Multi-modeling is an approach to database design that allows a database to be viewed as if multiple database types, as in a Relational Knowledge Graph.

Data models types include relational, graph-based, document-oriented, and key-value stores.

Traditional databases are usually based on a single data model, such as a relational database that uses tables, rows, and columns to store data. In contrast, multi-model databases are designed to support multiple data models, allowing developers and users to choose the best model for their specific use case.

NB FactEngine and Boston view any database as if it were a multi-model database.

Multi-model databases often provide a unified interface for data access and management, allowing users to work with multiple data models using a single query language or API. This can simplify application development and reduce the complexity of managing multiple databases.

Multi-model database vendors often argue that their databases offer several benefits, including the ability to store different types of data in a single database, improved performance and scalability, and increased flexibility and agility in data modeling. For example, a developer could use a document-oriented data model to store unstructured data such as text documents, while using a graph-based model to store relationships between data entities.

Database multi-modeling and multi-model databases provide a flexible and powerful approach to data management that can support a wide range of use cases and data models.

FactEngine's Boston conceptual modeling software is custom built to work with multi-model databases or to view your database as a Relational Knowledge Graph.