Data warehouse concepts

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DATA WAREHOUSE
A data warehouse is the main repository of the organization's historical data, its corporate memory. For
example, an organization would use the information that's stored in its data warehouse to find out what day of the
week they sold the most widgets in May 1992, or how employee sick leave the week before the winter break
differed between California and New York from 2001-2005. In other words, the data warehouse contains the raw
material for management's decision support system. The critical factor leading to the use of a data warehouse is that
a data analyst can perform complex queries and analysis on the information without slowing down the operational
systems.

While operational systems are optimized for simplicity and speed of modification (online transaction processing,
or OLTP) through heavy use of database normalization and an entity-relationship model, the data warehouse is
optimized for reporting and analysis (on line analytical processing, or OLAP). Frequently data in data warehouses is
heavily denormalised, summarised and/or stored in a dimension-based model but this is not always required to
achieve acceptable query response times.
More formally, Bill Inmon (one of the earliest and most influential practitioners) defined a data warehouse as
follows:

Subject-oriented, meaning that the data in the database is organized so that all the data elements relating to the
same real-world event or object are linked together;

Time-variant, meaning that the changes to the data in the database are tracked and recorded so that reports can be
produced showing changes over time; obieefans.com

Non-volatile, meaning that data in the database is never over-written or deleted, once committed, the data is static,
read-only, but retained for future reporting;

Integrated, meaning that the database contains data from most or all of an organization's operational applications,
and that this data is made consistent History of data warehousing
Data Warehouses became a distinct type of computer database during the late 1980s and early 1990s. They were
developed to meet a growing demand for management information and analysis that could not be met by operational
systems. Operational systems were unable to meet this need for a range of reasons:
• The processing load of reporting reduced the response time of the operational systems,
• The database designs of operational systems were not optimized for information analysis and
reporting,
• Most organizations had more than one operational system, so company-wide reporting could not be
supported from a single system, and
• Development of reports in operational systems often required writing specific computer programs
which was slow and expensive.
As a result, separate computer databases began to be built that were specifically designed to support management
information and analysis purposes. These data warehouses were able to bring in data from a range of different data

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sources, such as mainframe computers, minicomputers, as well as personal computers and office automation
software such as spreadsheet, and integrate this information in a single place. This capability, coupled with user-
friendly reporting tools and freedom from operational impacts, has led to a growth of this type of computer system.
As technology improved (lower cost for more performance) and user requirements increased (faster data load cycle
times and more features), data warehouses have evolved through several fundamental stages:

Offline Operational Databases - Data warehouses in this initial stage are developed by simply copying the
database of an operational system to an off-line server where the processing load of reporting does not impact on the
operational system's performance.

Offline Data Warehouse - Data warehouses in this stage of evolution are updated on a regular time cycle (usually
daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented
data structure

Real Time Data Warehouse - Data warehouses at this stage are updated on a transaction or event basis, every time
an operational system performs a transaction (e.g. an order or a delivery or a booking etc.)

Integrated Data Warehouse - Data warehouses at this stage are used to generate activity or transactions that are
passed back into the operational systems for use in the daily activity of the organization.

DATA WAREHOUSE ARCHITECTURE

The term data warehouse architecture is primarily used today to describe the overall structure of a Business
Intelligence system. Other historical terms include decision support systems (DSS), management information
systems (MIS), and others.

The data warehouse architecture describes the overall system from various perspectives such as data, process, and
infrastructure needed to communicate the structure, function and interrelationships of each component. The
infrastructure or technology perspective details the various hardware and software products used to implement the
distinct components of the overall system. The data perspectives typically diagrams the source and target data
structures and aid the user in understanding what data assets are available and how they are related. The process
perspective is primarily concerned with communicating the process and flow of data from the originating source
system through the process of loading the data warehouse, and often the process that client products use to access
and extract data from the warehouse.

