Detailed Analysis of Basic Categories of NoSQL Databases

This report provides an overview of the basic categories of NoSQL databases, which have become a preferred choice over relational databases due to their flexibility, performance, and scalability. It explores four primary types: key-value stores, document databases, wide-column stores, and graph databases. Key-value stores, the simplest type, store data as key-value pairs, optimized by a cache mechanism. Wide-column stores organize data in columns, offering efficient data access. Document databases pair keys with complex data structures like JSON, while graph databases use nodes and edges to represent network information. The report highlights examples of each database type and references relevant research.

Running head: BASIC CATEGORIES OF NoSQL DATABASES
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Basic Categories of NoSQL Databases
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Basic Categories of NoSQL Databases
In the contemporary world, technology is growing rapidly, and business organizations are
using technology to create and maintain a competitive advantage over their competitors. The
technique adopted in any organization should solve the problem as well as be agile and flexible
to the development practices supporting them. The flexibility and agility of the NoSQL databases
have made them become a preference compared to the old rational databases, (Klein, Gorton,
Ernst, and Donohoe, 2015). Apart from flexibility, the NoSQL databases also excellent
performance, scale, and compatibility with modern applications. NoSQL databases do not have a
relational data model. There are four primary NoSQL databases, namely graph databases,
document databases, wide-column stores, and the Key-value stores
Key-value stores are the simplest types of NoSQL databases. The databases store an item
as an attribute name (key) with its respective value. The key can be auto-generated or synthetic,
while the value can be BLOB, JSON, and String. This type of NoSQL database uses a hash table
that a unique pointer to a particular data item and a unique key. A logical group of keys is known
as a bucket, although it does not group the data physically. Both the key and bucket are essential
in reading a value since the real key is a hash, which is a combination of key and bucket,
(Moniruzzaman and Hossain, 2013). The cache mechanism dramatically enhances the
performance of this type of databases. The most famous examples of this type of databases are
Amazon's Dynamo and Riak.
The wide-column stores' store date in cells clustered in columns instead of data rows.
The columns are then logically grouped into column families which contain an unlimited number
of columns created either at schema definition or runtime. Most rational databases store data in
rows, but this is not the case with the wide-column stores NoSQL databases, (Nayak, Poriya, and

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Poojary, 2013). Storing data in columns comes with the advantage of easy and faster search and
access of data. This database can be used to store extensive data that need quick access. The
most famous example of wide-column stores is HBase and Cassandra databases.
Document databases store data such that keys are paired to a complex data structure
referred to as a document. A document may contain several different key-array pairs, key-value
pairs as well as nested documents. The databases are closely related to the key-value store, but
here, the values are referred to as documents, and they provide specific structure and encoding of
the data. The standard encodings used for this database include JSON, BSON, and XML. An
example of this database is Apache CouchDB, which stores data using JSON and Javascript its
query language and uses HTTP and MapReduce for an API.
Graph databases are used for storing network information such as social connections.
The database uses a flexible graphical representation which addresses the scalability
apprehensions perfectly. In this type of databases, nodes and ages are used to store and represent
data. The nodes are organized based on the relationship with one another, and edges are used to
describe the relationships. The links and nodes are both assigned to some defined properties.
The HyperGraphDB and Neo4J are examples of this type of database.

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References
Klein, J., Gorton, I., Ernst, N., Donohoe, P., Pham, K., & Matser, C. (2015, February).
Performance evaluation of NoSQL databases: a case study. In Proceedings of the 1st
Workshop on Performance Analysis of Big Data Systems (pp. 5-10). ACM.
Moniruzzaman, A. B. M., & Hossain, S. A. (2013). Nosql database: New era of databases for big
data analytics-classification, characteristics and comparison. arXiv preprint
arXiv:1307.0191.
Nayak, A., Poriya, A., & Poojary, D. (2013). Type of NOSQL databases and its comparison with
relational databases. International Journal of Applied Information Systems, 5(4), 16-19.