Database Implementation and Research Activity

This document discusses the assessment part A and B of database implementation and research activity. It covers queries related to database implementation and important features of NoSQL and cloud database. The document also includes references for further reading.

Running head: DATABASE IMPLEMENTATION AND RESEARCH ACTIVITY
Database Implementation and Research Activity
Name of the Student
Name of the University
Author’s note:

1DATABASE IMPLEMENTATION AND RESEARCH ACTIVITY
Table of Contents
Assessment Part A:..........................................................................................................................2
Assessment Part B:..........................................................................................................................4
NoSQL Database:........................................................................................................................4
Important Features of Cloud Database:.......................................................................................6
References:......................................................................................................................................9

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Assessment Part A:
Query 1: SELECT SUM(LINE_UNITS) AS 'TOTAL LINE UNITS' FROM LINE
GROUP BY INV_NUMBER;
Query 2: SELECT * FROM PRODUCT WHERE V_CODE='';
Query 3: SELECT * FROM INVOICE WHERE INV_SUBTOTAL<75 AND
INV_SUBTOTAL>25;
Query 4: SELECT * FROM INVOICE I WHERE I.INV_SUBTOTAL = (SELECT
MIN(INV.INV_SUBTOTAL) FROM INVOICE INV GROUP BY INV.INV_SUBTOTAL);

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Query 5: SELECT DISTINCT P.V_CODE, V.V_NAME FROM VENDOR V INNER
JOIN PRODUCT P ON V.V_CODE = P.V_CODE;
Query 6: SELECT V.V_CODE FROM VENDOR V EXCEPT SELECT P.V_CODE
FROM PRODUCT P;
Query 7: SELECT V.V_CODE FROM VENDOR V WHERE V.V_CODE NOT IN
(SELECT P.V_CODE FROM PRODUCT P);
Query 8: SELECT P.V_CODE, COUNT(P.V_CODE) AS 'NUMBER OF PRODUCTS'
FROM PRODUCT P WHERE V_CODE != '' GROUP BY V_CODE;

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Query 9: SELECT DISTINCT V.V_NAME, V.V_CODE, COUNT(P.P_CODE) AS
'NUMBER OF PRODUCTS' FROM VENDOR V INNER JOIN PRODUCT P ON
V.V_CODE=P.V_CODE WHERE V.V_CODE != '' GROUP BY V.V_NAME, V.V_CODE;
Query 10: SELECT P. P_DESCRIPT, I.INV_NUMBER, I.INV_DATE FROM
CUSTOMER C INNER JOIN INVOICE I ON C.CUS_CODE = I.CUS_CODE INNER JOIN
LINE L ON I.INV_NUMBER = L.INV_NUMBER INNER JOIN PRODUCT P ON L.P_CODE
= P.P_CODE WHERE C.CUS_CODE= 10011;
Assessment Part B:
NoSQL Database:
i. Distributed: The NoSQL database is capable of distributing data at a global scale.
The potential of the data sharing depends on the database management system.
The distribution of data is done among many locations where several data centers
are installed or data is stored in cloud environment for data reading and writing

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operations (Lourenco et al., 2015). The main impact this feature is that
organizations do not have to worry about the data availability. It is because
multiple copies of data is distributed where it needs be in masterclass architecture.
ii. Flexibility: This is another important feature of the NoSQL database. Like the
relational database, there is no need to insert data into the columns and tables for
accessing the database and evaluating the data. The NoSQL supports multi model
capabilities which is the reason behind NoSQL’s flexibility regarding the data
handling (Liao et al., 2016). The NoSQL has the feature of the processing
unstructured and semi-structured data very easily.
iii. Zero Downtime: The last but not the least important feature of the NoSQL is the
zero downtime for the databases. The masterless architecture is the reason that
zero downtime is possible in NoSQL. Because of this special architecture, the
database can store many copies of the same data in different nodes of the system
(Fowler, Godin & Geddy, 2016). This means that if a node goes down then the
data stored in the node can be easily retrieved from the other node(s).
iv. Easily Scalable: Achieving scalability in the relational database is a costly and
complex task. The reason behind is the conventional master-slave architecture of
relational database. Each and every node is equal in the masterless NoSQL
database and this makes the database extremely scalable. The administrative
operations are highly complex in relational database when database chunks are
stored into different servers instead of one powerful server (Gessert et al., 2017).
The masterless architecture allows accepting new huge amounts of data and
complex cloud application software easily.