DATA STORAGE METHOTS
In OLTP - online transaction processing systems relational database design use the discipline of data
modeling and generally follow the Codd rules of data normalization in order to ensure absolute data integrity. Less
complex information is broken down into its most simple structures (a table) where all of the individual atomic level
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elements relate to each other and satisfy the normalization rules. Codd defines 5 increasing stringent rules of
normalization and typically OLTP systems achieve a 3rd level normalization. Fully normalized OLTP database
designs often result in having information from a business transaction stored in dozens to hundreds of tables.
Relational database managers are efficient at managing the relationships between tables and result in very fast insert/
update performance because only a little bit of data is affected in each relational transaction.

OLTP databases are efficient because they are typically only dealing with the information around a single
transaction. In reporting and analysis, thousands to billions of transactions may need to be reassembled imposing a
huge workload on the relational database. Given enough time the software can usually return the requested results,
but because of the negative performance impact on the machine and all of its hosted applications, data warehousing
professionals recommend that reporting databases be physically separated from the OLTP database.

In addition, data warehousing suggests that data be restructured and reformatted to facilitate query and analysis by
novice users. OLTP databases are designed to provide good performance by rigidly defined applications built by
programmers fluent in the constraints and conventions of the technology. Add in frequent enhancements, and to
many a database is just a collection of cryptic names, seemingly unrelated and obscure structures that store data
using incomprehensible coding schemes. All factors that while improving performance, complicate use by untrained
people. Lastly, the data warehouse needs to support high volumes of data gathered over extended periods of time
and are subject to complex queries and need to accommodate formats and definitions of inherited from
independently designed package and legacy systems.

Designing the data warehouse data Architecture synergy is the realm of Data Warehouse Architects. The goal of a
data warehouse is to bring data together from a variety of existing databases to support management and reporting
needs. The generally accepted principle is that data should be stored at its most elemental level because this provides
for the most useful and flexible basis for use in reporting and information analysis. However, because of different
focus on specific requirements, there can be alternative methods for design and implementing data warehouses.
There are two leading approaches to organizing the data in a data warehouse. The dimensional approach advocated
by Ralph Kimball and the normalized approach advocated by Bill Inmon. Whilst the dimension approach is very
useful in data mart design, it can result in a rats nest of long term data integration and abstraction complications
when used in a data warehouse.

In the "dimensional" approach, transaction data is partitioned into either a measured "facts" which are generally
numeric data that captures specific values or "dimensions" which contain the reference information that gives each
transaction its context. As an example, a sales transaction would be broken up into facts such as the number of
products ordered, and the price paid, and dimensions such as date, customer, product, geographical location and
salesperson. The main advantages of a dimensional approach is that the data warehouse is easy for business staff
with limited information technology experience to understand and use. Also, because the data is pre-joined into the
dimensional form, the data warehouse tends to operate very quickly. The main disadvantage of the dimensional
approach is that it is quite difficult to add or change later if the company changes the way in which it does business.
The "normalized" approach uses database normalization. In this method, the data in the data warehouse is stored in
third normal form. Tables are then grouped together by subject areas that reflect the general definition of the data

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(customer, product, finance, etc.). The main advantage of this approach is that it is quite straightforward to add new
information into the database -- the primary disadvantage of this approach is that because of the number of tables
involved, it can be rather slow to produce information and reports. Furthermore, since the segregation of facts and
dimensions is not explicit in this type of data model, it is difficult for users to join the required data elements into
meaningful information without a precise understanding of the data structure.

Subject areas are just a method of organizing information and can be defined along any lines. The traditional
approach has subjects defined as the subjects or nouns within a problem space. For example, in a financial services
business, you might have customers, products and contracts. An alternative approach is to organize around the
business transactions, such as customer enrollment, sales and trades.
Advantages of using data warehouse
There are many advantages to using a data warehouse, some of them are:
• Enhances end-user access to a wide variety of data.
• Business decision makers can obtain various kinds of trend reports e.g. the item with the most sales
in a particular area / country for the last two years.
A data warehouse can be a significant enabler of commercial business applications, most notably

Customer relationship management (CRM).
Concerns in using data warehouses
• Extracting, cleaning and loading data is time consuming.
• Data warehousing project scope must be actively managed to deliver a release of defined content and
value.
• Compatibility problems with systems already in place.
• Security could develop into a serious issue, especially if the data warehouse is web accessible.
• Data Storage design controversy warrants careful consideration and perhaps prototyping of the data
warehouse solution for each project's environments.

