Relational Database Management System

This document provides an overview of relational database management system (RDBMS) and its queries. It also discusses the concept of NoSQL databases and their different types. The document includes sample queries and examples of key-value stores, document databases, graph stores, and wide-column stores. It is a valuable resource for students studying database management systems and NoSQL databases.

Running head: RELATIONAL DATABASE MANAGEMENT SYSTEM
Relational Database Management System
Name of the Student
Name of the University
Author’s note:

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Table of Contents
Part A:..............................................................................................................................................2
Part B:..............................................................................................................................................7
References:....................................................................................................................................10

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Part A:
Query 1: select vendor.V_CODE, V_NAME, COUNT(P_CODE) AS
NumberOfProducts
From vendor
Inner Join product
On vendor.V_CODE = product.V_CODE
Group By vendor.V_CODE;
Query 2: Select CUS_LNAME, FORMAT(SUM(INV_TOTAL), 2) as
Total_invoice_value
From customer

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inner join invoice
on customer.CUS_CODE = invoice.CUS_CODE
group by customer.CUS_CODE;
Query 3: Select CUS_LNAME, Count(INV_TOTAL) as Num_Of_Invoices
From customer
inner join invoice
on customer.CUS_CODE = invoice.CUS_CODE
group by customer.CUS_CODE;

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Query 4: Select LINE_UNITS, FORMAT(SUM(LINE_PRICE), 2) as 'Total unit price'
from line
Group by LINE_UNITS;

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Query 5: select * from invoice
where INV_SUBTOTAL = (select Min(i.INV_SUBTOTAL) from invoice i);
Query 6: select * from invoice
where INV_SUBTOTAL = (select Min(i.INV_SUBTOTAL) from invoice i);

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Query 7: select P_DESCRIPT from customer
inner join invoice
on customer.CUS_CODE = invoice.CUS_CODE
inner join line
on invoice.INV_NUMBER = line.INV_NUMBER
inner join product
on line.P_CODE = product.P_CODE
Where CUS_LNAME = 'Orlando';

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Query 8: Insert into vendor values (25591, 'Quick Deliver', 'Sarwar Tapan', 615, '432-
643', 'GA', 'Y')
Query 9: insert into customer values (11691428, 'Syed', ' Haroon Ali Uddin', 'S', 615,
'335455', 315.00)
Query 10: ALTER TABLE vendor
ADD COLUMN Customer_rating VARCHAR(14) AFTER V_ORDER;
Query 11: Update vendor Set Customer_rating = 'GREAT' WHERE CUS_CODE
=11691428;

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Part B:
The NoSQL is referred to the approach of database design which can integrate a wide
range data models such as key-value, document, graph and columnar formats. The NoSQL is
abbreviation of ‘not only SQL’ (Chang et al., 2016). The NoSQL database approach can be
considered as the alternative of relational database management system or RDBMS. The
RDBMS was based on creating tables and joining those tables (Alfian, Syafrudin & Rhee, 2017).
The design has very less importance in the NoSQL approach. The NoSQL databases are
particularly beneficial when the amount of data to be managed is huge.
Key-Value Stores: The key-value database or key-value store is used for implementing a
simple model of data which combines a value with an associated unique key. The database
developed using the key-value approach is known as the key-value databases (Aniceto et al.,
2015). The key-value databases are highly process efficient, optimized and extremely scalable in
terms of session handling and caching in website based applications. The implementations of
key-value pair database will differ based on the working behaviour of the system with the RAM,

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disk drives or solid state drives (Hospital, 2015). Berkeley DB, Aerospike, Redis,
MemchacheDB and Riak are the typical example of key-value database management systems.
Document Database: The document databases are also known as the document stores.
This type of NoSQL record semi-structured descriptions of data along with the actual data within
a document format. The document database is extremely flexible in terms of permitting the
developers creating and updating the programs with no need of referencing the master schema
(Guimaraes et al., 015). The document database has become more popular with the usage of
JavaScript Object Notation and JavaScript. This is a data alternating format which consists of
obtained wide currency between the website application developer communities. The mobile
application data management and content management are the most popular usages of the
document based data (Holemans, Kasprzyk & Donnay, 2018). The examples of the document
database are DocumentDB, CouchDB, MarkLogic, ouchbase Server and MongoDB.
Graph Stores: This NoSQL database manage the data as nodes. The nodes can be
referred as the relational database records and edges. The edges defines the connection between
various nodes (Klein et al., 2015). As the graph NoSQL database record the association between
the nodes, it is capable of supporting richer representations of relationship among data. The
relational databases are limited to schemas, on the other hand, NoSQL graph store can evolve
over time and loose its dependability on schemas (Reniers 2017). The applications which have to
map relationships are most suitable for graph database. Examples of graph databases include
Neo4j, IBM Graph, AllegroGraph and Titan.
Wide-Column Stores: The Wide-column database records the tables of organized data as
columns rather than the rows. Both the SQL and NoSQL database approaches uses the wide-
column database approach. Very large amount of data is efficiently and effectively handled by

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the Wide-column database. The application of this wide-column database can be seen in
recommendation engines, fraud detection, catalogs and various other kinds of data processing.

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References:
Alfian, G., Syafrudin, M., & Rhee, J. (2017). Real-time monitoring system using smartphone-
based sensors and NoSQL database for perishable supply chain. Sustainability, 9(11),
2073.
Aniceto, R., Xavier, R., Guimarães, V., Hondo, F., Holanda, M., Walter, M. E., & Lifschitz, S.
(2015). Evaluating the Cassandra NoSQL database approach for genomic data
persistency. International journal of genomics, 2015.
Chang, B. R., Tsai, H. F., & Hsu, H. T. (2016). Secondary index to Big Data NoSQL Database—
Incorporating solr to HBase approach. Journal of Information Hiding and Multimedia
Signal Processing, 7(1), 80-89.
Guimaraes, V., Hondo, F., Almeida, R., Vera, H., Holanda, M., Araujo, A., ... & Lifschitz, S.
(2015, November). A study of genomic data provenance in NoSQL document-oriented
database systems. In 2015 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM) (pp. 1525-1531). IEEE.
Holemans, A., Kasprzyk, J. P., & Donnay, J. P. (2018). Coupling an Unstructured NoSQL
Database with a Geographic Information System. In GEOProcessing 2018. The Tenth
International Conference on Advanced Geographic Information (pp. 23-28).
Hospital, A., Andrio, P., Cugnasco, C., Codo, L., Becerra, Y., Dans, P. D., ... & Gelpí, J. L.
(2015). BIGNASim: a NoSQL database structure and analysis portal for nucleic acids
simulation data. Nucleic acids research, 44(D1), D272-D278.

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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.
Reniers, V., Rafique, A., Van Landuyt, D., & Joosen, W. (2017). Object-NoSQL Database
Mappers: a benchmark study on the performance overhead. Journal of Internet Services
and Applications, 8(1), 1.