6DATABASE IMPLEMENTATION AND RESEARCH ACTIVITY
v. Multi-Model: If relative knowledge-bases need data to be place into tables and
columns to be accessed and analyzed, the varied knowledge model capabilities of
NoSQL knowledge-bases build them extraordinarily versatile once it involves
handling data. They will ingest structured, semi-structured, and unstructured
knowledge with equal ease, whereas relative databases area unit extraordinarily
rigid, handling primarily structured knowledge (Mior & Salem, 2018).
Completely different knowledge models handle specific application necessities.
Developers and designers opt for a NoSQL information to additional simply
handle completely different agile application development necessities. Well-liked
knowledge models embody graph, document, wide-column, and key-value. The
perfect is to support multiple knowledge models that permits you to use constant
knowledge in numerous knowledge model varieties while not having to manage a
totally different information.
Important Features of Cloud Database:
i. Cost Effectiveness: The most important feature of cloud database is the cost
effective approach. The industries are adopting the cloud database just because
they can save a lot of capital (Amiridis et al., 2015). The host of the cloud
database charge capitals based on either resource usage or time period for which
the database is used. The cloud database is a shared hardware platform.
ii. Scalability: Because of this feature, the cloud database has been a center
attraction for the medium and big organizations also. The in-house traditional
databases uses the same amount of resources no matter what is the business
requirements. Upgrading the storage in case of in-house system is a costly

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approach. However, the cloud database is a very efficient and effective solution
for matching the business requirements (Ellison, Calinescu & Paige, 2018). The
client organization can contact host to increase or decrease the resource usage.
This way the database is always ideal for business situations.
iii. Security: Though many people seem to believe that cloud database is prone to
cyber-attack, the modern cloud database are extremely secured and also capable
of preventing the attacks like DDoS. The host of the database monitors the
security related factor all the time, more that in-house system monitoring can be
(Pancholi et al., 2016). The databases are handled by security experts based on
how crucial and sensitive data they have. If the customer’s security concern is
extremely high then host appoint third party security providers.
iv. Mobility: This feature has changed the future of the small and medium
organizations. The small organization never could imagine that they can access
their organization data from outside of office for buying cost effective service.
The cloud database host allows the organization accessing the data from different
locations using internet connection (Cao et al., 2018). The management and staff
can track and do their work from outside of office. This feature has increased the
productivity hundred times.
v. Increased Collaboration: In the event that the association has two representatives
or more, the executives ought to make collaboration a best need. All things
considered, there isn't much point to having a group on the off chance that it can't
work like a group. Cloud database makes collaboration a straightforward
procedure (Hossain & Muhammad, 2016). Colleagues can view and share data

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effectively and safely over a cloud-based stage. Some cloud-based administrations
even give collective social spaces to associate workers crosswise over association,
thusly expanding interest and commitment. Collaboration might be conceivable
without a cloud database arrangement, however it will never be as simple, nor as
successful.

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References:
Amiridis, V., Marinou, E., Tsekeri, A., Wandinger, U., Schwarz, A., Giannakaki, E., ... &
Herekakis, T. (2015). LIVAS: a 3-D multi-wavelength aerosol/cloud database based on
CALIPSO and EARLINET.
Cao, W., Liu, Z., Wang, P., Chen, S., Zhu, C., Zheng, S., ... & Ma, G. (2018). PolarFS: an ultra-
low latency and failure resilient distributed file system for shared storage cloud database.
Proceedings of the VLDB Endowment, 11(12), 1849-1862.
Ellison, M., Calinescu, R., & Paige, R. F. (2018). Evaluating cloud database migration options
using workload models. Journal of Cloud Computing, 7(1), 6.
Fowler, B., Godin, J., & Geddy, M. (2016). Teaching Case Introduction to NoSQL in a
Traditional Database Course. Journal of Information Systems Education, 27(2), 99-103.
Gessert, F., Wingerath, W., Friedrich, S., & Ritter, N. (2017). NoSQL database systems: a survey
and decision guidance. Computer Science-Research and Development, 32(3-4), 353-365.
Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted industrial internet of things (iiot)–
enabled framework for health monitoring. Computer Networks, 101, 192-202.
Liao, Y. T., Zhou, J., Lu, C. H., Chen, S. C., Hsu, C. H., Chen, W., ... & Chung, Y. C. (2016).
Data adapter for querying and transformation between SQL and NoSQL database. Future
Generation Computer Systems, 65, 111-121.
Lourenco, J. R., Cabral, B., Carreiro, P., Vieira, M., & Bernardino, J. (2015). Choosing the right
NoSQL database for the job: a quality attribute evaluation. Journal of Big Data, 2(1), 18.

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Mior, M. J., & Salem, K. (2018). Renormalization of NoSQL database schemas. In International
Conference on Conceptual Modeling (pp. 479-487). Springer, Cham.
Pancholi, V. R., & Patel, B. P. (2016). Enhancement of cloud computing security with secure
data storage using AES. International Journal for Innovative Research in Science and
Technology, 2(9), 18-21.