- Karan Pratap Singh
NoSQL is a broad category that includes any database that doesn't use SQL as its primary data access language. These types of databases are also sometimes referred to as non-relational databases. Unlike in relational databases, data in a NoSQL database doesn't have to conform to a pre-defined schema. NoSQL databases follow BASE consistency model.
Below are different types of NoSQL databases:
A document database (also known as a document-oriented database or a document store) is a database that stores information in documents. They are general-purpose databases that serve a variety of use cases for both transactional and analytical applications.
- Intuitive and flexible
- Easy horizontal scaling
One of the simplest types of NoSQL databases, key-value databases save data as a group of key-value pairs made up of two data items each. They're also sometimes referred to as a key-value store.
- Simple and performant
- Highly scalable for high volumes of traffic
- Session management
- Optimized lookups
- Basic CRUD
- Values can't be filtered
- Lacks indexing and scanning capabilities
- Not optimized for complex queries
A graph database is a NoSQL database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data instead of tables or documents.
The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation.
- Query speed
- Agile and flexible
- Explicit data representation
- No standardized query language
- Fraud detection
- Recommendation engines
- Social networks
- Network mapping
A time-series database is a database optimized for time-stamped, or time series, data.
- Fast insertion and retrieval
- Efficient data storage
- IoT data
- Metrics analysis
- Application monitoring
- Understand financial trends
Wide column databases, also known as wide column stores, are schema-agnostic. Data is stored in column families, rather than in rows and columns.
- Highly scalable, can handle petabytes of data
- Ideal for real-time big data applications
- Increased write time
- Business analytics
- Attribute-based data storage
Multi-model databases combine different database models (i.e. relational, graph, key-value, document, etc.) into a single, integrated backend. This means they can accommodate various data types, indexes, queries, and store data in more than one model.
- Suitable for complex projects
- Data consistent
- Less mature