HISTORY OF DATA WAREHOUSING

Data warehousing emerged for many different reasons as a result of advances in the field of information systems.
A vital discovery that propelled the development of data warehousing was the fundamental differences between
operational (transaction processing) systems and informational (decision support) systems. Operational systems are
run in real time where in contrast informational systems support decisions on a historical point-in-time. Below is a
comparison of the two.
Characteristic Operational Systems (OLTP) Informational Systems (OLAP)
Primary Purpose Run the business on a current Support managerial decision
basis making

Type of Data Real time based on current data Snapshots and predictions
Primary Users Clerks, salespersons, Managers, analysts, customers
administrators
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Scope Narrow, planned, and simple Broad, complex queries and
updates and queries analysis

Design Goal Performance throughput, Ease of flexible access and use
availability
Database concept Complex simple

Normalization High Low
Time-focus Point in time Period of time
Volume Many - constant updates and Periodic batch updates and
queries on one or a few table queries requiring many or all
rows rows
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Other aspects that also contributed for the need of data warehousing are:
• Improvements in database technology
o The beginning of relational data models and relational database management systems (RDBMS)
• Advances in computer hardware
o The abundant use of affordable storage and other architectures
• The importance of end-users in information systems
o The development of interfaces allowing easier use of systems for end users
• Advances in middleware products
o Enabled enterprise database connectivity across heterogeneous platforms

Data warehousing has evolved rapidly since its inception. Here is the story timeline of data warehousing:
1970’s – Operational systems (such as data processing) were not able to handle large and frequent requests for data
analyses. Data stored was in mainframe files and static databases. A request was processed from recorded tapes for
specific queries and data gathering. This proved to be time consuming and an inconvenience.

1980’s – Real time computer applications became decentralized. Relational models and database management
systems started emerging and becoming the wave. Retrieving data from operational databases still a problem
because of “islands of data.”

1990’s – Data warehousing emerged as a feasible solution to optimize and manipulate data both internally and
externally to allow business’ to make accurate decisions.
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After information technology took the world by storm, there were many revolutionary concepts that were created to
make it more effective and helpful. During the nineties as new technology was being born and was becoming
obsolete in no time, there was a need for a concrete fool proof idea that can help database administration more
secure and reliable. The concept of data warehousing was thus, invented to help the business decision making
process. The working of data warehousing and its applications has been a boon to information technology
professionals all over the world. It is very important for all these managers to understand the architecture of how it
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works and how can it be used as a tool to improve performance. The concept has revolutionized the business
planning techniques. obieefans.com

Concept
Information processing and managing a database are the two important components for any business to have a
smooth operation. Data warehousing is a concept where the information systems are computerized. Since there
would be a lot of applications that run simultaneously, there is a possibility that each individual processes create an
exclusive “secondary data” which originates from the source. The data warehouses are useful in tracking all the
information down and are useful in analyzing this information and improve performance. They offer a wide variety
of options and are highly compatible to virtually all working environments. They help the managers of companies to
gauge the progress that is made by the company over a period of time and also explore new ways to improve the
growth of the company. There are many “it’s” in business and these data warehouses are read only integrated
databases that help to answer these questions. They are useful to form a structure of operations and analyze the
subject matter on a given time period.

The structure
As is the case with all computer applications there are various steps that are involved in planning a data warehouse.
The need is analyzed and most of the time the end user is taken into consideration and their input forms an
invaluable asset in building a customized database. The business requirements are analyzed and the “need” is
discovered. That would then become the focus area. If a company wants to analyze all its records and use the
research in improving performance.

A data warehouse allows the manager to focus on this area. After the need is zeroed in on then a conceptual data
model is designed. This model is then used a basic structure that companies follow to build a physical database
design. A number of iterations, technical decisions and prototypes are formulated. Then the systems development
life cycle of design, development, implementation and support begins.

