIPAA-compliant reconciliation for EMR to Azure clinical data warehouse migrations with AI-generated PHI validation rules

The 8-week implementation engagement covers: discovery and source profiling, platform deployment on Azure (AKS, Azure OpenAI, Azure SQL), mapping specification validation setup, validation rules engine configuration, reconciliation quality engine enablement, AI Recommender and conversational agent setup, and knowledge transfer to your Azure data teams.

Target Audience: Data Quality & QA Teams, Data Engineering & Migration Teams, Cloud Migration Leads, Data Architects, and Compliance & Audit Teams adopting or extending Microsoft Azure.