The question of where to store metadata has become more pressing as organisations accumulate more data assets and more complex relationships between those assets. Two architectural approaches dominate: relational databases and knowledge graphs. Both can store metadata, but they’re fundamentally different in how they represent information.

How Relational Databases Handle Metadata

In a relational model, metadata is stored in tables with predefined schemas. A typical implementation includes tables for datasets, columns, owners, lineage relationships, and quality metrics. Relationships are expressed through foreign keys.

This approach is well-understood and reliable. SQL databases are mature technology. Every data engineer knows how to query them. Performance characteristics are predictable.

For straightforward metadata management — tracking what datasets exist, who owns them, and basic lineage — a relational database is perfectly adequate.

The limitations emerge as complexity increases.

Schema rigidity. New metadata types require database migration, application changes, and testing. For organisations where requirements evolve frequently, this creates friction.

Relationship complexity. Direct, predefined relationships work well. Multi-hop queries like “which datasets are affected if Source X changes?” require joining through multiple tables, and performance degrades as chains grow.

Semantic richness. Relational schemas capture structure but not meaning. The richness of how a business concept is interpreted in different contexts is difficult to express relationally.

How Knowledge Graphs Handle Metadata

Knowledge graphs store information as entities (nodes) connected by typed relationships (edges), typically using RDF or property graph models. This model offers several advantages.

Schema flexibility. New entity types and relationship types can be added without restructuring existing data. No migration, no schema alteration.

Natural relationship traversal. “What is the full lineage chain from source to dashboard?” is a graph traversal query that performs consistently regardless of chain length.

Semantic representation. Knowledge graphs express meaning through ontologies — formal definitions of entity types, relationships, and constraints. Two columns being “semantically equivalent” can be expressed directly as a relationship.

Standards support. W3C standards (RDF, OWL, SPARQL) enable interoperability between systems, which matters as governance extends beyond organisational boundaries.

The Trade-offs

Knowledge graphs are not universally superior.

Tooling maturity. Graph databases like Neo4j have improved but still lack the ecosystem breadth of PostgreSQL. Finding experienced graph database engineers is harder.

Query language complexity. SPARQL and Cypher have steeper learning curves than SQL. For simple queries, they’re comparable; for complex analytics, the syntax is less familiar.

Performance for simple operations. Relational databases are typically faster for straightforward CRUD operations and tabular queries.

Operational overhead. Running a graph database in production requires different expertise. Clustering, backup, and performance tuning all work differently.

Decision Framework

Choose relational if: metadata requirements are well-defined and stable; primary use cases are cataloguing and search; relationship complexity is limited; the team has strong SQL expertise.

Choose knowledge graphs if: metadata requirements are evolving; multi-hop relationship queries are important; semantic richness matters; federation across multiple metadata sources is needed.

Consider hybrid approaches if: neither requirement set dominates. Several modern data catalogue platforms use relational databases for core operations with graph databases for relationship-intensive operations like lineage traversal.

Practical Guidance

For organisations just starting with structured metadata management, a relational database is usually the right starting point. It’s simpler to implement, easier to staff, and sufficient for initial requirements. As metadata complexity grows, the limitations will become apparent, and migration to a graph-based or hybrid architecture can be planned based on concrete experience.

The worst decision is to over-engineer the initial architecture based on anticipated future needs. Start with what works today, instrument it to understand where limitations emerge, and evolve based on evidence.