Different Types Of Fact Tables

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1. Different Types of Fact Tables
Dimension –
A dimension table typically has two types of columns, primary keys to fact tables and
textual\descreptive data.
Fact –A fact table typically has two types of columns, foreign keys to dimension tables and
measures those that contain numeric facts. A fact table can contain fact's data on detail or aggregated
level. Types of Dimensions –
Slowly Changing Dimensions: Attributes of a dimension that would undergo changes over time. It
depends on the business requirement whether particular attribute history of changes should be
preserved in the data warehouse. This is called a Slowly Changing Attribute and a dimension
containing such an attribute is called a Slowly Changing Dimension. Rapidly Changing Dimensions:
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Non–Additive:
Non–additive facts are facts that cannot be summed up for any of the dimensions present in the fact
table.Eg: Facts which have percentages, ratios calculated. Factless Fact Table: In the real world, it is
possible to have a fact table that contains no measures or facts. These tables are called "Factless Fact
tables".
Eg: A fact table which has only product key and date key is a factless fact. There are no measures in
this table. But still you can get the number products sold over a period of time.
Based on the above classifications, fact tables are categorized into two:
Cumulative:
This type of fact table describes what has happened over a period of time. For example, this fact
table may describe the total sales by product by store by day. The facts for this type of fact tables are
mostly additive facts. The first example presented here is a cumulative fact table.
Snapshot:
This type of fact table describes the state of things in a particular instance of time, and usually
includes more semi–additive and non–additive facts. The second example presented here is a
snapshot fact table.
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Types of Facts in Data WarehouseTypes of Facts in Data Warehouse
A fact table is the one which consists of the measurements,
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5. What Is Data Warehousing? Its Uses and Applications for...
Abstract Many corporations are experiencing significant business benefits of using data warehouse
technology. Users report gains in market competitiveness through increased revenue and reduced
costs through information management. Data warehousing is thus a major issue within most
organizations, and thus the development of a data warehouse with a strong base is essential. This
paper aims to present the important concepts of Data Warehousing such as Data Warehousing tools
and the benefits of Data Warehousing, that a manager must understand in order to execute a
successful Data Warehousing project in his/her company. Keywords: Data Warehouse Technology,
Market Competitiveness, Data Warehousing tools, benefits of Data Warehousing, Data ... Show
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Some of those parts are summarized into information "components" and stored in the warehouse.
Data Warehouse users make requests and are delivered information "products" that are created from
the components and parts stored in the warehouse. A Data Warehouse is typically a blending of
technologies, including relational and multidimensional databases, client/server architecture,
extraction/transformation programs, graphical user interfaces, and more. Data warehousing is one of
the hottest industry trends – for good reason. A well–defined and properly implemented data
warehouse can be a valuable competitive tool. (Perkins). The following describes the components of
a data warehouse (Figure.2) Summarized data: There are two kinds of summarized data, lightly
summarized data and highly summarized data. Lightly summarized data are the hallmark of a Data
Warehouse. All departments in a corporation do not have the same information requirements, so
effective Data Warehouse design provides for customized, lightly summarized data for every
department. Highly summarized data are primarily for the executives. Highly summarized data can
come from either the lightly summarized data used by enterprise elements or from current detail. If
executives require more detailed information they have the capability of accessing increasing levels
of detail through a "drill down" process. Current Detail: The heart of a
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9. Data Warehousing And Business Intelligence
CIS 531 – Fall 2015 – Data Warehousing and Business Intelligence Assignment # 3 Big Data,
Bigger Opportunities Abstract: Big data is the present most–liked theme of today 's technology.
These research goes through all description of techniques and technologies of extracting of the data,
storing of data, distribution of data, analyzing of data, managing of data with high velocity and from
the structured data and helps in the handling of the extreme data. Big data has the presentation the
capacity to improve predictions, saving money and enhancing the decision making process in the
fields of the traffic control, weather forecasting, disaster prevention, fraud control, business
transaction, education system, health and the national security. The graph below tells about the heat
map from granter's July 2012 study on big data by industry and by facet of technologies (Columbus,
2012). July 2012 study on big data by industry and by facet of technologies (Columbus, 2012). July
2012 study on big data by industry and by facet of technologies (Columbus, 2012). July 2012 study
What does Data.gov means? This is process of meeting the program's core purpose such that
increasing the public access, machine readable data sets that are created by the federal government.
Data.gov provides better services to public in three ways. 1) Promote and lead: Identifying the
administrative and cultural barriers such as big data leadership, data cycle,
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13. Data Warehousing And Mobile Computing
I. Abstract
Data warehousing and mobile computing are the way of the future, yet the literature on the topic is
somewhat scarce at present. This literature review focuses on the interaction of data mining, data
warehousing, mobile computing and security under the following guiding research question: How
can the mobile application of data mining and data warehousing retain a high level of security?
While cloud computing is a problematic and groundbreaking, it is useful way to engage in data
mining and warehousing in the mobile computing context. This literature review also focuses on the
ethical use of data mining, policy recommendations and adoption of security protocols, and future
research suggestions.
II. Introduction
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The core of the essay focuses on conducting a literature review. The literature review focuses on
four major sets of academic and research literature related to this topic: first, data mining; second,
data warehousing; third, mobile computing; and finally, on the topic of security itself. These are all
fast–moving fields, and all of these comprise rapidly changing sets of literature and material due to
the subject matter. The literature were examined in order to answer the question of how mobile
applications of data mining and data warehousing can set and retain a high level of security.
III. Literature Review As mentioned above, the literature review focuses on the four main topics of
investigation. Focusing on data mining, data warehousing; mobile computing and while also
focusing on security within each topic. Themes of organizational culture and communication
emerge, which in turn sheds a light on importance of information security and the need of
centralization.
a. Data Mining Data mining is a relatively new phenomenon, therefore the number of peer–reviewed
journal articles, blogs and other online sources on the topic are limited but growing rapidly. One key
book, Data Mining and Analysis: Fundamental Concepts and Algorithms by Zaki and Meira Jr.,
takes an algorithmic approach, as the title suggests. Zaki and Meira Jr. define data mining by stating
that "data mining comprises the core algorithms that enable one to gain fundamental insights and
knowledge
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17. Business Intelligence And Data Warehousing Essay
TERM PAPER/SEMINAR 0n 21st CENTURY SUCCESS MANTRAS: BUSINESS
INTELLIGENCE AND DATA WAREHOUSING
Submitted to
AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY (ASET)
Guided by: Mrs. Darothi Sarkar Submitted by: AKSHAY DOGRA
Enroll No.A2345913057 Roll No.57 AMITY UNIVERSITY UTTAR PRADESH GAUTAM
BUDDHA NAGAR CERTIFICATE
This is to certify that Mr. AKSHAY DOGRA student of B.Tech. in CSE(Evening) has carried out the
work presented in the project of the Term paper entitle "BUSINESS INTELLIGENCE AND DATA
WAREHOUSING" as a part of First year programme of Bachelor of Technology in CSE (Evening)
from Amity University, Noida, Uttar Pradesh under my supervision.
