Categories of NoSQL Database

This article explains the four categories of NoSQL databases with examples of where they are used. The categories include key value store database, document store database, column store database, and graph base database.

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Categories of NoSQL Database
There are four categories of NoSQL databases. All the categories are explained below
stating their different with each and examples where the storages are used.
1. Key Value store database- The key value store is considered as the simplest of the data
stores where the client or the user can get the key value, assign a particular key value or delete a
value of the key from that data store (Haseeb & Pattun, 2017). In this data store, the
responsibility is on the application to know what type of value is stored. Access of primary key is
always used by the data of key value. This particular data store have great performance and can
be scaled as per the need of the v-business.
Examples where this data storage can be used are
 For storing the data of user session
 Maintaining a user profile that does not have schema
 Storing the preferences of the user
 Storing the data of a shopping cart
The databases like Redis, Riak, and MemcacheDB uses the concept of key value data
storage.
2. Document Store database- The data store of document NoSQL is same as the key
value database. This data store also stores key and a value (Chandra, 2015). The only difference
with the key value storage is that this data storage is document base and the value in this data
storage contains some structured as well as semi-structured data. This value is mainly referred as
a document and can be indifferent formats such as JSON, XML, and BSON.

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Examples where this data storage can be used are
 Platforms of E-Commerce
 Management system that deals with contents
 Blogging platforms, and
 Analytics Platforms
MongoDB, CouchDB, Elasicsearch, and Apache are the platforms that uses document
based data store.
3. Column Store database- In this data storage, data is mainly stored in a grouped cells
mainly in columns rather than storing of rows of data. The columns in this data storage are
grouped as column families (Eshtay, Sleit & Aldwairi, 2019). This database store is faster
compared to other two data storage system. The column families that are stored in this data
storage mainly include similar types of data that can be accessed together.
Examples where this data storage can be used are
 Systems maintaining counters
 Management system that deals with contents
 Blogging platforms
 Services that includes expiring usage
 Systems that mainly requires very heavy write requests.
Cassandra, and Apache Hadoop are the platforms that uses document based data store.
4. Graph base database- The model that is used for this is mainly built on the entity,
value and attribute model (Chen & Lee, 2019). The entities are mainly known as nodes that have

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many properties. This data storage is flexible and mainly describes how the data related to some
other data. The nodes helps to store data associated with each of the entity in the database, stores
the relationships that are between the nodes and a property that have simply a node that is
opposite end in a relationship.
Examples where this data storage can be used are
 Detecting frauds in an organization
 Used in search that are based on graph
 Networking as well as the operations of IT
 Social networks or different shopping cart
OrientDB, Neo4j and ArangoDB are the platforms that uses document based data store.

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References
Chandra, D. G. (2015). BASE analysis of NoSQL database. Future Generation Computer
Systems, 52, 13-21.
Chen, J. K., & Lee, W. Z. (2019). An Introduction of NoSQL Databases Based on Their
Categories and Application Industries. Algorithms, 12(5), 106.
Eshtay, M., Sleit, A., & Aldwairi, M. (2019). Implementing Bi-Temporal Properties into Various
Nosql Database Categories. International Journal of Computing, 18(1), 45-52.
Haseeb, A., & Pattun, G. (2017). A review on NoSQL: Applications and
challenges. International Journal of Advanced Research in Computer Science, 8(1).