Technical architecture
An adaptive transition layer that detects schemas automatically, maps to canonical concepts with confidence-based governance, and self-heals when sources change.
Architecture overview
Where the Foundry sits in your data infrastructure.
Whether you use medallion architecture, data mesh, or your own layered approach, the transition from raw source data to governed, consistent output is traditionally the most fragile point. MTN Data Foundry operates at this boundary—automatically detecting structure, mapping to a canonical layer, and adapting when source schemas change.
Medallion terminology provides a familiar reference point. The Foundry itself is architecture-agnostic and works with any pipeline that requires resilient schema mapping.
What it does
- • Ingests bronze-layer data from any source
- • Maps to silver-layer semantic definitions
- • Adapts when source schemas change
- • Outputs governed, consistent data downstream
What it does not do
- • Replace your silver or gold layers
- • Compete with your warehouse or BI tools
- • Require changes to source systems
- • Store data long-term (it's a transition layer)
Ingestion and schema detection
MTN Data Foundry ingests data from heterogeneous sources without requiring upfront schema definitions. Incoming payloads are fingerprinted to identify structure and detect changes.
- Supports JSON, HL7, X12, CSV, and structured database connections
- Schema signatures are computed from payload structure, not just field names
- New schema versions trigger mapping workflows automatically
- Malformed payloads are quarantined with detailed error context
Semantic mapping and governance
Data is mapped to a canonical concept layer that represents healthcare entities consistently across sources. Mapping decisions are governed by confidence thresholds and human approval.
- Mappings are suggested based on field values, patterns, and healthcare standards
- Confidence scores determine whether mappings auto-apply or require review
- Human-in-the-loop workflows route uncertain cases to domain experts
- Mapping versions are immutable; changes create new versions
Governance model
Configurable thresholds control automation vs. human review
Every decision is logged for compliance and debugging
Mappings are versioned; rollback is always available
Backward compatibility guaranteed
Change detection and self-healing
When source schemas change, the Foundry detects the change, evaluates impact, and either adapts automatically or routes for review. Historical data remains stable.
- Schema changes detected through payload fingerprinting
- High-confidence changes apply automatically with audit logging
- Low-confidence changes pause and notify for human decision
- Historical data is never retroactively remapped unless explicitly requested
Monitoring and resilience
Continuous monitoring of data transmission, structure, and quality. Problems are surfaced before they impact downstream systems.
- Transmission health monitoring tracks source connectivity and data flow
- Alerting triggers on schema drift, volume anomalies, and quality degradation
- Downstream systems are protected from bad data through validation gates
- Full observability through structured logs and metrics export
Deployment options
Flexible deployment to fit your security and infrastructure requirements.
Cloud-hosted
Managed deployment in your preferred cloud environment with SOC 2 compliance and BAA support.
- • AWS, Azure, or GCP
- • Single-tenant isolation
- • Managed updates and monitoring
On-premises
Deploy within your existing infrastructure for complete data control and air-gapped environments.
- • Kubernetes or VM deployment
- • No external data transmission
- • Your security policies apply
Ready to discuss architecture?
We'll walk through how this fits your specific stack and answer technical questions in detail.