Data Analytics and Business Analytics: ETL for Data Warehouse Construction

This article discusses the ETL process for construction of data warehouse in support of business intelligence. It covers extraction, transformation and loading of data, and explores the tools provided by SSIS. The article also highlights the benefits of data warehousing and business intelligence, and the potential problems that may arise. Subject: Data Analytics, Course Code: N/A, Course Name: N/A, College/University: N/A

Running head: DATA ANALYTICS AND BUSINESS ANALYTICS
DATA ANALYTICS AND BUSINESS ANALYTICS
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1DATA ANALYTICS AND BUSINESS ANALYTICS
Table of Contents
Introduction..........................................................................................................................2
Discussion............................................................................................................................3
Operation of Data Warehouse in support of Business Intelligence.................................3
ETL for construction of Data Warehouse........................................................................7
Extraction of Data........................................................................................................8
Transformation of Data................................................................................................8
Loading Data...............................................................................................................9
Tools provided by SSIS.............................................................................................12
Conclusion.........................................................................................................................15
References..........................................................................................................................17

2DATA ANALYTICS AND BUSINESS ANALYTICS
Introduction
Data warehousing is a well-known technique which is used for collecting and proper
management of data. It is done with the help of sources to provide some meaningful business
(Gill and Singh, 2015). It is considered to be part of a technological component that allows the
use of data. It is defined as the storage for a huge amount of information by business. The
method of changing data into available information makes it available to a large number of users.
ETL stands for Extract, transform and load. The data staging layer mainly hosts the ETL process
for extract, integrate and cleaning of data from different operational sources which is used for
feeding the data warehouse layer (Dembczynski , 2015). In this particular layer, ETL process
provides feedback to the recoiled data layer in terms of single and top quality of sources which
are provided. ETL process as a whole can be easily stated as reconciliation. ETL takes place at
the time when the data warehouse comes up with population. After that, it checks or analyzes the
fact that the data warehouse is updated on a regular basis or interval. ETL mainly comprises of
three phases that are extraction, transformation and lastly loading.
In the coming pages of the report, a research has been done regarding the concepts and
technology of ETL data. After that, a description has been provided regarding the operation of a
data warehouse which is used for support of Business Intelligence. ETL is considered to be a
well-known process for construction of Data warehouse solution. It mainly comprises of three
important phases like extraction, transformation and lastly loading of data. Microsoft SSIS
comes up with a large number of tools like data flow engine, scripting environment and lastly
Data Profiler.

3DATA ANALYTICS AND BUSINESS ANALYTICS
Discussion
Operation of Data Warehouse in support of Business Intelligence
The main purpose which is related to the business intelligence in the sector of business is
to incorporate corporate executive, manager of business and other workers related to operational
to make better and more decision of business in an informed manner (Gill and Singh, 2014). It
can be stated here that most of the organization tend to include the business intelligence concept
to cut down the cost, identify business opportunity which does not pertain in the industry and
directly spot inefficient process of business which may be hampering the overall working of the
organization.
The business intelligence can be referred to a technology, tool, application and practice
which can be used to integrate, collect, present and analyze the raw data of the organization
which would be useful in the aspect of creating an actionable and insight of the information of
the business (Leitch et al. 2016). The process of business intelligence can be considered as a
discipline as well as a process of technology-driven mechanism which is made up of several
activities which are interrelated.
 Data mining
 An analytical process which is done online
 Reporting
 Querying.
Benefits of warehouse
The above discussion majorly gives a clear idea of the concept of business intelligence.
The potential benefit which is related to the concept of business intelligence are stated below:

