4 Types of NoSQL Databases



NoSQL databases (aka "not only SQL") are non-tabular, and store data differently than relational tables. NoSQL databases come in a variety of types based on their data model. The main types are document, key-value, wide-column, and graph. They provide flexible schemas and scale easily with large amounts of data and high user loads.

4 major types of NoSQL databases emerged:

Document databases 
  • Store data in documents similar to JSON (JavaScript Object Notation) objects. 
  • Each document contains pairs of fields and values. 
  • The values can typically be a variety of types including things like strings, numbers, booleans, arrays, or objects
  • Their structures typically align with objects developers are working within code. 
  • A powerful query languages
  • Horizontally scale-out to accommodate large data volumes. 
  • MongoDB is consistently ranked as the world’s most popular NoSQL database according to DB-engines and is an example of a document database.

Key-value databases
  • A simpler type of database where each item contains keys and values. 
  • A value can typically only be retrieved by referencing its value.
  • Key-value databases are great for use cases where you need to store large amounts of data but you don’t need to perform complex queries to retrieve it. 
  • Common use cases include storing user preferences or caching. 
  • Redis and DynanoDB are popular key-value databases.

Wide-column stores
  • Store data in tables, rows, and dynamic columns. 
  • Wide-column stores provide a lot of flexibility over relational databases because each row is not required to have the same columns. 
  • Many consider wide-column stores to be two-dimensional key-value databases. 
  • Wide-column stores are great for when you need to store large amounts of data and you can predict what your query patterns will be. 
  • Wide-column stores are commonly used for storing Internet of Things data and user profile data. 
  • Cassandra and HBase are two of the most popular wide-column stores.

Graph databases
  • Store data in nodes and edges. 
  • Nodes typically store information about people, places, and things while edges store information about the relationships between the nodes. 
  • Graph databases excel in use cases where you need to traverse relationships to look for patterns such as social networks, fraud detection, and recommendation engines. 
  • Neo4j and JanusGraph are examples of graph databases.
  • Neo4j is native graph DB which store data in graph format and fully support ACID.

Multi-Models Databases
  • A database that supports multiple data models.
  • Enable users to write in any data model, then read with the same or another data model that it supported.
  • Provides benefits in analytics performance and convenience to users. 
  • Typical multi-model database stores data as Key-Value format and provides efficient read capability in other models.
  • ArangoDB is an example of a Multi-Models Database that provides simple GUI to users.

References:



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