Faculty Guide: Mrs. Darothi Sarkar Signature:
AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY, AUUP
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21. Data Warehousing : Big Data Management Essay
Abstract– The Data which is structured and unstructured and is so large with massive volume that it
is not possible by traditional database system to process this data is termed as Big Data. The
governance, organization and administration of the big data is known as Big Data Management. For
reporting and analysis purposes we use data warehouse techniques to process data. These are the
central repositories from disparate data sources. Now Big Data Management also requires the data
warehousing techniques for future predictions and reporting. So in this paper we touched certain
issues of data warehousing usage in Big Data management, its applications as well as limitations
also and tried to give the ways data warehousing is useful in Big Data Management.
I. INTRODUCTION
We are living in data age, around twenty one zetabytes of data is predicted to be there till 2020.
Recent years have witnessed a dramatic increase in our ability to collect data from various sensors,
devices, in different formats, from independent or connected applications. This data flood has
outpaced our capability to process, analyze, store and understand these datasets. Today people are
totally into social networking sites such as Facebook, Orkut etc. Each user stores their data like
photos, statuses etc into these that contributes to the ever increasing size and speed of datasets. Now
if we look into the upcoming boom topic in the industry i.e. IOT, the internet of things, it will
connect people
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25. Data Warehousing Concepts, Products And Applications
The text book Data Warehousing concepts, techniques, products and applications by C.S.R. Prabhu.
Mainly, the text book gives the information about the data model, online analytical processing
systems and tools, data warehouse architecture, data mining algorithms, organizational issues of the
data warehouse, data warehouse segmentation, Application of data mining and data warehousing.
Firstly, the book describes Data Warehouse is a system where it is used for reporting the data from
the wide range of the sources and indeed it helps the company to guide the management decisions.
Moreover, Data Warehousing is the process where it evolved with the transformation and extraction
of data from the various applications. Identically, it also has a technique from the formulation of the
business intelligence where it gives effective implementation which makes the Data warehouse the
effective technology for the business use. Importantly, Data Warehouse is the division of data into
the individual data component. Similarly, Data Warehouse helps to analyze the data and whereas
they are technologies which helps to analyze the data available in the data warehouse. Indeed, the
functions of the Data Warehouse tools are data extraction, data cleaning, data transformation.
Mainly, the data extraction gathers the data from the multiple sources, data cleaning helps to find
and correct errors in data, data transformation converts the data into data warehouse format.
Consequently, data cleaning and
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29. Mysupermarket
BUILDING A BUSINESS MODEL ON DATA WAREHOUSING FOUNDA TIONS:
Executive Summary
mySupermarket is a grocery shopping and comparison website which aims to provide customers
with the best price for their shopping. This report examines how data warehousing provided
mySupermarket with the foundation in which to build a successful enterprise, and allowed a
subsequent expansion into the 'business intelligence' sector. The research draws attention to the
problems and limitations that mySupermarket encountered including; coping with diverse sources of
data streams, customer loyalty issues, achieving real–time data, data integrity and generating a
sustainable revenue stream. These problems were tackled respectively through; building ... Show
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It is, in essence, a large data storage facility which enables an enterprise to gain a competitive
advantage through analytics and business intelligence. Providing integrated access to multiple,
distributed, heterogeneous databases and other information sources has become one of the leading
issues in database research and industry, IEEE Computer (1991) which can be seen through the
success of First American Corporation (FAC), Cooper et al (2000) and Tesco/Dunnhumby, J. Perry
(2009).
Data mining is the process of 'digging–out' patterns from data, usually through Clustering,
Classification, Regression and Association rule learning. Data mining technology can generate new
business opportunities by providing: Automated prediction of trends and behaviours. Automated
discovery of previously unknown or hidden patterns
– D. Champion and C. Coombs (2010)
This process is carried out by sophisticated software packages such as Oracle, IBM and SQL. This
alleviates the (potentially) very time consuming task of manually inputting and analysing the data
Within data warehousing, there is a high importance placed on the quality of data, as without it,
meaningful analysis is impossible. Data collection should therefore be taken with a high level of
detail, and have solid definitions, as to avoid subjectivity.
The purpose of a data warehouse is to support creative strategic decision making through a greater
granularity of information with a
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33. Canadian Tire's Current Data Warehouse
Overview of Canadian Tire's Current Data Warehouse Gathering requirements from end users was
an important step in resolving the issues that currently exist at Canadian Tire. "Gathering
requirements for a data warehouse is not the same as defining the requirements for an operational
system" (Ponniah, pg. 121). When you research and then develop any system, it is critical that the
system produces exactly what the users need to perform their specific tasks. CTC did not have this
production for their end users. These users typically do not understand the big picture workings of a
data warehousing process. Therefore, the help of a business analyst is critical. From the results
provided in the case study, we can agree that these requirements must be addressed:
Capacity constraints
User query times
Defined data model
Full enabling of access to enterprise data
Overview of Canadian Tire's Business Intelligence The current process for CTC to acquire BI is a
broken system. Much of the information derived, from the current data in the warehouse, was done
by FRAG, individual user groups and hired business analysts who extracted data and created
reports. This was creating a serious misallocation of resources and responsibilities throughout the
company. Shadow groups, created to complete some of the required BI tasks, were not only less
efficient than an organized strategy, but were also a security risk to CTC. With small groups taking
over the responsibility of creating BI, the
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37. Data Warehousing And Information Warehousing
Data warehousing is a system that holds the data of an organization collected through various
channels and the data is processed through various analytical tools to generate reports for the
business users. This paper discusses the data warehouse concept along with the origin of the data
warehouse and the current trends of data warehousing. Various steps involved in the development of
the data warehousing project are discussed in this paper. This paper also lists out the challenges
encountered while planning, designing and implementing data warehouse projects and the
applications of the data warehousing. This paper concludes by discussing the future developments in
the data warehousing.
Data warehousing
Data warehousing is a ... Show more content on Helpwriting.net ...
Origin of data warehousing In early 80s business world more concerned about the large amount of
data that is emerging from their customer world and worried about how to store that amount of data,
during that time old business instruments took a large amount of time to execute the business instead
of running it and it is also costly, time consuming and risky to deals with that much big amount of
data (Inmon, 2005). The concept of data warehousing dates back to the late 1980s when IBM
researchers Barry Devlin and Paul Murphy introduced the term "Business data warehouse". In 1986,
Red Brick Systems founded by Ralph Kimball began to do research on improving data access
(Hammergren, 2005). In 1990s executives become less concerned with the day–to–day business
operations and overall concerned with overall business functions and worried about large amount of
data. Due to the improvements and magnification in the information systems where large amount of
data needs to be saved and retrieved, data warehousing was additionally enhanced and advanced to
cope up with such immensely large amounts of data (Kelly, 2009).