4DATA ANALYTICS AND BUSINESS ANALYTICS
 Optimization of the processes of internal business
 Driving revenue which is new
 Improving and accelerating the aspect of decision making.
 Operational efficiency is increased.
 Gaining competitive advantage in the field of operation over business rivals.
 Identification of the trend of business which is currently being introduced.
Implementation details
The tool of business intelligence can be considered to be essential relating to the data-
driven aspect of the decision support system (DSS). The concept of business intelligence is
sometimes considered interchangeable with the report, briefing books, query tools and
information system which is executive (Ferdynus and Huiart, 2016). These tools can be utilized
in a different manner according to the need of the organization, for example, the employee of the
business can directly use the tool to analyze the data themselves rather than waiting for the
information technology to run complex reports on the data. The access of the information helps
the user to back up the aspect of a business decision with a complex and hard number rather than
only gut anecdotes and feelings.
Software tools in the optimization of business intelligence.
The business intelligence software system can directly provide current, historical and
predictive view relating to the operation of the business. These aspects which are taken into
consideration are done using the data which has been gathered from the data warehouse or a data
mart and working occasionally from the data which are operational (Andersen, Thomsen and
Torp, 2018). The software elements in this context can directly support reporting, interactive

5DATA ANALYTICS AND BUSINESS ANALYTICS
slice and slice analyses, statistical mining of data and visualization. The application directly
tackles financial, production, sales and many other sources which are related to business data for
the purpose which directly include management of business performance (Kholod, Efimova and
Kulikov, 2016). The concept of benchmarking can be implemented in a concept which usually
gets the data of other organization which are in the same industry.
Example of business intelligence software solution
Some of the example which is related to the business intelligence software are stated
below:
 Sisense
 Looker
 Demo
 Cyfe
 Kissmatrices
 Visualr
 Indicative
 StarBI
 Centro
 ReportPlus
 Hevo data
Potential business intelligence problem
User resistance: The concept of implementation can be done using the aspect of dogged
relating to cultural challenges.

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Poor quality and irrelevant data: This aspect can be included in a manner of gaining the
appropriate standard of the data. Getting the idea of the data in an order which is working well
before the action and extracting on the insight (Mukherjee and Kar, 2017). The quality of the
data which is generated can be considered to one of the most important factors in the concept
which directly reflects the internal working of the organization and how the working framework
of the organization would be implemented. In most of the cases, security of the data can be one
of the most important factors.
Tools of BI: The core concept which is related to the BI can be considered to be reporting
not only in the aspect of management of the process. It can be stated here not to get confused
with the aspect of business intelligence with the concept of business analytics (Gill and Singh,
2014). There can be different tools which can be implemented in the concept and it directly
depends upon the need of the organization and the effect it would be imposing on the internal as
well as the external working of the organization.
The organization does not understand their business operations: The concept can be
related to the understanding aspect which is related to all the activity which makes up a particular
business process before starting the aspect of business intelligence project.
Latest trends in the business intelligence concept relating to warehouse
This section majorly deals with all the aspects which are related to the concept and the
latest trend in the business intelligence in the sector:
 Integration of content and data: Currently most of the organization are starting to see that
the content and the data should not be considered as a separate aspect relating to the
management of the information (Gill and Singh, 2014). On the other hand, apart from

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this, it can be stated that the concept must be integrated with the enterprise approach. The
management of enterprise information can directly bring business intelligence and
content enterprise management concept together.
 Business intelligence operation: currently it can be stated that most of the organization
are moving towards the concept of operational business intelligence which is currently
being uncontested and served by the vendors (Mukherjee and Kar, 2017). Traditionally it
can be stated that business intelligent vendors are directly targeting only the topmost
pyramid but it the paradigm is moving the focus towards the business intelligence to the
bottom of the pyramid with a focus of self-service business intelligence (SSBI)
 Conversational and embedded business intelligence: the concept in recent times cannot
be considered as an ordinary report which is related to the BI software but it can be
directly be integrated with the concept of advanced integrated analysis and reporting
(Rahman, Riyadi and Prasetyo, 2015).
ETL for construction of Data Warehouse
ETL is considered to be a complex process which consumes or takes up a part of Data
Warehouse development efforts. Apart from this, it requires skills and knowledge of business
analyst and application developer (Narra, Sahama and Stapleton, 2015). It is not considered to be
a one-time event instead of that it is a periodic event which is available on the monthly and daily
basis. The operation of ETL should be carried out on the relational database server and data
warehouse database. It builds up a logical and physical separation between source from system
and Data Warehouse. Along with this, it minimizes the effect of intense ETL activity which is
present on the source and databases of the warehouse. In the extraction phase, the required data
is extracted from various sources. The integration of data warehouse across the various