Collection of data
The project team analyzes various kinds of data that need to go into the database and also where they can find all
this information that they can use to build the database. There are two different kinds of data. One which can be
found internally in the company and the other is the data that comes from another source. There would be another
team of professionals who would work on the creation, extraction programs that are used to collect all the
information that is needed from a number of databases, Files or legacy systems. They identify these sources and ten
copy them onto a staging area outside the database. They clean all the data which is described as cleansing and make
sure that it does not contain any errors. They copy all the data into his data warehouse. This concept of data
extraction from the source and the selection, transformation processes have been unique benchmarks of this concept.
This is very important for the project to become successful. A lot of meticulous planning is involved in arriving at a
step by step configuration of all the data from the source to the data warehouse.

Use of metadata
The whole process of extracting data and collecting it to make it effective component in the operation requires
“metadata”. The transformation of an analytical system from an operational system is achieved only with maps of

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Meta data. The transformational data includes the change in names, data changes and the physical characteristics
that exist. It also includes the description of the data, its brigand updates. Algorithms are used in summarizing the
data.Meta data provides graphical user interface that helps the non-technical end users. This offers richness in
navigation and accessing the database. There is other form of Meta data called the operational Meta data. This forms
the fundamental structure of accessing the procedures and monitoring the growth of data warehouse in relation with
the available storage space. It also recognizes who would be responsible to access the data in the warehouse and in
operational systems.

Data marts-specific data
In every data base systems, there is a need for updation. Some of them do it by the day and some by the minute.
However if a specific department needs to monitor its own data in sync with the overall business process. They store
it as data marts. These are not as big as data arehouse and are useful for storing the data and the information of a
specific business module. The latest trend in data warehousing is to develop smaller data marts and then manage
each of them individually and later integrate them into the overall business structure.

Security and reliability Similar to information system, trustworthiness of data is determined by the trustworthiness
of the hardware, software, and the procedures that created them. The reliability and authenticity of the data and
information extracted from the warehouse will be a function of the reliability and authenticity of the warehouse and
the various source systems that it encompasses.
In data warehouse environments specifically, there needs to be a means to ensure the integrity of data first by having
procedures to control the movement of data to the warehouse from operational systems and second by having
controls to protect warehouse data from unauthorized changes. Data warehouse trustworthiness and security are
contingent upon acquisition, transformation and access metadata and systems documentation

The basic need for every data base is that it needs to be secure and trustworthy. This is determined by the hardware
components of the system the reliability and authenticity of the data and information extracted from the warehouse
will be a function of the reliability and authenticity of the warehouse and the various source systems that it
encompasses. In data warehouse environments specifically, there needs to be a means to ensure the integrity of data
first by having procedures to control the movement of data to the warehouse from operational systems and second
by having controls to protect warehouse data from unauthorized changes. Data warehouse trustworthiness and
security are contingent upon acquisition, transformation and access metadata and systems documentation.

Han and Kamber (2001) define a data warehouse as “A repository of information collected from multiple sources,
stored under a unified scheme, and which usually resides at a single site.”
In educational terms, all past information available in electronic format about a school or district such as budget,
payroll, student achievement and demographics is stored in one location where it can be accessed using a single set
of inquiry tools.

These are some of the drivers that have been created to initiate data warehousing.

• CRM: Customer relationship management .there is a threat of losing customers due to poor quality and sometimes
those unknown reasons that nobody ever explored. As a result of direct competition, this concept of customer
relationship management has been on the forefront to provide the solutions. Data warehousing techniques have
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helped this cause enormously. Diminishing profit margins: Global competition has forced many companies that
enjoyed generous profit margins on their products to reduce their prices to remain competitive. Since cost of goods
sold remains constant, companies need to manage their operations better to improve their operating margins

• Data warehouses enable management decision support for managing business operations. Retaining the existing
customers has been the most important feature of present day business. To facilitate good customer relationship
management companies are investing a lot of money to find out the exact needs of the consumer. As a result of this
direct competition the concept of customer relationship management came into existence. Data warehousing
techniques have helped this cause enormously. Diminishing profit margins: Global competition has forced many
companies that enjoyed generous profit margins on their products to reduce their prices to remain competitive. Since
cost of goods sold remains constant, companies need to manage their operations better to improve their operating
margins. Data warehouses enable management decision support for managing business operations.