Trends in data warehousing
To have a better results, the enterprise should be able to analyze its data quickly as it access it.
Enterprises should understand how customers interact with the business. According to Ponniah
(2004) "data warehousing is revolutionizing the way people perform business analysis and make
strategic decisions" (p. 40, para.
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41. The Concepts Of Living In The Age Of The Customer
There is no doubt which we are living in "The Age of the Customer" (Datasciencecentral.com,
2017). Consumers all over the globe are now digitally empowered, and they have the power to
determine which businesses will be successful and advance, and which ones will fall
(Datasciencecentral.com, 2017). As an outcome, the bulk of intelligent corporation without a second
thought perceive that they have to be customer–control to thrive (Datasciencecentral.com, 2017).
They must have real–time data and analytical information so that they can give their customers what
they desire and produce the exceedingly, most exceptional customer fulfillment achievable
(Datasciencecentral.com, 2017).
This comprehension has specified move up/upward(s) to the idea ... Show more content on
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Newer BI deployments execute methodologies for gauging ROI and defining the benefit of BI
efforts (Datasciencecentral.com, 2017).
There is incompetence in communication and alignment between IT and business teams
(Datasciencecentral.com, 2017).
Failure to precisely control operational contingency, fix latency difficulties and maintain scalability.
While BI is intended to change all of these, traditional BI is falling behind (Datasciencecentral.com,
2017).
The challenge with platform migration and combination (Datasciencecentral.com, 2017).
Bad data quality. If data mining is quick and comprehensive, if the condition of the data is not
relevant to it standard, it will not be useful in generating actionable intelligence for critical business
choices (Datasciencecentral.com, 2017). So how can merging conventional BI, agile BI, and big
data support businesses to flourish and thrive in today's market? Consider that big data gives
organizations whole picture of the consumer by drawing into various data sources
(Datasciencecentral.com, 2017). At the similarly, agile BI approaches the need for quicker and more
adaptable knowledge. Connect the pair, besides with previously existing conventional BI, and
applications that were previously separate can operate collectively to generate a robust method of
insight and analytics (Datasciencecentral.com, 2017).
Through this latest BI approach, organizations can consistently provide
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45. Data Warehousing Fundamentals For It Professionals
Running head: Summary and Review of Data Warehousing Fundamentals
Data Warehousing: Data Warehousing Fundamentals for IT Professionals
By
Paulraj Ponniah
Summary and Review
By
Department of Computer Science, Engineering, and Physics
University of Michigan–Flint
SUMMARY
Below is a summary of the book "Data Warehousing Fundamentals for IT Professionals", written by
Paulraj Ponniah. Data Warehousing Fundamentals was written in June, 2010 containing 544 pages
in its first edition, published by Wiley India Pvt Ltd and the edition type of this book is student. The
author has above thirty years of experience in the field of IT and he has command over the design
and implementations of database systems. Dr. Paulraj Ponniah has ... Show more content on
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Since the first version of "Data Warehousing Fundamentals", many corporations have implemented
data warehousing systems, in addition to implementation the great benefits are notice. Many more
enterprises are in the process of adopting this technology.
REVIEW
Author Ponniah divided the book into six major parts such as; Overview and Concepts. Planning
and Requirements, Architecture and Infrastructure, Data Design and Data Preparation, Information
Access and Delivery, and the sixth one is Implementation and Maintenance.
First 3 chapters of the book are written in a way that beginners may get clear view of the basic
concepts. First chapter described the need regarding strategic information, information crisis, and
that the data warehousing is a better solution for information crisis. Features and components of
Data warehouse, along with the concept and need of metadata is described. Various trends in data
warehouse are mentioned by the author based on his own industrial experience. Areas like
Continued growth in data warehousing
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49. Data Warehousing Is A Single Repository Or Storage Of Data...
1. INTRODUCTION
Data warehousing is a single repository or storage of data integrated from different sources which
allows the business and the organization to generate a report or analysis to make decisions based on
those data. Data warehousing is an integral part of any organization as it helps to increase the
efficiency of the decision makers by integrating and transforming the data from multiple
incompatible sources to the consistent and meaningful information which allows them to perform
accurate, consistent and substantive analysis of data(P. 1152).
The data stored in data warehouse will not provide any benefit to the organization until the hidden
information is extracted from it. There are various way to extract the data, however data mining is
the best method to get the meaningful trends and patterns from the data warehouse. Therefore, data
mining can be defined as the method of extracting the valid, comprehensible and previously
unknown information from the huge storage to use on the decision making process (P. 1233).
This report will presents the various features of data mining tools and emphasize the importance of
data mining to realize the value of data warehouse.
2. DATA MINING TOOLS
There are plenty of data mining tools available in the market and most of the seller also provides the
demo or freeware version. Some of them are RapidMiner, WEKA, R–Programming, Orange,
KNIME, NLTK etc (http://thenewstack.io/six–of–the–best–open–source–data–mining–tools/)
2.1
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53. The Availability Of New Information Management And...
The availability of new information management and supporting system like Data Warehousing,
Business Intelligence, Analytics, and/or Big Data has produced a remarkable moment in the history
of data analysis. Researching on this topic is very interesting for me. Thank Professor Kraft that
gives me opportunity to explore more on these topics. Taking this opportunity, I would like to
provide a brief summary of the book that discuss about the Profitable Data Warehousing, Business
Intelligence and Analytics. The book published by Technics Publications in July 1, 2012. I also
would like to thank David Haertzen who is an author of the book.
In the book, the author has discussed many interesting points but the three main points that I learn
and ... Show more content on Helpwriting.net ...
It involves business information and analysis that support strategic decision–making, and lead to
improved business performance. Decisions based on Business Intelligence and Analytics can impact
the bottom line by reducing cost and increasing revenues.