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enterprises is considered to be a bit challenging to get the warehouses to the next state which is
very much usable (Vaisman and Zimányi, 2014). Generally, data is extracted from various
heterogeneous sources. Each of the data sources is extracted comes up with distinct sources
which need to be managed. Along with it needs to integrate into the ETL system so that data can
be extracted in an efficient way. ETL process requires a proper system for which is different for
DBMS, operating system and lastly communication protocol. The logical data map aims in
providing regarding the start point and end point.
Extraction of Data
Relevant and useful data is extracted from various sources in this phase. An individual
makes use of static extraction at the data warehouse requires population for the time. This can be
considered like a snapshot of various operational data. Incremental extraction is used for
updating data warehouse on regular basis (White, 2018). Sizes are a large number of changes
which are given to source data for extraction. Incremental extraction is applied to update data
warehouse the log which is maintained by database DBMS. Generally, a timestamp is given with
an operational database which is used for recording data, when it is changed or added then it can
be used for streamlining the process of extraction. Extraction is considered to be source driven if
an individual writes it to the operational databases. The extracted data is generally selected on
the basis of the quality which is provided (Kimball et al. 2015). This particular thing depends on
the fact how perfectly the constraints are applied
Transformation of Data
Transformation is the main part of the total reconciliation phase. It mainly converts the
operational format into the required format of the data warehouse (Talib et al. 2016). If a three-
layer architecture is implemented, then the output phase is provided to the reconciled layer of

9DATA ANALYTICS AND BUSINESS ANALYTICS
data. In the presence of reconciled data layer, a proper establishment is required for mapping the
data between the source layer and warehouse layer. In this particular case, there can be an
instance of a complex integration phase is needed for designing the data warehouse (Khan and
Hoque, 2015). A list of parameters should be taken into account like lose text can easily hide
valuable information and different formats are needed for individual data which is provided. A
list of this should be taken into account for transforming the recoiled data layer.
 Conversion and normalization which tends to operate on both of the given formats
of storage and proper units are required for measuring the each of the provided
data.
 Matching which is associated with equivalent fields which works from various
phases.
 Selection is associated which a large number of source fields and records for it.
At the time of populating of data warehouse, normalization can be changed by
denormalization as data warehouse are sometimes denormalized and provides aggregation to
provide a sum to easily upgrade. The transformation process is almost connected to the ETL
tools.
Loading Data
Loading in the data warehouse is considered to be the last step. It is can be carried by
two methods like refresh and update.
Refresh: Data warehouse is completely being rewritten. It merely focuses on the fact that
older data is being replaced (Astriani and Trisminingsih, 2016). Refresh is achieved by a

10DATA ANALYTICS AND BUSINESS ANALYTICS
combination by the static extraction which aims in initiating the initial population of the data
warehouse.
Update: The changes are given to source data which is added to the data warehouse.
Different updates are carried out without any kind of detection or modification of existing data.
Fig 1: Example of the ETL process
(Source: Chevalier et al. 2015)
ETL tool plays an important role in the processing of data in the warehouse. They come
up with flexible functional which is used for transforming, cleaning and providing assurance to
data quality (Mireku Kwakye, 2017). The functionalities are mainly designed a useful form of
data for the various end-user application. ETL technology comes up with a wide range of ETL
processes, methods and modeling process. Such a process can be considered to be an acyclic
graph with comes up with no activities and recordsets are present in each and every node which
represents each and every section of the graph (Chevalier et al. 2015). One can easily look into
the fact that the ETL process as the synthesis of the various individual task which is used for
extraction, cleaning and execution graph which is also referred to as workflow. As a result of its

