Draft:Unified Data Layer

A Unified Data Layer (UDL) is a governed storage and query plane designed to consolidate time series signals, domain-specific Common Data Models (CDMs), and reference tables in a single environment that supports advanced analytics and Artificial Intelligence workloads. In practice a UDL combines the schema-on-read flexibility of a data lake with the governance and lineage controls typical of a data warehouse. It is frequently implemented on an open-table format such as Apache Parquet, Delta Lake, or Apache Iceberg, and exposes data by way of ANSI SQL, GraphQL, REST, or streaming interfaces like Apache Kafka.

Concept and purpose

The main goal of a UDL is to provide a single “source of truth” for enterprise data. Incoming payloads, often produced by protocols such as MQTT or OPC UA, are validated against data contracts, mapped to standardized entity names and units (for example, SI), and enriched with provenance metadata that satisfies the traceability requirements of regulations such as Title 21 CFR Part 11. Role-based or attribute-based access control (aligned with NIST Special Publication 800-53) ensures that engineers, data scientists, and external partners see only the records they are authorized to view.

Architecture

Most deployments follow a tiered structure:

• Raw zone – immutable files landed directly from edge brokers for replay or forensics;
• Harmonized zone – CDM tables that have passed contract validation;
• Semantic zone – dimensional or star schemas published to business intelligence and self-service tools;
• Feature store – time-aligned snapshots that feed machine learning training and online inference.

Upstream validation is often performed by an on-premises Edge Intelligence Hub, which appends contextual attributes (BatchID, EquipmentID, OrderID) before forwarding records to the UDL and to a real-time publish/subscribe broker sometimes called a Unified Namespace.

Adoption and use cases

In the automotive, semiconductor, and pharmaceutical sectors, manufacturers report that a UDL reduces the effort required to calculate cross-site key performance indicators such as overall equipment effectiveness (OEE) and first-pass yield, while also accelerating predictive maintenance and digital twin projects. Because the same lineage metadata is available to auditors, the architecture is increasingly referenced in discussions around regulated analytics and GxP compliance, and is a key enabler for Agentic AI.

Relation to other concepts

The UDL combines ideas from the lakehouse and from data virtualization frameworks. When data is co-located, the layer can persist open-format tables; when sources remain distributed, it exposes federated views through a single semantic catalog, avoiding bulk replication. Contract-driven governance and alignment with manufacturing standards such as ISA-95 and ISA-88 give the model its industrial focus that bridges the gap between IT and OT. Common Data Model tables can therefore be stored in the UDL or queried in place, while the Unified Namespace supplies the low-latency event stream that the UDL captures for audit, replay, and historical analytics.

See also

• Data mesh
• Linked data
• Common Data Model
• Data contract
• Data abstraction