According to Haertzen (2012), "Enterprise Data Warehousing (EDW) is a process for collecting,
storing, and delivering decision support data for an entire enterprise or business unit". A data
warehouse is not operational data. It contains a copy of operational and other data, rather than being
a source of original data. This data is often obtained from multiple data sources and is useful for
strategic decision–making. Its purpose is not just to maintain historical data. A data warehouse
contains specific data that has been gathered for analytics and reporting. Enterprise Data
Warehousing includes people, processes, and technologies to achieve the goal of providing decision
support. The Data Warehouse contains intelligent data collections which are modeled to support the
reporting and analysis needs of the Decision Support function of the organization. So, the main key
goals of data warehouse are: Make fact based decisions, Make timely decisions, Make profitable
decisions that reduce costs and increase revenue. These decisions can support a number of
stakeholders include: Customers, Employees, Shareholders, Suppliers,
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57. Data Warehousing And Data Mining
Data Warehousing and Data Mining
Data Warehousing also known in many industries as an Enterprise Data Warehouse is a system that
contains a central repository of integrated data, often collected from multiple sources and is used to
perform data analysis enabling the creation of detailed reports that contribute significantly to a
corporation's business intelligence. Data Warehousing emerged as a result of advances in the field of
information systems over the last several decades. There are two major factors that drive the need
for data warehousing in most organizations. First and foremost, businesses require an integrated,
company–wide view of high–quality information to maintain and improve upon their strategic
position. Secondly, information systems departments must separate information from operational
systems to improve performance dramatically in managing company data. Critical to the success of
a Data Warehousing system, Data mining allows for companies to create customer profiles,
manipulate information easily, and provide knowledgeable access to the current state of their
company. However, a reality that many companies often find out the hard way is that data mining
and data warehousing does not work for them. As with many new tools or technology, companies
may jump on the bandwagon without fully contemplating its potential weaknesses. In order to
remain competitive in today's business world, companies should consider implementing data
warehouses, but only with
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61. Data Warehousing And Data Mining Essay
Introduction Data Warehousing and Data Mining has always been associated with manufacturing
companies, where sales and profit is the main driving force. Subsequently Higher Education has
grown throughout the years; this growth is predominately associated with the increase of online
institutions. This growth has resulted in higher education to adapt to a more business like institution
(Lazerson, 2000). Since higher education has blurred the lines with traditional businesses, it is
important to have the tools to assist them with valuable data and information, in making decisions.
Using of data and having the right data mining tools can insure the institute's success, in many
forms, such as, identifying market trends, precision marketing, new products, performance
management, grants and funding management, student life cycle management and procurement to
mention a few. To get a better grasp on these benefits it's important to understand data warehouse,
data mining and the associated benefits.
Data Warehouse Data warehouse are multiple databases that work together. In other words, data
warehouse integrates data from other databases. This will provide a better understanding to the data.
Its primary goal is not to just store data, but to enhance the business, in this case, higher education
institute, a means to make decisions that can influence their success. This is accomplished, by the
data warehouse providing architecture and tools which organizes and understands the
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65. Multidimensional Data Model
A MULTIDIMENSIONAL DATA MODEL
Data warehouses and OLAP tools are based on a multidimensional data model. This model views
data in the form of a data cube.
FROM TABLES TO DATA CUBES
What is a data cube?
A data cube allows data to be modeled and viewed in multiple dimensions. It is defined by
dimensions and facts.
In general terms, dimensions are the perspectives or entities with respect to which an organization
wants to keep records. Each dimension may have a table associated with it, called a dimension table,
which further describes the dimension.
Facts are numerical measures. The fact table contains the names of the facts, or measures, as well as
keys to each of the related dimension tables.
Example:
2–D representation, the sales ... Show more content on Helpwriting.net ...
Fact constellation:
Sophisticated applications may require multiple fact tables to share dimension tables. This kind of
schema can be viewed as a collection of stars, and hence is called a galaxy schema or a fact
constellation.
Fact constellation schema of a data warehouse for sales and shipping
This schema species two fact tables, sales and shipping. The sales table definition is identical to that
of the star schema. A fact constellation schema allows dimension tables to be shared between fact
tables.
In data warehousing, there is a distinction between a data warehouse and a data mart. A data
warehouse collects information about subjects that span the entire organization, such as customers,
items, sales, assets, and personnel, and thus its scope is enterprise–wide. For data warehouses, the
fact constellation schema are commonly used since it can model multiple, interrelated subjects.
A data mart, on the other hand, is a department subset of the data warehouse that focuses on selected
subjects, and thus its scope is department–wide. For data marts, the star or snowflake schemas are
popular since each are geared towards modeling single subjects.
Examples for defining star, snowflake, and fact constellation schemas
In DMQL, The following are the syntax to define the Star, Snowflake, and Fact constellation
Schemas:

66. MEASURES:
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70. Data Mining and Warehousing Techniques
Background – One of the most promising developments in the field of computing and computer
memory over the past few decades has been the ability to bring tremendous complex and large data
sets into database management that are both affordable and workable for many organizations.
Improvement in computer power has also allowed for the field of artificial intelligence to evolve
which also improves the sifting of massive amounts of information for appropriate use in business,
military, governmental, and academic venues. Essentially, data mining is taking as much
information as possible for a variety of databases, sifting it intelligently and coming up with usable
information that will help with data prediction, customer service, what if scenarios, and
extrapolating trends for population groups (Ye, 2003; Therling, 2009). . In any data warehousing
model, the ultimate success of the operation is entirely dependent upon the strengths and
weaknesses of the information delivery tools. If the tools are effective, data will be available in a
robust manner that it ultimately appropriate for the end user. Because there are so many different
types of delivery mechanisms, the data must be available in a variety of formats. In the data
warehouse for instance, the data must be transactional from numerous sources and have the ability
to slice and dice into usable reports. We can think of it as a major information repository functioning
in three layers: staging used to store the raw data
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74. The Enterprise Data Warehouse : General Overview
The Enterprise Data Warehouse
3.1 General Overview
3.1.1 Enterprise Insights Overview The Enterprise Insights (EI) team's mission is to provide timely,
reliable, and actionable information to facilitate the best strategic and operational business decisions
to support SWA company objectives. EI seeks to deliver advanced analytic capabilities through
partnership with business customers to provide relevant and insightful information to businesses.
The EI team facilitates access to the enterprise data warehouse (EDW) which is used to consolidate
data from many data sources both within SWA and outside SWA. The EDW has several purposes:
Consolidate and integrate enterprise data by subject area in a relational store at the lowest level of ...
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This overview provides the foundation for how the estimates are formulated in EI data warehouse
and business intelligent using the proposed estimation utility.
3.1.1.1 The Enterprise Data Warehouse (EDW)
"A data warehouse is a subject oriented, integrated, time variant, non–volatile collection of data in
support of management 's decision making process". Source
There are
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78. Emerging Technologies And Techniques For Business Leaders
We are currently living in the digital world. Data generated by each and every device is growing
exponentially in every area like Aviation, Satellite, Stock Market, Research, Social Media, Retail
Industry etc., more and more techniques and discoveries are taking place to collect and process vast
amounts of data in shorter interval of time. In order to significantly improve progress in those areas,
scalable and high performance IT infrastructures are needed to deal with the high volume, velocity
and variety of data.
On the other hand, every part of our day to day usage of electronic devices generate some amount of
data in its fashion. Companies grew in rapid phase to collect each piece of data generated by the
people to use for their analytics to study and predict customer willingness towards the products.