11DATA ANALYTICS AND BUSINESS ANALYTICS
nature of design and user interface tools, an ETL process can be accompanied by a plan which
needs to be executed.
The fact should be taken into consideration that the ETL process is not a novel in the
domain of computer science, several issues have been encountered. The ultimate issue is also
known as traditional ETL which is provided in the agreement which is provided in provided
language for formal ETL process (Mireku Kwakye, 2017). Optimizing of whole ETL process
creates a large number of research issues. Processing of parallel ETL is considered to be vital.
Standardization is a well-known problem which requires a note of attention. The functionality of
ETL depends on new areas of traditional data warehouse environment. Such cases are inclusive
of
 On-Demand ETL process which is executed from web data and they require it to
manually initiated by the demand of some specific individual.
 Streaming of ETL process requires the possible kind of filtering, conversion of
value, transformation for streaming information in the given format.
 Real-time ETL focus on capturing the requirement of data warehousing which
contains fresh data as much as possible.
With the development of internet and technology, the interest has moved to multiple
kinds of data. It does not require the important format of traditional data (Meehan et al. 2017).
Current ETL application should be focused to handle the data in the more efficient way.
The ultimate goal of this makes use of some architecture for data warehouse along with
mediated systems (Dakrory, Mahmoud and Ali, 2015). ETL tools create a flexible management
and design for the process of ETL. It is a method to solve the issues of data integration and allow

12DATA ANALYTICS AND BUSINESS ANALYTICS
users to have the desired quantity of given data. Apart from this, there are issues in handling in
this type of data sources from websites. The solution comes up wrapper layer which takes place
in the mediated system (Tiwari et al. 2017). Both integrated data and metadata are stored in the
relational database. There is another possibility of a database system which is present in the
memory database. Different users come with quires which are processed in the given logic layers
as mediator. ETL architecture comes up with some assumption like:
 The system comes up with flexible and independent for design and management
of ETL process which is used for solving the process data integration problem.
 The system will allow the users to adopt any kind of data source and is extensible
in nature
 The system creates a platform where the user can easily access data and methods
by establishing a connection between the required data elements.
 System focus on the automatic creation of elements between the given element.
 The system can easily integrate with any presentation layer.
Tools provided by SSIS
Microsoft SQL server integration service (SSIS) is a well-known platform which can be
used for building high performance of data of integrated data solution. It is inclusive of various
phases like extraction, transformation, and load (ETL) packages which is used for data
warehousing (Katragadda, Tirumala and Nandigam, 2015). SSIS is inclusive of graphical tools
and wizards which is used for building graphical tools and wizards which is used for building
and debugging of packages. It performs the task of various workflow function like FTP
operation, SQL statement and sending e-mails messages and lastly messages. It can be
considered to be data warehouse and destination which is used for extracting and loading of data.

13DATA ANALYTICS AND BUSINESS ANALYTICS
Along with this, it aims in transforming for proper cleaning, merging and copying a large
number of data. Microsoft SQL server integration is created with the help of SSIS designer
(Zhao, Chen and Feng, 2016). The package which needs to be created taken from the file, after
that it reformats the given data. After that, the package is expanded for demonstrating the
logging or error flow and proper configuration.
Integration service is a well-known platform which helps in building high-performance of
data integration and proper workflow solution. It is inclusive of various phases like extraction,
loading, and transformation in data warehousing. Data can be easily processed from different
location and formats. It is ultimately saved in centralized formats which are Data Warehouse.
Graphical tools and wizards are helpful in debugging packages and building.
SSIS Architecture is considered to be a part of SQL server and is provided into Data
Transformation Services. It is considered to be part of SQL Server 7.0. From the perception of
the end-user, DTS and SSIS are almost similar but actually, they are different. SSIS is fully
written from the beginning phase where a new enterprise ETL product is left behind from the
limitation of DTS. The difference between DTS and SSIS is very large while the internal
structure of SSIS architecture and DTS is quite different from each other. It comes up with
separate kind of data flow engine which works from control flow engine or runtime of SSIS
engine. It has been designed in such a way that it can provide a much higher degree of parallel
progress to improve the performance.
The architecture of SSIS comprises of two important parts like
SSIS Runtime Engine: SSIS runtime provides an idea regarding the control and flow of
the package. It ultimately saves the outline of the given package, runs package along with

