Metadata driven quality framework service

Low Code Rule Configuration: Business and data teams can add or modify data quality rules using metadata instead of engineering changes reducing dependency on development cycles Result: Faster rollout, lower maintenance cost One Framework, Multiple Use Cases: Supports: a. Data lakes b. Data warehouses c. ETL/ELT pipelines d. Streaming pipelines Result: Unified DQ strategy across the enterprise Enterprise-Grade Governance: Built-in support for following a. AI enabled Rule generations and versioning b. Dataset ownership c. Quality Metrics and Thresholds d. Audit trails Result: Compliance-ready, auditable data quality processes Cloud-Optimized for Cost & Scale: Runs only when required, scales automatically, and leverages serverless components to minimize operational overhead Result: Pay only for what you use Accelerated Time-to-Value: Pre-built rule templates, connectors, and reference implementations enable customers to go live in weeks, not months Result: Faster business outcomes from analytics and AI Designed for Modern Data & AI: Ensures high-quality, standardized data required for following a. BI & reporting b. Machine learning models c. Personalization & recommendations d. Regulatory reporting Result: Reliable insights and trusted AI outputs Differentiators for AWS Marketplace: a. True metadata-driven design (not rule hard-coding) b. Modular adoption (start small, scale enterprise-wide) c. Low learning curve for customers d. Extensible for future AI-driven quality checks