Emerging technologies and techniques are introducing day by day to identify new optimal ways to
predict customer interests. Data generated are to be collected on the fly and provide insights to help
business leaders to take better decisions.
As Big Data problems evolve, each application have its own characteristics with respect to their data
and analysis process. Firstly, besides the huge amount of historical data, streaming data plays an
important role. For instance, GPS ground stations do monitor and predict geological events on
earthquakes generates lots of real time data which needs streaming data processing. Automatic
trading systems in stock market needs dynamic
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82. Data Warehousing and Data Mining
Data Warehouses and Data Marts: A Dynamic View
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Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone, Ph.D. White Paper No.
Three March 27, 1997
Patterns of Data Mart Development In the beginning, there were only the islands of information: the
operational data stores and legacy systems that needed enterprise–wide integration; and the data
warehouse: the solution to the problem of integration of diverse and often redundant corporate
information assets. Data marts were not a part of the vision. Soon though, it was clear that the vision
was too sweeping. It is too difficult, too costly, too impolitic, and requires too long a development
period, for many ... Show more content on Helpwriting.net ...
Moreover, its relation to the data warehouse turns the first pattern of development on its head. Here
multiple data marts are parents to the data warehouse, which evolves from them organically. The
third pattern of development attempts to synthesize and remove the conflict inherent in the first two.
Here data marts are seen as developing in parallel with the data warehouse. Both develop from
islands of information, but data marts don't have to wait for the data warehouse to be implemented.
It is enough that each data mart is guided by the enterprise data model developed for the data
warehouse, and is developed in a manner consistent with this data model. Then the data marts can be
finished quickly, and can be modified later when the enterprise data warehouse is finished. These
three patterns of data mart development have in common a viewpoint that does not explicitly
consider the role of user feedback in the development process. Each view assumes that the
relationship between data warehouses and data marts is relatively static. The data mart is a subset of
the data warehouse, or the data warehouse is an outgrowth of the data marts, or there is parallel
development, with the data marts guided by the data warehouse data model, and ultimately
superseded by the data warehouse, which provides a final answer to the islands of information
problem. Whatever view is taken, the role of users in the dynamics of data warehouse/data
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86. Role Of Data Warehousing And Data Mining Technology
Janakkumar Patel
Farzan Soroushi
ISM 530
September 28, 2014
White paper on ROLE OF DATA WAREHOUSING AND DATA MINING TECHNOLOGY IN
BUSINESS INTELLIGENCE
ABSTRACT
Information tеchnology is now еssеntial in еach part of our livеs which hеlp businеss and еntеrprisе
to makе usе of applications likе dеcision support systеm, rеporting and quеry onlinе analytical
procеssing, and prеdictivе еxamination and businеss routinе managеmеnt. A data warеhousе is a
rеpository of rеlational databasеs dеsignеd for quеry which is analyzеd by data mining tеchniquе
allowing еnormous data sеts to bе еxplorеd so as to yiеld hiddеn and unidеntifiеd еxpеctations that
can bе usеd in futurе for еffеctivе dеcision making.
Table of Contents
Introduction................................................................................................................4
Data Warehouse..........................................................................................................4
Metadata– handling unstructured and semi–structured data.........................................................4
Requirement for data warehouse........................................................................................5
Process of Data
Warehousing.......................................................................................................................5
Data Mining Process.....................................................................................................6
DATA MINING MODELS TO SUPPORT BI......................................................................7
CRISP (Cross–Industry Standard Process for Data Mining).............................................7
Six Sigma........................................................................................................8
SEMMA.........................................................................................................8
Next Generation data mining techniques..............................................................................9
The Future of
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90. Data Management, Data, Warehousing, And Warehousing Essay
There are many different areas in information systems to study. Data management, data mining, data
warehousing, information management, information security, information assurance, healthcare
informatics and bioinformatics are just a small sample of some of the different areas of study that
will be examined in this paper. Also included in this paper are answers to questions posed by the
rubric for this assignment.
Data management, mining, and warehousing all deal with data in different ways. Data management
establishes the groundwork for an organization to structure, regulate, process, and store data that
they acquire (Rouse, 2016). Data management also encompasses the creation of definitions and
standards for the acquired data which will be adhered to throughout the organization (Definition of:
Data management, 2016).
Data mining is "[t]he process of finding significant, previously unknown, and potentially valuable
knowledge hidden in data" (Gordon, 2007). Organizations use data mining to sift through massive
quantities of raw data in order to find patterns and relationships that will ultimately be used for
business purposes (Definition of: Data mining, 2016). Organizations mainly use data mining to get a
better idea of their customer's purchasing habits, product preferences, etc. in order to create sales
tactics targeted at a certain customer demographic (Definition of: Data management, 2016).
Data warehouses are huge repositories where data from various sources all
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94. Examples Of Historical Data Trends
Historical Data Trends
Now that the E–Station's database is in production and functioning properly, the next phase is to
obtain historical data trends. It is necessary to use both internal and external data sources to build
trends for current and future analysis. The database management team will build a central data
warehouse (DWH) repository to store the data needed for reporting. The team will use the same
software used earlier to secure the database tables, namely, SQL Server Data Tools (SSDT). The
software also has built in capabilities for Business Intelligence (BI) reporting functions that include
summarized reports along with charts and graphs. This paper will outline how the E–Station
database management team will use SSDT–BI ... Show more content on Helpwriting.net ...
Satisfied customers mean they are returning on a continuous basis. After the Schema model, the
DBA will create the necessary tables and queries needed for reporting purposes to show where the
business value stands (Chapple, 2017).
Fact Table
The fact table stores the customers charging information. The data is numerical and easily
manipulated for summing the rows (Chapple, 2017). The analyst will pull a profit report using
criteria by station, spot, and equipment.
Dimension Table
The dimension table will store the primary key of the charging units, and the fact table will store the
foreign key, linking back to the dimension table, which has the qualitative data (Chapple, 2017). The
dimension table data is the sales of kilowatts used, along with the location and equipment used.
Besides, the dimension table will also store the geography and time data as the attributes. Both the
dimension and fact tables need indexing on the start time for efficient query retrieval, which is a
significant key to use for appending data in chronological order (Brown, 2017). Thus, the best load
strategy is to update the new or changed data. The DBA will perform load testing and query
performance testing to ensure the granular level data is consistent and accurate. Also, index testing
is necessary to ensure system performance is adequate.
Reporting
The analyst will use dashboards created by the DBA to pull the weekly, monthly, and quarterly
reports. The reports will
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98. Data Warehousing and Data Mining
Data Warehousing and Data mining
December, 9 2013
Data Mining and Data Warehousing
Companies and organizations all over the world are blasting on the scene with data mining and data
warehousing trying to keep an extreme competitive leg up on the competition. Always trying to
improve the competiveness and the improvement of the business process is a key factor in
expanding and strategically maintaining a higher standard for the most cost effective means in any
business in today's market. Every day these facilities store large amounts of data to improve
increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible
future trends; say for seasonal reasons. Data ... Show more content on Helpwriting.net ...