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supporting logs, breakpoints, configuration and lastly transaction (Runtuwene et al. 2018).
Runtime-engine is parallel to a control flow engine which aims in coordination for the
implementation of a task or works with SSIS. It handles the engine threats which helps in data
flow task which is provided in the package by the help of execution. It ultimately helps in the
execution of the data flow pipeline and allows data mask to run in the given pipeline. The
integration services run on windows which is not similar to SSIS runtime service or engine. It
aims in the design and execution integration of service packages which are provided. The given
service of windows can be easily started which is used for handling SSIS packages. It is used for
establishing a connection with multiple SSIS server. Along with it is used for start/ stop packages
in a remote area, control the packages along with import and export package.
SSIS Data Flow Engine/ Pipeline: It handles the flow of data from various sources
along with transformation on the targets of the system. With the proper execution of the data
flow task, SSIS data flow engine gathers data with sources which can be used for transformation.
It is aimed to deliver it to the given location of the destination. Data engine flow engine works
by the method of buffering. It mainly extracts the given data from the source and keeps in the
given structure in the buffer. It is applied to transformation in a memory instead of the proper
processing of it. Processing is considered to be much faster which takes place without the
physical stage of data at each and every stage of integration. Manipulation of data takes place
during transferring the whole thing from source to the target location.
Script environment: In the SSIS designer, the script parts comprises of two parts that
are the design of metadata and code design node (Sangar et al. 2015). In the metadata-design
mode, one easily modifies the script components in both inputs and outputs but the given code
can be written only once (Daniel and Dutta, 2018). After that, all the given inputs and outputs

15DATA ANALYTICS AND BUSINESS ANALYTICS
have been configured easily so that one can easily write to code design mode for writing the
script. Script components are automatically used to generate base codes of given metadata which
is generated from both input and output. If change is seen the metadata after the script
component which is generated in the base code. Then the given code might not be compatible
with provided code.
Data Profiler: The task of a data profiler is to provide the functionality which works
inside the process of proper extraction, transformation and proper loading of data. By the help of
this one can achieve a large number of benefits like analyzing the source data in a more effective
way, having an understanding of source data in the much better way and lastly prevention of any
problems related to data quality before it is introduced in the data warehouse (Chen, Zhao and
Feng, 2016). Quality of data is considered to be a vital aspect of many businesses. With the
passage of time of enterprise is building much analytical and business intelligence system which
is present on the top of the whole transaction system. It comes up with the ability to provide key
performance indicators and data mining (Mitri, 2015). It totally depends on the validation of data
to which this is based. With the passage of time, the importance of valid data has increased. Data
is easily streamed into the enterprise from the diverse system and sources which comes up a
large number of users.
Conclusion
From the above discussion, it can be easily concluded that this report is all about
analyzing the concept and technology of ETL. The whole process starts by finishing Microsoft
SSIS. After that, an idea has been provided regarding the operation of a data warehouse for
supporting business intelligence. In the above pages of the report, the importance of ETL process
for construction of data warehouse solution has been discussed in brief. The three important

16DATA ANALYTICS AND BUSINESS ANALYTICS
phases of ETL that is the extraction of data, the transformation of data and loading of data has
been discussed in details. The importance of Microsoft SSIS for providing a collection of tool
support ETL has been provided. Three tools of ETL supports are data flow engine, script
environment, data profiler has been discussed in details. Different benefits of warehouse have
been discussed in details. The implementation details of business intelligence have been
provided. A list of business intelligence software solution has been given. After that issues in
business intelligence has been provided. The architecture of SSIS which comprises of two
aspects like runtime engine and data flow pipes has been provided. ETL can be stated as the
method of extract, transform and load. An ETL tool mainly gathers data from various RDBMS
system, the transformation of data like calculation. After that, it is loaded into the Data
Warehouse system. The data is loaded in the DW system are provided in the given form of
dimension and tables. Data warehouse and business intelligence are considered to be responsible
for technical delivery and support to data of given enterprise. Three types of data flows in the
data warehouse that are structured, semi-structured and lastly unstructured data. There are three
types of data warehouse like an enterprise data warehouse, operation data store and lastly data
mart.

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