Glossary
A quick reference for the terminology used in ADL and its documentation.
Business Key One or more columns that uniquely identify a business entity. In a Data Vault, business keys drive how data is loaded into hubs and linked across objects.
CAT Scan A diagnostic tool in ADL that scans your project metadata for potential issues — missing connections, incomplete definitions, and other inconsistencies.
Classification A tag applied to data objects, data items, or connections to categorize them. Common examples include PII (personally identifiable information), Sensitive, and Confidential. See Classifications.
Connection See Data Connection.
Convention A naming rule defined for your project that helps keep tables, columns, and other elements consistently named. See Naming Conventions.
Data Connection A metadata entity that represents where data lives — a database, schema, or warehouse. Includes details like connection type (SQL Server, Snowflake, etc.), connection string, and location. See Connections.
Data Item A column or field within a Data Object. Each Data Item has properties like name, data type, nullability, and ordinal position.
Data Item Mapping A definition of how a single column maps from a source Data Object to a target Data Object, including any transformation logic.
Data Object A metadata entity that represents a table, view, or query in your data solution. Contains Data Items (columns), relationships, business keys, classifications, and extensions. See Data Objects.
Data Object Mapping A definition of how data flows from one Data Object to another — connecting a source to a target and describing the column-level transformations. See Data Object Mappings.
Data Vault A data modeling methodology that uses Hubs (business entities), Links (relationships), and Satellites (descriptive attributes) to create a flexible, auditable data warehouse.
DDL
Data Definition Language — SQL statements like CREATE TABLE, ALTER TABLE, and DROP TABLE that define database structure.
ETL / ELT Extract, Transform, Load (or Extract, Load, Transform) — the process of moving data from source systems to a target data store, with transformations applied either before or after loading.
Extension A custom key-value pair attached to a metadata entity. Extensions let you store any additional information that your templates can reference — for example, a schema name, deployment flag, or custom annotation.
Generation Metadata Schema The open-source JSON schema that ADL uses to store metadata. It defines the structure for data connections, data objects, mappings, and related entities. See the Schema Reference.
Handlebars
An open-source templating language used by ADL to write templates. Handlebars uses {{expressions}} to insert dynamic values and supports loops, conditionals, and helper functions. See Handlebars Helpers.
Hub In Data Vault modeling, a Hub represents a core business entity (e.g., Customer, Product) and contains the business key and metadata columns.
Landing Area The first layer in a data architecture where source data is loaded without transformation. Also called a staging area.
Link In Data Vault modeling, a Link represents a relationship between two or more Hubs (e.g., the relationship between Customer and Order).
Mapping See Data Object Mapping.
Metadata Structured information that describes your data solution — what tables exist, what columns they have, where they live, and how they relate to each other. In ADL, metadata is stored as JSON files. See Metadata & Data Objects.
Persistent Staging Area (PSA) A staging pattern that preserves the full history of data as received from source systems. Unlike a regular staging area, a PSA keeps every version, enabling re-processing of historical data.
Perspective A saved graph view in ADL with specific node positions, zoom level, and layout. Perspectives let you save and switch between different visual arrangements of your data model.
Satellite In Data Vault modeling, a Satellite stores descriptive attributes and tracks changes over time for a Hub or Link.
Template A Handlebars file that defines what output ADL should generate. When processed against a metadata object, it produces a text file (SQL script, documentation, configuration, etc.). See Templates & Code Generation.
Template Mapping A link between a Data Object and a Template that tells ADL “use this template to generate output for this object.”