Precise definitions for terms that are commonly confused, conflated, or misused in enterprise data architecture. Each term includes what it is, what it is not, and where it fits in the target-state architecture.
Agent Access
Consumption of platform data by AI agents through governed interfaces: semantic layers, data products exposed as tools, or guarded text-to-SQL. Not: a mandate to run agents on the EDP; agents are consumers, not tenants. See Agents as Consumers. Architecture layer: Layer 6 (consumption).
Bronze / Silver / Gold
Data refinement stages within the EDP: raw ingestion (bronze), cleansed and conformed (silver), business-ready (gold). Not: business process stages or a workflow model. Architecture layer: Layer 4 (EDP).
Catalog
The service that tracks tables, vends storage credentials, and enforces access policy across query engines, typically speaking the Iceberg REST API (Apache Polaris, Unity Catalog, AWS Glue, BigLake metastore). Not: just a metadata directory; it is a control point and a lock-in surface. See Open Formats and Catalogs. Architecture layer: control plane.
Data Fabric
A vendor-marketed architectural approach emphasizing metadata-driven data integration across distributed systems. Not: a single product or platform. Not the same as data mesh. Included here for disambiguation; this guide does not prescribe data fabric as a pattern.
Data Lake
A storage system for raw, unprocessed data in native formats (Parquet, JSON, CSV). Not: a data warehouse. Not governed by default. Architecture layer: Layer 4 (EDP), typically the bronze/raw tier.
Data Mesh
An organizational model for decentralized data ownership where domain teams own and publish data products. Not: a technology platform. Not a replacement for centralized infrastructure. See Data Mesh positioning for detailed treatment. Architecture layer: organizational overlay across Layers 4-5.
Data Products
Governed, documented, discoverable datasets with defined ownership, schema, SLA, and quality contract. Not: raw tables, API endpoints, or “any data someone published.” Architecture layer: Layer 5 (Semantic / Data Product Layer).
Data Warehouse
A structured analytical data store optimized for SQL queries, aggregations, and reporting. Not: an operational database. Not a data lake (though lakehouses blur this line). Architecture layer: Layer 4 (EDP).
Enterprise Data Platform (EDP)
An integrated analytical platform that ingests, historizes, governs, and serves data across business domains for analytics, AI, and regulatory reporting. Not: an operational platform, a transaction processing system, or a workflow engine. Architecture layer: Layer 4.
Feature Store
An ML-specific data system that manages the computation, storage, and serving of features for model training (offline) and inference (online). Not: a general-purpose data store. Bridges the analytical and operational worlds. Architecture layer: between Layer 5 and Layer 6.
Lakehouse
A platform combining data lake storage flexibility with data warehouse query capabilities, typically using open table formats (Delta Lake, Apache Iceberg). Not: a replacement for operational databases. Architecture layer: Layer 4 (EDP).
MCP (Model Context Protocol)
The open standard for connecting AI agents to tools and data, under Linux Foundation governance since late 2025. Warehouse vendors expose governed query tools through managed MCP servers. Not: a security or authorization model; it standardizes connection and discovery only. Architecture layer: consumption interface at Layer 6.
Open Table Format
Table metadata specifications (Apache Iceberg, Delta Lake) that let multiple engines read and write the same data on object storage with transactional guarantees. Not: a database or a query engine. Architecture layer: Layer 4 (EDP) storage.
Operational Data Store (ODS)
A purpose-built store for current-state, operational access patterns with low latency and transactional consistency. Holds denormalized, access-optimized data for operational use. Not: the EDP. Not a data warehouse. See EDP Is Not an ODS. Architecture layer: Layer 3a.
Raw / Curated / Consumption
Alternative naming for bronze/silver/gold. Same concept: data refinement stages within the EDP. Some organizations prefer this naming to avoid the “gold = final product” misconception. Architecture layer: Layer 4 (EDP).
Semantic Layer
Governed business definitions (metrics, entities, blessed joins) served as an interface between data products and their consumers, human or agent. The Open Semantic Interchange (OSI) spec is the emerging portability standard. Not: a BI-tool convenience; it is access infrastructure. Architecture layer: Layer 5.
Serving Layer
Infrastructure that sits between data products and operational consumers, optimized for low-latency, high-concurrency access. APIs, caches, materialized views, feature stores. Not: the EDP. Not the data product itself. Architecture layer: between Layer 5 and operational consumers.
Transaction Processing Platform
Systems designed for ACID-compliant, low-latency business transactions: payments, orders, bookings, settlements. Not: the EDP. Not analytical. Architecture layer: Layer 3.
Workflow Platform
Systems for orchestrating multi-step business processes with state management, human tasks, exception handling, and SLA tracking. Temporal, Camunda, or purpose-built BPM. Not: dbt. Not Airflow (which is a pipeline orchestrator, not a business workflow engine). Architecture layer: Layer 3.
Zero-ETL
Vendor-managed continuous replication from an operational database into an analytical store, with no pipeline to build. Not: a replacement for contracts and quality gates; it is a managed variant of CDC ingestion. Architecture layer: ingestion into Layer 4.
Terms with dedicated pages in this guide (e.g., Data Mesh) are defined briefly here with a link to the full treatment.