The question still remains whether or not the user purchased, but this was a source of enticement for
a customer to potentially what Amazon likes to call impulse buying. This is not something that has
been openly admitted, however there are several case studies (Coskun Samli, A. A., Pohlen, T. L., &
Bozovic, N. 2002). Not soon after is when Wal–Mart picked up on the trend and placed their
destination towards data mining and data warehousing. Now Walmart on its own stores 460
terabytes on Teradata mainframes which is actually is half the total usage of the internet today.
Imagine this on top of the physical locations where there are roughly about 100 million patrons
entering Walmart's doors every day. If this does not convince you of the possibilities of data mining
I am not sure what else would convince you other wise and with a profit margin to be spread
between the shareholders and CEO's of roughly about 64 billion dollars a year I do believe that I
would model after these two giants to make a statement in the business world.
Utilizing different techniques for data mining is extremely important for what may work for ne may
not work for the other. With the
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102. Data Warehouse And Data Warehousing
Introduction
The data collection company has a huge collection of data generally goes into terabytes. Data
warehouse is the only option Though not much structured like relational databases but when used in
line with them, data warehouse turns out to be a huge help. Every moment, a single page visit
generates thousands of records to be saved. Data may get outdated but it cannot be deleted as few
months down the line, it will be used for analysis. This analysis generated future prospects of
business. And this analysis depends on – how the data is collected and how efficiently it be arranged
to give clear picture for analysis.
Being a CIO of the company I had to look for data warehouse vendor which can enhance our
analytics operations.
The ... Show more content on Helpwriting.net ...
It helps an organization consolidate data from several sources by separating analysis workload from
transactional workload. Additionally, a data warehouse environment includes ETL which is
Extraction, Transportation and Loading solution, an OLAP which is Online Analytical Processing
Engine, analysis tools and other tools so as to look over the process of gathering data and finally
delivering it to business users. The data stored in these warehouses must be stored in a way which is
reliable, secure, and easy to process and manage. The need for data warehousing arises as businesses
become more complex and start generating and gathering huge amount of data which were difficult
to manage in the traditional way.
A more detailed analysis of the need for Data Warehousing is as follows:
Data Integration
An enterprise data is large and complex and spread out through a variety of different in–house and
external systems. Also there is a need to analyze the data across different systems by location, time
and channel. Hence the data integration is needed here so that all the data organized and stored in
one location. It also cuts down time and the lengthy process involved in generation of reports
because a series of steps involved in it which are stripping and extraction of data from one source
and then sorting and merging of data and then enriching the data by
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106. Case Study: Active Data Warehousing
1. Describe "active" data warehousing as it is applied at Continental Airlines. Does Continental
apply active or real–time warehousing differently than this concept is normally described?
An active data warehousing, or ADW, is a data warehouse implementation that supports near–time
or near–real–time decision making. It is featured by event–driven actions that are triggered by a
continuous stream of queries that are generated by people or applications regarding an organization
or company against a broad, deep granular set of enterprise data. Continental uses active data
warehousing to keep track of their company's daily progress and performance. Continental's
management team holds an operations meeting every morning to discuss how their ... Show more
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The customers can rest assured knowing that their personal information (i.e. social security numbers
and credit card numbers) are protected from being opened by any users that are not authorized to
view this sensitive information.
5. What special issues about data warehouse management (e.g., data capture and loading for the data
warehouse (ETL processes) and query workload balancing) does this case suggest occur for real–
time data warehousing? How has Continental addressed these issues? Real–time data warehousing
creates some special issues that need to be solved by data warehouse management. These can create
issues because of the extensive technicality that is involved for not only planning the system, but
also managing problems as they arise. Two aspects of the BI system that need to be organized in
order to elude any technical problems are: the architecture design and query workload balancing.
Architecture design is important because when a company is progressively receiving business and
different aspects of the customers' usage of the company changes the warehouse needs to frequently
be updated. Continental planned for the company to use real–time data warehousing so they
structured the design to accommodate for the demand of real–time information. The information
then became easier to update the warehouse in a timely manner. Query workload balancing is
another important aspect of the warehouse that
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110. The Return on Investment of Data Warehousing Essay
The Return on Investment of Data Warehousing
This paper will present the return on investment (ROI) of data warehousing (DW). The history of
data warehousing is based on the definition and timeline. Then, detailed information about return on
investment will be discussed. Following, will be information about data warehousing new
technology of hardware and software. Data Warehousing is a new term in my department where we
use the Network Appliance (NetApps) Netfiler storage devices/units. The information read was very
informative and helpful in my understanding data warehousing better. Finally, a conclusion about
the return on investment of data warehousing. According to Ralph Kimball's article, ... Show more
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The data warehouse comes ready for use, but an organization has to get prepared to use it. The main
factor is data warehouse usage. A data warehouse can be used for decision making for management
staff. Article, www.coppereye.com/data_warehousing, states the aspects of return on investment of
data warehouse is "the architectures have typically placed a premium on storing large volumes of
data, and being able to execute queries very rapidly against this data." Real–time, with current
information, is what is available with all the new data warehouse technology. Also, the article states,
"it is common practice that loading the data is done overnight, and in many cases taken much longer
with the growing success of data warehouse projects." Another aspect is, "business owners are no
longer willing to accept reporting on last week's or even yesterday's performance, but want
immediate access to data and reports about what is happening in the business to make ever more
time–critical decisions.": The website article, www.generation5.ca/mwm, discusses measuring the
ROI of information technology (IT). "Sales growth can be affected by many factors – innovation,
client benefits, competition, etc." "Price optimization for any company, can be either a
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114. Customer Service And Customer Loyalty
In my opinion our company is relatively strong in 2 of the 4 characteristics of "analytic competitor"
as described in the text. Our goal as company is to be superior in customer service and customer
loyalty. To that end we have tools developed that assist everyone in the company and we are
committed, from the CEO down to line–level team member, to review the same statistics used for
continuous improvement. Where we lack strength is having a distinctive capability and due to some
system limitations in data warehousing, we still do quite a bit of individual reporting. Prior to
working for this company I worked at the Well Established Casino (WEC) organization for 10 years.
I learned quite a bit about standard reporting at WEC. Digging into how things worked and what
interpretations were used to categorize casino player behavior was very rewarding. While WEC was
superior in bucketing and appropriately setting expectations in future customer behavior, their
reporting lacked some details preventing marketing operations from quickly responding to outlier
influences and climate changes in the industry. I moved onto HTC where most of the reporting was
financial in nature. Having opened newer casino properties our corporate structure did not
immediately embrace the need for marketing analysis to potentially improve revenue streams that
were already robust enough. I came on board with HTC in late 2010 as the Regional Director of
Marketing Analysis when they opened casino number two.
