| Types | Key Value Store | Column Store | Document Store | Graph Database |
| Performance | High | High | High | Variable |
| Scalability | High | High | Variable (High) | Variable |
| Flexibility | High | Moderate | High | High |
| Complexity | None | Low | Low | High |
| Read | Consistent Read | Read rare | Read Intensive | More Read Intensive |
| Write | Consistent Write | Write Many | Not write Intensive | Less Write Intensive |
| Others | Caching
User Session Caching Contents |
IOT Quick stream OS |
Handle lots of variety of data | Data
Type may relate each other
Vertical Scaleout Horizontal Scaleout |
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
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