• Deregulation: the ever growing competition and the diminishing profit margins have made companies to explore
various new possibilities to play the game better. A company develops in one direction and establishes a particular
core competency in the market. After they have their own speciality, they look for new avenues to go into a new
market with a completely new set of possibilities. For a company to venture into developing a new core competency,
the concept of deregulation is very important. . Data warehouses are used to provide this information. Data
warehousing is useful in generating a cross reference data base that would help companies to get into cross selling.
this is the single most effective way that this can hap

• The complete life cycle. The industry is very volatile where we come across a wide range of new products every
day and then becoming obsolete in no time. The waiting time for the complete lifecycle often results in a heavy loss
of resources of the company. There was a need to build a concept which would help in tracking all the volatile
changes and update them by the minute. This allowed companies to be extra safe In regard to all their products. The
system is useful in tracking all the changes and helps the business decision process to a great deal. These are also
described as business intelligence systems in that aspect.

Merging of businesses: As described above, as a direct result of growing competition, companies join forces to
carve a niche in a particular market. This would help the companies to work towards a common goal with twice the
number of resources. In case of such an event, there is a huge amount of data that has to be integrated. This data
might be on different platforms and different operating systems. To have a centralized authority over the data, it is
important that a business tool has to be generated which not only is effective but also reliable. Data warehousing fits
the need Relevance of Data Warehousing for organizations Enterprises today, both nationally and globally, are in
perpetual search of competitive advantage. An incontrovertible axiom of business management is that information is
the key to gaining this advantage. Within this explosion of data are the clues management needs to define its market
strategy. Data Warehousing Technology is a means of discovering and unearthing these clues, enabling
organizations to competitively position themselves within market sectors. It is an increasingly popular and powerful
concept of applying information technology to solving business problems. Companies use data warehouses to store
information for marketing, sales and manufacturing to help managers get a feel for the data and run the business
more effectively. Managers use sales data to improve forecasting and planning for brands, product lines and

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business areas. Retail purchasing managers use warehouses to track fast-moving lines and ensure an adequate supply
of high-demand products. Financial analysts use warehouses to manage currency and exchange exposures, oversee
cash flow and monitor capital expenditures.

Data warehousing has become very popular among organizations seeking competitive advantage by getting strategic
information fast and easy (Adhikari, 1996). The reasons for organizations for having a data warehouse can be
grouped into four sections:

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• Warehousing data outside the operational systems:
The primary concept of data warehousing is that the data stored for business analysis can most effectively be
accessed by separating it from the data in the operational systems. Many of the reasons for this separation has
evolved over the years. A few years before legacy systems archived data onto tapes as it became inactive and many
analysis reports ran from these tapes or data sources to minimize the performance on the operational systems.

• Integrating data from more than one operational system:
Data warehousing are more successful when data can be combined from more than one operational system. When
data needs to be brought together from more than one application, it is natural that this integration be done at a place
independent of the source application. Before the evolution of structured data warehouses, analysts in many
instances would combine data extracted from more than one operational system into a single spreadsheet or a
database. The data warehouse may very effectively combine data from multiple source applications such as sales,
marketing, finance, and production.

• Data is mostly volatile:
Another key attribute of the data in a data warehouse system is that the data is brought to the warehouse after it has
become mostly non-volatile. This means that after the data is in the data warehouse, there are no modifications to be
made to this information.

• Data saved for longer periods than in transaction systems:
Data from most operational systems is archived after the data becomes inactive. For example, an order may become
inactive after a set period from the fulfillment of the order; or a bank account may become inactive after it has been
closed for a period of time. The primary reason for archiving the inactive data has been the performance of the
operational system. Large amounts of inactive data mixed with operational live data can significantly degrade the
performance of a transaction that is only processing the active data. Since the data warehouses are designed to be the
archives for the operational data, the data here is saved for a very long period.

Advantages of data warehouse:
There are several advantages of data warehousing. When companies have a problem that requires necessary changes
in their transaction, they need the information and the transaction processing to make a decision.