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118. Data Warehousing System for Miller Inc
Design document Support for need of data warehousing Miller Inc. requires a transaction processing
system since the volume of transaction has increase beyond the normal capacity for performing
server and disk bound tasks which are associated with querying and reporting. Miller Inc.'s system
utilizes servers and disks that are not used by the transaction processing system. Therefore a data
warehouse will provide the quickest way to process transactions within a reasonable amount of time.
It also allows for less server and disk resources to be utilized which is expected to reduce the
transaction processing time considerably and thus help in speeding up the system overall. It is also
expected that running queries and reports which normally use up a lot of the limited system
resources will be speeded up since the data warehousing architecture uses separate servers and disks
for querying and reporting. The data warehousing system will also allow the company to use a data
model and server technology that speeds up querying and reporting. This is because these will not be
included in the data processing time thus allowing the company to use a modeling technique that
does not slow down or complicate the transaction processing system. The data warehouse will also
allow the company to use a bit–mapped indexing system as their server technology in order to speed
up query and report processing. Technologies for transaction recovery will also be employed to
speed up transaction
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122. Conversion Xml Schema For Data Warehouse Schema
Assignment –3
Literature Review
Conversion of the XML Schema to Data Warehouse Schema
Introduction: eXtensible Markup Language is used mainly in most of the organizations for e–
commerce and online applications. Indeed, XML has become the standard for representing,
exchanging the data among the various applications on the internet. Moreover, XML schema is used
for representing the XML document structure where XML data is associated with the XML schema.
Furthermore, data warehouse provides tools which business use the data for making the important
decisions. Correspondingly, data is stored in the fact table and multidimensional tables. Mainly, the
table association between them are generally represented with the three data warehouse schemas
like a) star schema b) fact constellation schema c) snowflake schema. Simultaneously, the use of the
internet is increasing day by day and by first integrating the data and secondly converting the data
into XML schema from the schema graph to the various data schemas. At first, schema graph is
taken as the model for the conversion of the data that is extracted from the XML schema and the
data is transformed into the various schema. Consequently, the data warehouse schema is
constructed with these fact tables, dimension tables and the relation existing between the graph and
tables.
Mainly, in the data warehouse analyzing the large data helps the decision–making process. Indeed,
in the data warehouse, the integration of the data from the
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126. Data Warehousing : A Data Warehouse
I am sure that use of this technology will grow radically in next few years.
Data warehouse:
Data warehousing is an efficient system which store the past as well as current data used for creating
reports. Data warehousing system is used for decision making by analyzing the reports. A data
warehouse is a relational database, which is designed for analysis and query. It helps an organization
to consolidate and analyze data from different sources and make decision. A data warehouse
environment consists of OLAP (On–Line Analytical Processing) engine, ETL (Extraction,
Transformation and Loading) process, client analysis tools and other applications that manage
gathering and delivering the data.
A data warehouse allows you to perform many types ... Show more content on Helpwriting.net ...
What are the products that are frequently bought by best customers? ". In this case 'sales' is the
subject, thus a data warehouse can be defined by any subject like purchase, inventory, finance,
marketing etc..,
Integrated:
Data is gathered from disparate sources and stored r uploaded into data warehouse, so the data must
be in consistent format. Problems such as inconsistency among units of measure and naming
conflicts must be resolved. When there are no conflicts and inconsistency then it is said to be
integrated.
Nonvolatile:
Once the data is loaded into the warehouse, it should not be changed because this data allows us to
analyze what has happened. In general data warehouse provides read only access and once the data
loaded into these systems, changes are very rare.
Time Variant:
Analytics need huge amount of data to make a decision. In OLTP systems current data is maintained
and historic data will be moved to an archive where as a warehouse stores all the historic as well as
current data. When compared to operational systems data in a warehouse has longer time horizon.
Derived and aggregated data are common in data warehouse. A data warehouse is a demoralized or
partially demoralized database management system. Data warehouse is suitable for ad hoc queries
and it can perform a variety of possible query operations. Using bulk data modification techniques,
on a regular basis a data warehouse is updated. It depends on the requirement
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130. An Overview Of Data Warehousing
An Overview of Data Warehousing
Samuel Eda
Wilmington University Abstract
Data warehousing is a crucial element of decision supporting process, which now for a long time
has become a focus of the database industry. Vast number of commercial products and various
services has been available now, and all of the top notch database management system vendors now
have offerings in this area. This paper provides an overview of history of data warehousing, the type
of systems in data warehousing, focusing on data mart, online analytical processing (OLAP), and
online transaction processing (OLTP). This paper also emphasizes on the data warehouse
environment, information storage, design methodologies including bottom–up design and top–down
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Data warehouses are targeted for decision supporting. Old, summarized and consolidated data is
very much important than detailed as well as individual records. As data warehouses store
consolidated data, possibly from several operational databases, for perhaps a very long time, they
tend to be in orders of magnitude much greater than operational databases; enterprise data
warehouses are projected to be hundreds of gigabytes to terabytes in size.
The data stored in the warehouse is uploaded from the operational systems for example marketing,
sales, etc. The data may be passing through an operational data store for additional operations before
it is used in the DW for reporting.
History The concept of data warehousing goes back to the late 80s when IBM researchers Barry
Devlin and Paul Murphy developed "business data warehouse". To summarize, the concept of data
warehousing was created to provide an architectural model for the flow of collection of data from
various operational systems to the decision supporting environments. The concept attempted to
solve the various technicalities associated with this flow of data, primarily the high costs associated
with it. In the absence of a data warehousing, an enormous amount of redundancy was needed to
support multiple decision support environments. In larger organizations it was usual for multiple
decision support environments to operate on their own. Even
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134. The Data Warehouse Toolkit By Ralph Kimball And Margy Ross
ASSIGNMENT –1
The Data Warehouse Toolkit
Summary: The text book I have chosen is "The Data Warehouse Toolkit" third edition, written by
Ralph Kimball and Margy Ross. This book mainly involves on techniques to develop the business in
real–time. As the authors had a lot of experience because of their work from 1980's, they have seen
both the growth and failures of the companies in the market. Chapters in this text book involves
goals of data warehousing which include Data staging area, data presentation, data access tools.
Kimball modeling techniques involves gathering business requirements and data realities, business
processes, different table techniques. Case studies in retail sales are explained in this text book, four
step dimensional design process which includes the design process with the help of different
dimensions and facts. In order management chapter it deals with the business processes that to be
implemented in data warehouses as they supply core business performances metrics and finally
provide the real time warehousing requirements. Customer relationship management involves in
improving the customer relation with the company or product, understanding the needs of customer
and providing high level service is the goal of this chapter. In accounting, we deal with model of
general ledger information for the data warehouse, it describe the years and dates at which things to
be happened and show different dimensional models which helps to combine the data from
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138. Data Warehouse : A Model Suitable For Presentation And...
