Comprehensive Report: Exploring NoSQL Database Technologies and Usage

This report provides a comprehensive overview of NoSQL database technologies. It begins by defining NoSQL databases as an alternative to traditional relational databases, capable of accommodating diverse data models like documents, graphs, and key-value pairs. The report then categorizes NoSQL databases into four primary types: key-value stores, document-oriented stores, column-oriented stores, and graph databases. Each category is explained with its unique characteristics and use cases. For instance, key-value stores are highlighted for their simplicity in storing and retrieving data using key-value pairs, while document-oriented stores are discussed for their schema-less flexibility in handling semi-structured data. Column-oriented stores are presented for their efficiency in handling large datasets, and graph databases are described for their ability to model interconnected data. The report also provides examples of where each type of NoSQL database is used, offering practical insights into their applications. Finally, the report includes a list of references used in the research.

Running head: NoSQL DATABASE
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NoSQL database technologies
NoSQL can be describes as a particular approach to the designing of database which
has the ability to accommodate a wide range of data models including various documents,
graph as well as columnar formats, key values and many more. NoSQL can also be
considered as a particular alternative that could be used in traditional relational database
(Chandra, 2015). In these databases, the data is placed in numerous tables and hence the
schema of data is designed in a careful manner, this designing is done before the database of
completely built. These databases are useful for the purpose of working with huge sets of data
that are distributed in nature as well.
Four basic categories of NoSql databases and Differences between them
The four categories of NoSql along with their differences are mentioned below
Key value store: this is considered as the beginning of the NoSql. Users utilize it for
using key for the purpose of using as well as storing data. A key is an id and the value is
considered as an object encode anything the user wants (Ruiz, Morales & Molina, 2015). The
application needs to parse data after the object has been received from the key vale store.
Document oriented store: this is usually for the purpose of schema less, XML/JSON
semi-structure storage. It adds various collection concepts for the purpose of having a good
understanding of hierarchy organization of that information. The value of that particular
object is in the format of JSON.
ColumnOriented Store: for this particular format, a particular column is stored in
the form of a file. For example people store name column for the complete table in the format
of a file, gender column for the complete table as another file, age the third one (Klein,
Gorton & Ernst, 2015). This particular format saves a huge amount of money for the purpose

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of unnecessary input output for a particular query workload, hence it helps in having benefits
of cache loyalty.
Graph Database: this is comparatively a new software for the purpose of data
management, this is even more powerful that the relational model. Graph database model
data is usually interconnected through vertex types as well as edge types, a particular network
style model.
Examples of where they are used
The Key value store is used to figure out what a particular user needs for his storage
needs. It can also be used for the purpose of enhancing the overall performance in order to get
degree due to the cache mechanisms that are enabled for accompanying the mappings. The
Document Store NoSQL database can be used for the purpose of allocating values to the
stored data. It can also be used for providing structure as well as encoding of data that is
managed (Gadepally, Bolewski & Hook, 2015). Column store NoSQL database provides a
high performance, proprietary and compressed data storage. Graph base NoSQL database
helps in representing data in a graphical format. They can be used for the purpose of
representing data in the form of edges as well as nodes. This provides the user an easier
understanding of the values.

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References
Chandra, D. G. (2015). BASE analysis of NoSQL database. Future Generation Computer
Systems, 52, 13-21.
Gadepally, V., Bolewski, J., Hook, D., Hutchison, D., Miller, B., & Kepner, J. (2015, May).
Graphulo: Linear algebra graph kernels for NoSQL databases. In 2015 IEEE
International Parallel and Distributed Processing Symposium Workshop (pp. 822-
830). IEEE.
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.
Ruiz, D. S., Morales, S. F., & Molina, J. G. (2015, October). Inferring versioned schemas
from NoSQL databases and its applications. In International Conference on
Conceptual Modeling (pp. 467-480). Springer, Cham.