Metadata Graph

The fundamental nodes in a metadata graph represent distinct metadata entities. These are not the raw data records themselves, but the containers, definitions, and descriptors for that data.

Core entity types include:

• Data Assets: Tables, files, APIs, streams, and reports.
• Structural Elements: Schemas, databases, columns, fields, and data types.
• Operational Artifacts: Pipelines, jobs, transformations, and applications.
• Business Concepts: Glossary terms, business entities (e.g., 'Customer'), metrics, and policies.

Each node is uniquely identified and carries properties describing its technical and business context.

Edges define the semantic and operational connections between entity nodes, transforming a collection of metadata into an interconnected graph. These relationships encode meaning and enable powerful graph traversals.

Key relationship types are:

• Structural: contains (Schema → Table), hasColumn (Table → Column).
• Lineage: derivedFrom (Output Table ← Input Table), executedBy (Transformation → Pipeline).
• Semantic: isTypeOf (Column 'cust_id' → Business Term 'Customer Identifier'), relatedTo.
• Operational: accessedBy (Table → Application), governedBy (Asset → Policy).

These typed edges provide the paths for answering complex questions about data flow and dependency.

The formal ontology defines the types of entities (classes) and relationships (properties) allowed in the graph, providing a shared, consistent vocabulary. It acts as the graph's schema, ensuring semantic interoperability.

Its components include:

• Class Hierarchy: A taxonomy (e.g., Database is a subclass of DataContainer).
• Property Definitions: Domain and range restrictions for relationships (e.g., contains can only link a Schema to a Table).
• Constraints & Rules: Logical rules for inference (e.g., if a Column is classifiedAs 'PII', then its parent Table is classifiedAs 'Restricted').

This schema is often defined using standards like RDF Schema (RDFS) or the Web Ontology Language (OWL).

Properties are key-value pairs attached to entities and relationships, storing descriptive metadata. They provide the detailed context needed for discovery, governance, and operational use.

Examples of entity properties:

• Technical: dataType: string, rowCount: 1,000,000, lastUpdated: 2024-05-15T10:30:00Z.
• Business: description: 'Master customer contact list', owner: '[email protected]', sensitivity: 'Confidential'.
• Operational: refreshFrequency: 'daily', SLATarget: '99.9%'.

Relationship properties might include confidenceScore: 0.95 for a lineage link or transformationLogic for a derivedFrom edge.

A critical function of the metadata graph is to resolve and unify references to the same logical entity across different source systems. This creates a single, authoritative reference point.

This involves:

• Entity Resolution: Identifying that CUST_DB.dbo.CUSTOMER and CRM_SYSTEM.USERS refer to the same business concept of 'Customer'.
• Cross-System Linking: Establishing sameAs or equivalentTo relationships between matched entities.
• Global Unique Identifiers (URIs): Assigning a persistent, system-agnostic ID to each canonical entity, such as urn:company:entity:Customer.

This layer is what transforms a catalog of siloed metadata into a unified enterprise map.

A metadata graph system often includes a reasoner that applies logical rules defined in the ontology to infer new facts, enriching the graph automatically and ensuring consistency.

Capabilities include:

• Transitive Closure: Inferring that if Pipeline A upstreamOf Pipeline B, and Pipeline B upstreamOf Report C, then Pipeline A is implicitly upstreamOf Report C.
• Classification: Automatically tagging all columns within a table as 'PII' if the table is classified as such.
• Consistency Checking: Detecting contradictions, such as a Column being marked deprecated: true but also criticalFor: 'Q4 Report'.

This moves the graph from a static store to an active, intelligent model of the data ecosystem.

An architectural framework that uses a knowledge graph as a unifying semantic layer to provide integrated, contextualized, and governed access to enterprise data across disparate sources. It is the overarching architecture within which a metadata graph operates.

• Core Function: Provides a business-meaningful, virtualized view of data.
• Key Component: Relies on a central ontology to define concepts and relationships.
• Contrast with Data Fabric: A semantic data fabric specifically employs formal semantics (ontologies, RDF) for integration, whereas a general data fabric may use other metadata types.

A centralized inventory of an organization's data assets, enhanced with metadata, search, and governance tools. A modern, active data catalog is often powered by a metadata graph to enable discovery based on meaning and context.

• Evolution: From a passive spreadsheet of tables to an interactive semantic catalog.
• Graph-Powered: Uses graph relationships to show lineage, impact analysis, and data dependencies.
• Primary Use Case: Enables data discovery, understanding, and trust for analysts and data scientists.

The tracking of data from its origin, through its transformations and movements, to its final consumption. In a metadata graph, lineage is modeled as a directed graph of processes and datasets.

• Graph Representation: Nodes represent datasets, columns, or processes; edges represent derivation and dependency relationships.
• Critical for: Compliance audits (e.g., GDPR), impact analysis for schema changes, and debugging data pipeline failures.
• Provenance: A closely related concept focusing on the origin and history of a specific data item.

A formal, explicit specification of a shared conceptualization. In a metadata graph, the ontology defines the types of metadata entities (e.g., Dataset, Column, User) and the allowed relationships between them (e.g., contains, derivedFrom, ownedBy).

• Role: Serves as the schema or data model for the metadata graph.
• Standards: Often expressed in languages like OWL (Web Ontology Language) or RDFS (RDF Schema).
• Enables: Semantic reasoning, consistency validation, and intelligent inference of new metadata relationships.

A system that provides a unified, graph-based view over heterogeneous data sources in real-time using mapping definitions, without requiring the physical materialization of the entire graph. A metadata graph can be implemented as a VKG.

• Key Benefit: Provides real-time, integrated metadata views without massive ETL and storage costs.
• Technology: Relies on query federation and mapping languages like R2RML or RML.
• Use Case: Creating an enterprise-wide metadata graph where source metadata remains in its native systems (e.g., Data Catalogs, DBMS, ETL tools).

The process of combining data from disparate sources by resolving schematic and data-level conflicts through the use of shared ontologies and semantic mappings. Building a metadata graph is a primary application of semantic integration.

• Core Challenge: Aligning different metadata schemas (e.g., one tool's 'table' is another's 'entity').
• Process: Involves entity resolution, schema mapping, and ontology alignment.
• Outcome: Creates a coherent, unified metadata layer that understands that CustomerID in System A and client_identifier in System B refer to the same conceptual attribute.