1. Data warehouse
Answer: The term data warehouse is often used to refer to a system that extracts data from one or
more sources, in order to transform and store in a model suitable for presentation and analysis. It can
also be used to refer to just the database used in the aforementioned type of system. There are two
main approaches to building a data warehouse, the Kimball approach and the Inmon approach.
2. Data mart
Answer: A data mart is a database which contains a subset of the data found in a data warehouse and
is meant for use by single department. Typically,, data marts will use a dimensional model over a
normalized relational model. Both the Kimball and Inmon approach utilize data marts. In the
Kimball approach, the data ... Show more content on Helpwriting.net ...
The coarser the grain the more limited the data warehouse is in terms of analysis opportunities.
Since analysis is the main purpose of most data warehouses, a fine grain is usually selected over a
coarse grain. However, a higher level grain can provide performance and storage advantages over a
finer grain. Two important things to remember, with respect to choosing a grain, are that the grain
must be consistent and the level of granularity should be sufficient to answer all possible user
queries.
4. Roll up
When you roll up data you create a summary of the data stored based on one or more dimensions
and a function or formula. This can be accomplished because some dimensions are hierarchical, e.g.
in the dimension time, a year contains twelve months, each of those months contains between 27
and 31 days, each of those days contains twenty–four hours and so forth. A roll up command moves
up these dimensional hierarchies to return a summary at higher level. For example, if you have a
data warehouse and each record in a fact table representing a single sales transaction, then you can
roll up on sales by summing all sales based on time. You could have total sales by month, by quarter
by year etc. You can roll up a fact based on multiple dimensions. For example, if each sales
transaction was connected to a sales person and each sales person worked for a department and each
department existed as part of a section, then you
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142. The Development Of Data Warehouse Essay
3. Traditional ETL process
3.1 Traditional ETL During ETL process, data from many sources will be extracted and integrated
into data warehouse periodically. Extraction is a process to identified and retrieve all relevant data
from the sources. The role of transformation is to cleansing the data and integrated different schema
to defined schema in data warehouse. Meanwhile, loading is a process to physically move the data
from operational system to data warehouse.
3.1.1 ETL Concept
It is necessary to define ETL scope by analyzing each target table (its dimension and facts) at the
beginning of ETL architecture process. It is necessary to fetch the behavior of each target table;
where its source is and what kind of business process that depend on it.
3.1.1.1. Metadata
In the early development of data warehouse, integration is formed by making specific ETL program
for the structure of source database and data warehouse database. As the time passed by, it is found
that those specific ETL programs essentially are doing the same process. Many block programs are
reusable for another ETL process. At this point, ETL tools that can do automated data integration to
data warehouse are developed. To put it briefly, metadata is data about data. Specially, metadata
ETL is data about ETL process. Defining metadata ETL is necessary to build ETL program with
high reusability
.
3.1.1.2. Extraction Extraction identifies all relevant sources, then extract the data as efficiently as
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146. Kudler Dimensional Model Hands-on-Project Essay
Kudler Dimensional Model Hands–On–Project Erwin Martinez DBM–460 March 14, 2011 Daniel
McDonald Kudler Dimensional Model Hands–On–Project Kudler is looking for ways to increase
sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict
future trends and behaviors to allow them to make proactive, knowledge–driven decisions. Kudler's
marketing director has access to information about all of its customers: their age, ethnicity,
demographics, and shopping habits. The starting point will be a data warehouse containing a
combination of internal data tracking all customers contact coupled with external market data ...
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When building a fact table, the final ETL step is converting the natural keys in the new input records
into the correct, contemporary surrogate keys. ETL maintains a special surrogate key lookup table
for each dimension. Tables Customers CusID | Lname | Fname | Address | City | State | Postal_Zone |
Phone_Number | E_Mail | C1 | Martinez | Erwin | 1234 Main St | Oregon ST | | 95123 | 408–891–
4574 | [email protected] | C2 | Smith | John | 2345 Sun St | San Jose | CA | 95130 | 408–234–
5678 | [email protected] | C3 | Lind | Kathy | 3564 Roberts Dr | New Orleans | LA | 54213 | 504–
657–8954 | [email protected] | Stores StoreID | Store_Name | Store_Address | Store_City |
Store_State | Store_Postal_code | Store_Phone | Store_Email | S1 | La Jolla |
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150. Data Warehousing And Cloud Computing
INTRODUCTION This paper clearly illustrates the concepts of Data warehousing and Cloud
computing. It also discusses the benefits and disadvantages of implementing Data Warehouse in a
Cloud. Both cloud computing and data warehousing are the latest trends in modern computing.
DWH is an integrated software component of the cloud and it provides timely support and accurate
response to complex queries with Online Analytical Processing (OLAP) and data mining tools.
Cloud computing provides reasonable speed of services in a less period of time when it is compared
to in–house data warehouse deployment. Reduced cost, a pay–per–use payment model and backups
are also made available by cloud computing. Besides these there are several challenges when
deploying data warehouses in the cloud. These challenges may include security, computational and
network problems. These problems are mainly caused often due to incompatible nature of functional
requirements to deploy DWH in the cloud environment. Cloud providers offer a low–end node for
computations and a local data warehousing systems is good in terms of CPU, memory and disk
bandwidth. Growing need of cloud computing will allow its evolution more in future to
accommodate critical DWH. The evolving nature of cloud computing and DWH will help the small
and medium sized businesses. EXPLANATION OF PAPER A common feature of data warehouse
on which most of the scholars agree upon is that a data warehouse acts as storage of historical data.
This
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154. Using Extract Transform Load ( Etl )
In today 's organizations, basic descision making procedures and day by day operations frequently
rely upon information that is put away in an assortment of information stockpiling frameworks,
arrangements, and areas. To transform this information into helpful business data, the information
commonly should be consolidated, purified, institutionalized, and compressed. For example, data
may be changed over to an alternate information sort or different database servers may store the
vital information utilizing diverse patterns. Dissimilarities like these must be settled before the
information can be effectively stacked to an objective target. After the plan and improvement of data
warehouse as per the business prerequisites, the way toward combining the information into the
information stockroom from different sources is to be thought of. Extract Transform Load (ETL)
procedures are basic in the achievement of the Data Warehousing ventures. The way toward
extricating information from one source (extract), changing it as per the outline of the data
warehouse(transform) and stacking it into data warehouse (load) constitute ETL. As it were, ETL is
the way toward extracting information from different information sources, changes it according to
the prerequisites of the target data warehouse and effectively stacking it into the data warehouse
(database). In the transformation procedure data is institutionalized to make it perfect with the target
database along with data purifying
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