A multi-model database is a type of database that supports multiple data models — such as key-value, document, graph, relational, and columnar — within a single unified backend. This means you can store and query different types of data using the most appropriate model, without needing multiple database systems.
Multi-model databases provide flexibility and efficiency by allowing developers and analysts to work with diverse datasets while maintaining consistency, security, and scalability — all in one place.
Why Multi-Model Databases Matter
Traditional databases typically follow a single data model. But today’s applications often require different models for different tasks — for example:
- User profiles in document format
- Relationships stored as a graph
- Log data handled as time-series or columnar records
- Real-time session data stored as key-value pairs
Instead of integrating and managing multiple systems, a multi-model database handles all these needs, reducing complexity and boosting performance.
Supported Models in Multi-Model Databases
- Document: JSON/BSON documents (e.g., for user data, content)
- Key-Value: Simple, fast storage of session or configuration data
- Graph: Nodes and edges for relationships and networks
- Relational: Traditional tables with SQL queries
- Columnar: High-speed analytics over large datasets
Benefits of Multi-Model Databases
- Consolidation: One platform for multiple data types
- Reduced complexity: Fewer systems to integrate and maintain
- Improved performance: Use the best model for each data access pattern
- Developer agility: Build more flexible applications without rigid schema constraints
- Cost-efficiency: Eliminate the need to license and scale multiple tools
Common Use Cases
- Omnichannel customer platforms: Combine user profiles (document), relationships (graph), and activity logs (time-series)
- IoT platforms: Handle device data in time-series and configuration in key-value pairs
- Financial apps: Use relational tables for transactions and documents for regulatory metadata
- E-commerce platforms: Manage product catalogs, customer relationships, and analytics in one system
Popular Multi-Model Databases
| Database | Description |
|---|---|
| ArangoDB | Supports document, graph, and key-value models with a unified query language (AQL) |
| OrientDB | Combines graph and document models with ACID compliance |
| MarkLogic | Enterprise multi-model database with document, graph, and relational support |
| Couchbase | Combines key-value and document storage with SQL-based querying |
| Azure Cosmos DB | Globally distributed database supporting document, graph, and key-value models |
How ClicData Works with Multi-Model Data
ClicData enables you to connect to a variety of data models — whether through native connectors or APIs — and blend them seamlessly for analysis and visualization.
- Pull data from REST APIs, SQL databases, or NoSQL stores
- Transform document or graph-based data into tabular format for dashboards
- Unify data from multiple sources into a single workspace
- Create live dashboards from multi-model datasets
With ClicData, even complex multi-model data environments can be made report-ready — helping you turn diverse datasets into actionable insights.
Multi-Model Database FAQs
When should I use a multi-model database instead of multiple specialized databases?
Use a multi-model database when your application needs to store and query different types of data — such as documents, graphs, and relational tables — but you want to avoid the complexity and cost of running multiple database systems.
How do multi-model databases handle querying across different data models?
They offer unified query languages or APIs that allow you to access and join data from multiple models in a single query, such as combining document data with graph relationships.
What are potential challenges when adopting a multi-model database?
Challenges can include increased learning curve for the query language, balancing performance across models, and ensuring that all models meet your scalability and consistency needs.
How does ClicData work with data from multi-model databases?
ClicData can connect via APIs or native connectors, transform non-tabular formats like documents or graphs into analysis-ready tables, and blend them with other data sources for unified dashboards.