An Interactive Guide to Relational, NoSQL, and Vector Databases
Organizes data into tables with predefined schemas (rows and columns). Enforces strict data consistency and integrity through relationships and transactions (ACID properties).
Core Model: Structured Tables
A category of databases with flexible data models (document, key-value, graph). Designed for scalability, high availability, and handling unstructured or rapidly changing data.
Core Model: Flexible Schemas
Specialized for storing and querying high-dimensional vector embeddings. Excels at finding data based on semantic similarity rather than exact matches, crucial for AI applications.
Core Model: Vector Embeddings
Select a use case to see the recommended database type.
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| Attribute | Relational | NoSQL | Vector |
|---|---|---|---|
| Data Model | Tables with rows/columns | Document, Key-Value, Graph, etc. | High-dimensional vectors |
| Schema | Predefined and strict | Dynamic and flexible | Flexible, tied to embedding model |
| Scalability | Vertical (scale-up) | Horizontal (scale-out) | Horizontal (scale-out) |
| Query Language | SQL (Structured) | Varies (e.g., specific APIs) | APIs for similarity search (ANN) |
| Consistency | Strong (ACID) | Eventual (BASE), tunable | Eventual, focused on read speed |
| Best Workload | Transactional, structured data | Big data, unstructured content | AI/ML similarity search |