Information Technology - Database

This report discusses different topics related to data warehousing, data mining, Big Data technologies, NoSQL databases, and the impact of the Open Data movement. It also covers the differences between operational and strategic data sets and compares OLAP with OLTP.

Running head: INFORMATION TECHNOLOGY - DATABASE
Information Technology – Database
Name of the Student
Name of the University
Author’s note

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Table of Contents
1. Introduction..................................................................................................................................2
2. Discussion....................................................................................................................................2
2.1 Data Warehousing and Differences between Operational and Strategic Data Sets...............2
2.2 Data Mining and Comparison of OLAP with OLTP.............................................................5
2.3 Rise of ‘Big Data’ Technologies and Applications...............................................................8
2.4 ‘NoSQL’ Databases in Comparison with ‘ACID-Compliant’ Databases...........................10
2.5 Impact of ‘Open Data’ Movement.......................................................................................10
References......................................................................................................................................13

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1. Introduction
There is a growing need for organizations to integrate different kinds of changes within
the database into the process of development. This report helps in the discussing of different
types of topic such as Data Warehousing, Data Mining and many others. With the deployment of
database applications, there are different forms of security and legal concerns that would need to
be considered.
2. Discussion
2.1 Data Warehousing and Differences between Operational and Strategic Data Sets
Data Warehousing – This is defined as a process based on construction and using the
data warehouse. This form of warehouse is mainly constructed based on the integration of data
based on multiple heterogeneous sources, which supports ad hoc queries, analytical reporting and
making of decisions (Cuzzocrea, Bellatreche and Song 2013). The process of data warehousing
would involve the cleaning of data, integration of data and consolidations of data.
A data warehouse could be considered as a federated repository of data that would be
collected within the various operational systems of the enterprise. These type of data might be
logical or physical. The data warehousing would majorly emphasize on the capturing of data
based on diverse sources based on access and analysis rather than transaction processing
(Cuzzocrea 2013). The platforms based on data warehouses would be different from operational
databases because they would be able to store historical information and thus make the processes
easier for business leaders for analysing data within a specific period of time.

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(Fig 1: Benefits of a Data Warehouse)
(Source: Cuzzocrea 2013, pp. 482)
Differences between Operational and Strategic Data Sets
Remote data is considered as a strategic data that would include information based on
economy, political data, social statistics, ecology and technological advances. The strategic
manager make use of strategic data by collecting information related to industry in order to
create different kinds of standards (Chen et al. 2015). Other kinds of data based on economics
should be able to include the data based on the state of economy that includes recession, boom,
depression and different trends based on economy. Political data would include different kinds of
legal and regulatory information that includes employment and taxation laws.

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(Fig 2: Design of a Data Warehouse or Architectural Design)
(Source: Bellatreche, Khouri and Berkani 2013, pp. 79)
On the other hand, information based on different competitors such as statistics related to
the labour of a company, information regarding suppliers, projection based on needed sources
and accounting data could be included under operational data (Cuzzocrea, Bellatreche and Song
2013). The data collected from different kinds of sources would be able to help marketers to
create products that would seem to be superior based on competing with other kinds of products
(Bellatreche, Khouri and Berkani 2013). The data based on different customers would be helpful
for marketers based on the creation of consumer profiles that would be helpful for the creation of
products, distribution and advertising them.

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2.2 Data Mining and Comparison of OLAP with OLTP
Data Mining – This could be defined as a process based on discovering different forms
of patterns based within larger sets of data that would involve different kinds of methods based
on the intersection of machine learning, database systems and statistics. With the help of this
process, the sorting of data sets based on the identification of patterns and establishing of
relationships would be helpful (Wu et al. 2014). This would majorly help for the solving of
problems with a high level of data analytics trends and techniques. Different kinds of tools based
on data mining would be helpful for predicting the trends in the future.
(Fig 3: The Processes of Data Mining)
(Source: D’Oca and Hong 2015, pp. 398)
Aside from the steps based on raw analysis, the process of data mining would be helpful
for the involvement of databases and aspects based on data management. This would also

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include data processing, inference and model considerations, complexity considerations and
post-processing based on discovered structures. The techniques based on data mining would
mainly been use in different kinds of areas of research that would include cybernetics, genetics,
mathematics and marketing (D’Oca and Hong 2015). With the proper form of use of data mining
techniques, it would be used for the prediction of customer behaviour and driving of efficiencies.
(Fig 4: Steps of Data Mining and Knowledge Discovery)
(Source: D’Oca and Hong 2015, pp. 400)
Comparison of OLAP and OLTP
Basis of
Comparison
OLAP OLTP
Basics It could be defined as a data analysis
system and online data retrieving
system.
It is defined as an online
transactional systems that would be
able to manage the modification of

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different databases (Psaroudakis et
al. 2014).
Data The different OLTP databases would
become the primary source of data
based on OLAP.
OLTP and the different
transactions would become the
original sources of data (Wu et al.
2014).
Transaction OLAP has long forms of transactions. OLTP has short kind of
transactions.
Time The time taken for processing of
different kinds of transactions would be
comparatively more within OLAP
(Dehne et al. 2013).
The processing time of different
kinds of transactions within OLTP
would be less as compared to
OLAP.
Queries OLAP has complex kind of queries. OLTP has simple kind of queries.
Integrity The database based on OLAP does not
gets modified on a frequent basis.
Hence, the integrity of data is not
affected.
The database based on OLTP
should be able to maintain the
constraint based on data integrity
(Difallah et al. 2013).
Normalizatio
n
The tables within OLAP would not be
normalized.
The tables within the databases of
OLTP would be normalized based
on 3NF.

