Cassandra – Types of NoSQL Databases

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Dear Readers,

There are four general types (most common categories) of NoSQL databases. Each of these categories has its own specific attributes and limitations. There is not a single solution which is better than all others; however there are some databases that are better to solve specific problems. To clarify the NoSQL databases, let’s discuss the most common categories:

Wide Row Store:

Also known as wide-column stores, these databases store data in rows and users are able to perform some query operations via column-based access. A wide-row store offers very high performance and a highly scalable architecture.

Examples: Cassandra, HBase, and Google BigTable.

Key-value stores:

These NoSQL databases are some of the least complex as all of the data consists of an indexed key and a value. Designed to handle huge amounts of data, they allow developers to store schema less data. In the key-value storage, database stores data as hash table where each key is unique and the value can be string, JSON, BLOB (basic large object) etc. and a key may be strings, hashes, lists, sets, sorted sets.

Examples: Amazon DynamoDB, Riak, and Oracle NoSQL database.

Document oriented:

Expands on the basic idea of key-value stores where “documents” are more complex, in that they contain data and each document is assigned a unique key, which is used to retrieve the document. These are designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data.

Examples: MongoDB and CouchDB.


Designed for data whose relationships are well represented as a graph structure and has elements that are interconnected; with an undetermined number of relationships between them.

A graph database is a collection of nodes and edges. Each node represents an entity (such as a student or business) and each edge represents a connection or relationship between two nodes. Every node and edge is defined by a unique identifier. Each node knows its adjacent nodes. As the number of nodes increases, the cost of a local step (or hop) remains the same.

Examples: Neo4J and TitanDB.

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