Draft:Unified Data Layer
Submission declined on 24 July 2025 by Caleb Stanford (talk).
Where to get help
How to improve a draft
You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Editor resources
|  |
| Submission declined on 10 July 2025 by AirshipJungleman29 (talk). This draft's references do not show that the subject qualifies for a Wikipedia article. In summary, the draft needs multiple published sources that are:
This submission is not adequately supported by reliable sources. Reliable sources are required so that information can be verified. If you need help with referencing, please see Referencing for beginners and Citing sources. Declined by AirshipJungleman29 14 days ago. | |
Comment: WP:TOOSOON and may not meet notability, see Google scholar. Caleb Stanford (talk) 20:58, 24 July 2025 (UTC)
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..[1] 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
[edit]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[2] 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
[edit]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
[edit]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[3]
Relation to other concepts
[edit]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[4]. 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[5] for audit, replay, and historical analytics.
See also
[edit]References
[edit]- ^ "Hewlett Packard Enterprise drives agentic AI era with an intelligent, unified data layer for AI". Hewlett Packard Enterprise. 18 March 2025. Retrieved 10 July 2025.
- ^ "From Chaos to Clarity: Transforming Public Sector Data with a Unified Data Layer". GovNet Technology. 20 March 2024. Retrieved 10 July 2025.
- ^ "Why Customers Say Unified Data Is Critical for AI Agents". Salesforce. 3 April 2025. Retrieved 10 July 2025.
- ^ "Are you using the unified OT data layer to bridge the natural gap between IT and OT?". Control Engineering. 11 November 2023. Retrieved 10 July 2025.
- ^ "Unified Manufacturing Data Architecture Framework". UMDA Hub.
- in-depth (not just passing mentions about the subject)
- reliable
- secondary
- independent of the subject
Make sure you add references that meet these criteria before resubmitting. Learn about mistakes to avoid when addressing this issue. If no additional references exist, the subject is not suitable for Wikipedia.