• Time reduction

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"The warehouse has enabled employee to shift their time from collecting information to analyzing it and that helps
the company make better business decisions" A data warehouse turns raw information into a useful analytical tool
for business decision-making. Most companies want to get the information or transaction processing quickly in
order to make a decision-making. If companies are still using traditional online transaction processing systems, it
will take longer time to get the information that needed. As a result, the decision-making will be made longer, and
the companies will lose time and money. Data warehouse also makes the transaction processing easier.
• Efficiency
In order to minimize inconsistent reports and provide the capability for data sharing, the companies should provide a
database technology that is required to write and maintain queries and reports. A data warehouse provides, in one
central repository, all the metrics necessary to support decision-making throughout the queries and reports. Queries
and reports make the management processing be efficient.

• Complete Documentation
A typical data warehouse objective is to store all the information including history. This objective comes with its
own challenges. Historical data is seldom kept on the operational systems; and, even if it is kept, rarely is found in
three or five years of history in one file. There are some reasons why companies need data warehouse to store
historical data.

• Data Integration
Another primary goal for all data warehouses is to integrate data, because it is a primary deficiency in current
decision support. Another reason to integrate data is that the data content in one file is at a different level of
granularity than that in another file or that the same data in one file is updated at a different time period than that in
another file.

Limitations:
Although data warehouse brings a lot of advantages to corporate, there are some disadvantages that apply to data
warehouse.

• High Cost
Data warehouse system is too expensive. According to Phil Blackwood, “with the average cost of data warehouse
systems valued at$1.8 million”. This limits small companies to buy data warehouse system. As a result, only big
companies can afford to buy it. It means that not all companies have proper system to store data and transaction
system databases.
Furthermore, because small companies do not have data warehouse, then it causes difficulty for small companies to
store data and information in the system that may causes small companies to organize the data as one of the
requirement for the company will grow.

• Complexity
Moreover, data warehouse is very complex system. The primary function of data warehouse is to integrate all the
data and the transaction system database. Because integrate the system is complicated, data warehousing can
complicate business process significantly. For example, small change in the transaction processing system may have
major impacts on all transaction processing system. Sometimes, adding, deleting, or changing the data and
transaction can causes time consuming. The administrator need to control and check the correctness of changing

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transaction in order to impact on other transaction. Therefore, complexity of data warehouse prevents the companies
from changing the data or transaction that are necessary to make.

Opportunities and Challenges for Data Warehousing
Data warehousing is facing tremendous opportunities and challenges which to a greater part decide the most
immediate developments and future trends. Behind all these probable happenings is the impact that the Internet has
upon ways of doing business and, consequently, upon data warehousing—a more and more important tool for
today’s and future’s organizations and enterprises. The opportunities and challenges for data warehousing are
mainly reflected in four aspects.

• Data Quality

Data warehousing has unearthed many previously hidden data-quality problems. Most companies have attempted
data warehousing and discovered problems as they integrate information from different business units. Data that was
apparently adequate for operational systems has often proved to be inadequate for data warehouses (Faden, 2000).
On the other hand, the emergence of E-commerce has also opened up an entirely new source of data-quality
problems. As we all know, data, now, may be entered at a Web site directly by a customer, a business partner, or, in
some cases, by anyone who visits the site. They are more likely to make mistakes, but, in most cases, less likely to
care if they do. All these are “elevating data cleansing from an obscure, specialized technology to a core requirement
for data warehousing, cusomer-relationship management, and Web-based commerce “

• Business Intelligence

The second challenge comes from the necessity of integrating data warehousing with business intelligence to
maximize profits and competency. We have been witnessing an ever-increasing demand to deploy data warehousing
structures and business intelligence. The primary purpose of the data warehouse is experiencing a shift from a focus
on data transformation into information to—most recently—transformation into intelligence.

All the way down this new development, people will expect more and more analytical function of the data
warehouse. The customer profile will be extended with psycho-graphic, behavioral and competitive ownership
information as companies attempt to go beyond understanding a customer’s preference. In the end, data warehouses
will be used to automate actions based on business intelligence. One example is to determine with which supplier
the order should be placed in order to achieve delivery as promised to the customer.