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2.3 Rise of ‘Big Data’ Technologies and Applications
The primary concept of Big Data was mainly created for the purpose of facing the
constant increase in the amount of data that is created. In the recent times, there has been a huge
proliferation in the volumetrics of data. This huge form of data would be necessary for finding
the solutions based on storage and analysis. The use of Big Data technologies help in addressing
the issues of 3V: data variety, velocity and data volumetry (Hashem et al. 2015). These 3V’s
would be helpful would help in concerning collection of data, analysis and storage. The impact
of Big Data would be helpful for enabling the responding to several issues such as predictive
analysis, better stock management and predictive sales.
(Fig 5: Rate of Big Data rise from 2017-2022)
(Source: Hashem et al. 2015, pp. 101)

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Some of the latest forms of Big Data technologies and applications are:
 Apache Hadoop – This is one of the most important form of technology and a popular
framework that is being widely used for dealing with big volumes of data (O’Driscoll,
Daugelaite and Sleator 2013). One of the most widely used case of Hadoop is Data Lake.
 Batch Processing – This kind of technology would help in the processing of data till the
time there would not be any more systems of data entering. The incremental and
continuous treatments would be helpful for the architecture to take note of new entry of
data without the processing of the previous entry of data. In this method, the desired
results appear after the end of the entire processing of data. Some examples of batch
processing are Apache Spark and MapReduce.
 Streaming Processing – This form of treatment is opposite than batch processing. With
the help of this method, the desired results would be accessible before the end of
processing would be done (Hu et al. 2014). The technology of stream processing is
considered as an easy solution that would be able to improve the time of processing.
 NoSQL Database – As compared to the relational databases, the NoSQL databases
would be able to provide a new approach to storage of data, flexible and adapt to
different evolutions and also would be less sensitive to failures.
 Cloud Computing – This technology is described as an innovative way of deployment of
Big Data technologies that would be demanding huge processing capacities and huge
storage systems (Pokorny 2013). Cloud computing technologies are a powerful and less
expensive approach.

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2.4 ‘NoSQL’ Databases in Comparison with ‘ACID-Compliant’ Databases
The NoSQL databases would be able to encircle a wider range of databse technologies
that would be designed for catering to the demand of modern form of applications. The NoSQL
systems are easy for the process of deployment and thus be able to store a wider range of data
types (Nayak, Poriya and Poojary 2013). The NoSQL systems mostly excel in performance until
there would be a need of consistency within the data.
The NoSQL databases would primarily emphasize on the performance of work as
compared to data integrity. Most of the NoSQL databases would compromise on the ACID
compliance based on performance (Kaur and Rani 2013). Hence, most of the organizations
would make use of NoSQL for different data types that would not be impacted based on
consistency.
The SQL databases would be default to ACID compliance though most of them would be
able to offer options based on favouring performance over the integrity of data based on some
kind of options.
2.5 Impact of ‘Open Data’ Movement
‘Open Data’ movement could be defined as the kind of idea that different forms of data
would be freely available for everyone to make use of and again republish them as per their
needs. The republishing of data would be without any form of patents, copyright or any other
mechanisms based on control. Different kinds of tools for accessing and interpreting them would
be able to lead them towards innovations (Attard et al. 2015). Open Data is made free and
available publicly that would be accessible by everyone and would be easy to use.

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(Fig 6: User Views on Open Data Movement)
(Source: Attard et al. 2015, pp. 412)
The importance of data could be viewed as a public utility. This form of open data would
be leveraged by several enterprises, individuals that would also include commercial enterprises.
The subsequent aim of Open Data movement would be make data that is made using public
resources to be accessible for the use of public and made free of cost.
Open Data is considered as a global movement. There are different countries that have
adopted the International Open Data Charter, which intends to make the data of the government
to be presented in an open digital format (Hashem et al. 2015). Open Data is considered to be a
major form of global resource that would be helpful for spurring the economic growth based on
launching new businesses, optimizing the operations of existing companies. Creation of jobs and
thus be able to improve the climate based on foreign investment.

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Free form of available data from the U.S Government could be considered as an
important national resource. This would serve as a fuel based on innovation, entrepreneurship,
different forms of public benefits and scientific discovery. Based on a recent report, the use of
Open Data would be able to generate more than $3 trillion a year based on different forms of
additional value in various key sector of global economy such as transportation, electricity,
education and healthcare. The launch of different sets of Open Data Round Table meeting with
various government agencies and entrepreneurs have been proved to be helpful based on
connecting with business leaders and thus also make use of open data (Jetzek, Avital and Bjørn-
Andersen 2014). They would also be able to make use of different ways in which the data would
be openly available with different government officials who are in the process of work in order
to make the data easy to find and thus maximize their value for public use. The new kind of
initiative taken by the Open Data institute of United States would be able to create and thus be
able to implement the open source software and standards based on open government data. This
form of data would be in relation with fishing and hunting that would be aimed at streamlining
modernizing the industry.

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References
Attard, J., Orlandi, F., Scerri, S. and Auer, S., 2015. A systematic review of open government
data initiatives. Government Information Quarterly, 32(4), pp.399-418.
Bellatreche, L., Khouri, S. and Berkani, N., 2013, April. Semantic data warehouse design: From
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Applications (pp. 64-83). Springer, Berlin, Heidelberg.
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M. and
Zhang, W., 2015. Global land cover mapping at 30 m resolution: A POK-based operational
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Cuzzocrea, A., 2018. Effectively and Efficiently Supporting Encrypted OLAP Queries over Big
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Dehne, F.K.H.A., Kong, Q., Rau-Chaplin, A., Zaboli, H. and Zhou, R., 2013, October. A
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Difallah, D.E., Pavlo, A., Curino, C. and Cudre-Mauroux, P., 2013. Oltp-bench: An extensible
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