This article explains how to design a scalable multimodel database solution using Amazon Neptune, combining multiple database technologies for different data types.

• Multimodel (polyglot persistence) uses different databases for different data types to optimize performance
• Trade-off: better results but increased complexity requiring multiple skillsets and integrations
• Movie solution example uses DocumentDB, ElastiCache, OpenSearch, S3 data lake, DynamoDB, and Neptune
• Use data mesh and knowledge graph concepts to divide solution into documented data products
• Model data products using UML class diagrams with custom stereotypes for multimodeling
• Convert UML models to RDF ontologies for loading into Neptune knowledge graph
• Query knowledge graph with SPARQL to understand data product relationships and dependencies
• Neptune supports federation to external sources like DBPedia and Wikidata

The approach simplifies multimodel design by treating databases as interconnected data products, using a knowledge graph to manage relationships and enable product discovery.