• E-business and the Internet

Besides the data quality problem we mentioned above, a more profound impact of this new trend on data
warehousing is in the nature of data warehousing itself.
On the surface, the rapidly expanding e-business has posed a threat to data warehouse practitioners. They may be
concerned that the Internet has surpassed data warehousing in terms of strategic importance to their company, or that
Internet development skills are more highly valued than those for data warehousing. They may feel that the Internet
and e-business have captured the hearts and minds of business executives, relegating data warehousing to ‘second
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class citizen’ status. However, the opposite is true.

• Other trends

While data warehousing is facing so many challenges and opportunities, it also brings opportunities for other
fields. Some trends that have just started are as follows:
• More and more small-tier and middle-tier corporations are looking to build their own decision support systems.
• The reengineering of decision support systems more often than not end up with the architecture that would help
fuel the growth of their decision support systems.
• Advanced decision support architectures proliferate in response to companies’ increasing demands to integrate
their customer relationship management and e-business initiatives with their decision support systems.
• More organizations are starting to use data warehousing meta data standards, which allow the various decision
support tools to share their data with one another.

Architectural Overview
In concept the architecture required is relatively simple as can be seen from the diagram below:
Source System(s)
Data
Mart
ETL
Transaction Repository
ETL
ETL
ETL
Reporting Tools
Data
Mart
Data
Mart
Figure 1 - Simple Architecture
However this is a very simple design concept and does not reflect what it takes to implement a data warehousing
solution. In the next section we look not only at these core components but the additional elements required to make
it all work.
White Paper - Overview Architecture for Enterprise Data Warehouses

Components of the Enterprise Data Warehouse
The simple architecture diagram shown at the start of the document shows four core components of an enterprise
data warehouse. Real implementations however often have many more depending on the circumstances. In this
section we look first at the core components and then look at what other additional components might be needed.
The core components

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The core components are those shown on the diagram in Figure 1 – Simple Architecture. They are the ones that are
most easily identified and described.

Source Systems
The first component of a data warehouse is the source systems, without which there would be no data. These provide
the input into the solution and will require detailed analysis early in any project. Important considerations in looking
at these systems include:

Is this the master of the data you are looking for?
Who owns/manages/maintains this system?
Where is the source system in its lifecycle?
What is the quality of the data in the system?
What are the batch/backup/upgrade cycles on the system?
Can we get access to it?

Source systems can broadly be categorised in five types:
On-line Transaction Processing (OLTP) Systems These are the main operational systems of the business and will
normally include financial systems, manufacturing systems, and customer relationship management (CRM) systems.
These systems will provide the core of any data warehouse but, whilst a large part of the effort will be expended on
loading these systems it is the integration of the other sources that provides the value. Legacy Systems Organisations
will often have systems that are at the end of their life, or archives of de-commissioned systems. One of the business
case justifications for building a data warehouse may have been to remove these systems after the critical data has
been moved into the data warehouse. This sort of data often adds to the historical richness of a solution.
Missing or Source-less Data
During the analysis it is often the case that data is identified as required but for which no viable source exists, e.g.
exchange rates used on a given date or corporate calendar events, a source that is unusable for loading such as a
document, or just that the answer is
in someone.s head. There is also data required for the basic operation such as descriptions of codes.

This is therefore an important category, which is frequently forgotten during the initial design stages, and then
requires a last minute fix into the system, often achieved by direct manual changes to the data warehouse. The down
side of this approach is that it loses the tracking, control and auditability of the information added to the warehouse.
Our advice is therefore to create a system or systems that we call the Warehouse Support Application (WSA). This
is normally a number of simple data entry type forms that can capture the data required. This is then treated as
another OLTP source and managed in the same way. Organisations are often concerned about how much of this they
will have to build. In reality it is a reflection of the level of good data capture during the existing business process
and current systems. If
these are good then there will be little or no WSA components to build but if they are poor then significant
development will be required and this should also raise a red flag about the readiness of the organisation to
undertake this type of build.

