Data Cosmos Auto Data Mapping – AI-Powered Source-to-Target Data Mapping

• Complex Mapping Effort: Translating between legacy and modern data models requires high-friction, manual bridging.
• Time-Consuming Pipelines: Heavy reliance on manual effort creates severe bottlenecks and delays in ETL/ELT pipeline delivery.
• Limited Visibility: Complete lack of end-to-end lineage and transformation traceability creates a “black box” environment.
• High Error Risk: Inconsistent human-driven mappings introduce severe data quality vulnerabilities and downstream issues.
• Collaboration Gaps: Misalignment and broken communication loops persist across engineering, analytics, and governance teams.

How It Works — 4-Stage Intelligent Pipeline:

• AI-Powered Auto Mapping: Leverages AI to suggest accurate source-to-target data mappings automatically. Analyzes schema metadata, column names, data types, and business context to generate high-confidence mapping recommendations — eliminating manual mapping effort.
• Visual Mapping Canvas: Features an intuitive side-by-side canvas for schema mapping and transformation design. Enables data engineers and architects to visually review, refine, and approve AI-suggested mappings with drag-and-drop simplicity.
• End-to-End Data Lineage: Provides clear lineage graphs to ensure full traceability across all connected systems. Maps data flow from source to target including transformations, joins, and business rules — supporting audit readiness and governance compliance.
• Schema Management Layer: Supports flexible modeling via schema ingestion through CSV or manual input. Enables teams to onboard new sources and targets rapidly, manage schema versions, and maintain a governed catalog of all mapped entities.

Output: AI-generated source-to-target mapping specifications, visual mapping designs, end-to-end lineage graphs, schema inventory reports, transformation logic documentation, and exportable mapping artifacts for downstream ETL/ELT pipeline development.

Key Benefits:

• 50% Faster Data Mapping: Accelerates the integration process through automated AI-driven mapping capabilities
• 45% Improved Team Productivity: Boosts output across data engineering and analytics teams by eliminating manual mapping cycles
• 40% Reduction in Mapping Errors: Utilizes AI-driven validation to ensure higher data accuracy and consistency
• Stronger Cross-Functional Collaboration: Facilitates better communication and alignment across engineering, analytics, and governance stakeholders
• Enhanced Audit Readiness: Provides full lineage visibility to strengthen governance and compliance across all data transformations

Industry Applications:

• Banking: Automate source-to-target mapping for core banking data migrations (Oracle/Teradata → Snowflake). Lineage graphs ensure audit compliance for regulatory reporting transformations.
• Insurance: Accelerate claims and policy data mapping across legacy mainframe systems to modern cloud warehouses. AI-driven validation ensures actuarial logic fidelity across all mapped transformations.
• Travel: Map complex reservation, crew, and operations data across fragmented airline/hospitality systems. Visual canvas enables ops teams to validate domain-specific mapping logic.
• Healthcare: Map patient and clinical data across EHR systems with full lineage traceability. Schema management ensures HIPAA-compliant data handling across all mapped entities.

Cloud-Native Deployment on AWS: Deployed on Amazon EKS. Amazon Bedrock provides AI-powered mapping intelligence. Amazon S3 stores schema definitions, mapping artifacts, and lineage outputs. Integrates with Amazon Redshift, AWS Glue, Snowflake on AWS, and Databricks on AWS as target platforms.