Netflix presents UDA (Unified Data Architecture), a groundbreaking infrastructure based on a knowledge graph to address the chronic fragmentation of data models across its Content Engineering ecosystem. The fundamental problem: core business concepts such as "actor" or "movie" are independently redefined in each system (GraphQL Gateway, asset management, media computing), creating duplication, terminological inconsistencies, quality issues, and limited connectivity.
Foundational architecture: RDF/SHACL knowledge graph
UDA adopts RDF and SHACL as technical foundations, but confronts major operational challenges at enterprise scale: RDF lacked a usable information model, SHACL was not designed for enterprise data with local schemas and typed keys, teams lacked shared authoring practices, and ontology tooling offered no support for collaborative modeling. Solution: a "named-graph-first" information model in which each named graph conforms to a governing model, itself a named graph within the knowledge graph.
Upper Metamodel: the model of all models
Upper constitutes the formal language for describing business domains or systems, organizing concepts into domain models: controlled vocabularies defining key entity classes, attributes, and relations. Crucially, Upper is a bootstrapping upper ontology: self-referential (it models itself), self-describing (it defines the concept of a domain model), self-validating (it conforms to its own model). Upper projects to a Java Jena-based API and a federated GraphQL schema in the Enterprise Gateway. Since all domain models are conservative extensions of Upper, seamless runtime integration guarantees consistent data semantics.
Mappings and projections: connection and automation
Mappings connect domain model elements to data container representations (GraphQL resolvers, Data Mesh sources, Iceberg tables). Everything is addressable: from the domain model down to the specific attribute, from the Iceberg table down to the individual column. Mappings enable bidirectional discovery: from the business concept to the physical system storing the data, and vice versa. Projections produce concrete containers implementing characteristics derived from the registered domain model, with automatic transpilation to GraphQL/Avro schemas preserving semantics.
Production adopters: PDM and Sphere
PDM (Primary Data Management) manages authoritative controlled vocabularies using the SKOS (W3C) model. It takes a domain model as input, automatically generates the UI, and provisions Domain Graph Services and Data Mesh pipelines via UDA projections. Consuming vocabularies are unaware of SKOS—they work with familiar domain terminology.
Sphere: a UDA-powered self-service operational reporting system. Discovery happens via business concepts ("actors", "movies"), not technical tables. The UDA knowledge graph generates SQL queries via graph traversal, eliminating manual joins and technical mediation. Aggregated metadata is presented with unified vocabulary, and data landscape boundaries and islands are identified automatically.
Transformational impact
UDA turns conceptual models into an active control plane: it not only documents concepts but generates schemas, provisions services, orchestrates data movement, and enforces consistency automatically. Future developments: Protobuf/gRPC support, materialization of instance data into the knowledge graph, and resolution of the original Graph Search challenges that inspired this work.