Transactional Repository (TR)
The Transactional Repository is the store of the lowest level of data and thus defines the scope and size of

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the database. The scope is defined by what tables are available in the data model and the size is defined by the
amount of data put into the model. Data that is loaded here will be clean, consistent, and time variant. The design of
the data model in this area is critical to the long term success of the data warehouse as it determines the scope and
the cost of changes, makes mistakes expensive and inevitably causes delays.

As can be seen from the architecture diagram the transaction repository sits at the heart of the system; it is the
point where all data is integrated and the point where history is held. If the model, once in production, is missing key
business information and can not easily be xtended when the requirements or the sources change then this will mean
significant rework. Avoiding this cost is a factor in the choice of design for this data Model.

In order to design the Transaction Repository there are three data modelling approaches that can be identified.
Each lends itself to different organisation types and each has its own advantages and disadvantages, although a
detailed discussion of these is outside the scope of this document.
The three approaches are:

Enterprise Data Modelling (Bill Inmon)
This is a data model that starts by using conventional relational modelling techniques and often will describe the
business in a conventional normalised database. There may then be a series of de-normalisations for performance
and to assist extraction into the
data marts.
This approach is typically used by organisations that have a corporate-wide data model and strong central
control by a group such as a strategy team. These organisations will tend also to have more internally developed
systems rather than third party products.
Data Bus (Ralph Kimball)
The data model for this type of solution is normally made up of a series of star schemas that have evolved over time,
with dimensions becoming .conformed. as they are re-used. The transaction repository is made up of these base star
schemas and their associated dimensions. The data marts in the architecture will often just be views either directly
onto these schemas or onto aggregates of these star schemas. This approach is particularly suitable for companies
which have evolved from a number of independent data marts and growing and evolving into a more mature data
warehouse environment. Process Neutral Model

A Process Neutral Data Model is a data model in which all embedded business rules have been removed.
If this is done correctly then as business processes change there should be little or no change required to the data
model. Business Intelligence solutions designed around such a model should therefore not be subject to limitations
as the business changes.

This is achieved both by making many relationships optional and have multiple cardinality, and by carefully making
sure the model is generic rather then reflecting only the views and needs of one or more specific business areas.
Although this sounds simple (and it is once you get used to it) in reality it takes a little while to fully understand and
to be able to achieve. This type of data model has been used by a number of very large organisations where it
combines some of the best features of both the data bus approach and enterprise data modelling. As with enterprise
data modelling it sets out to describe the entire business
but rather than normalise data it uses an approach that embeds the metadata (or data about data) in the data model

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and often contains natural star schemas. This approach is generally used by large corporations that have one or more
of the following attributes: many legacy systems, a number of systems as a result of business acquisitions, no central
data model, or
a rapidly changing corporate environment.

Data Marts

The data marts are areas of a database where the data is organised for user queries, reporting and analysis.
Just as with the design of the Transaction Repository there are a number of design types for data mart. The choice
depends on factors such as the design of transaction repository and which tools are to be used to query the data
marts.
The most commonly used models are star schemas and snowflake schemas where direct database access is made,
whilst data cubes are favoured by some tool vendors. It is also possible to have single table solution sets if this meets
the business requirement. There is no need for all data marts to have the same design type, as they are user facing it
is important that they are fit for purpose for the user and not what suits a purist architecture.

Extract . Transform- Load (ETL) Tools
ETL tools are the backbone of the data warehouse, moving data from source to transaction repository

and on to data marts. They must deal with issues of performance of load for large volumes and with complex
transformation of data, in a repeatable, scheduled environment. These tools build the interfaces between components
in the architecture and will also often work with data cleansing elements to ensure that the most accurate data is
available. The need for a standard approach to ETL design within a project is paramount. Developers will often
create an intricate and complicated solution for which there is a simple solution, often requiring little compromise.
Any compromise in the deliverable is usually accepted by the
business once they understand these simple approaches will save them a great deal of cash in terms of time taken to
design, develop, test and ultimately support.

Analysis and Reporting Tools
Collecting all of the data into a single place and making it available is useless without the ability for users to access
the information. This is done with a set of analysis and reporting tools. Any given data warehouse is likely to have
more than one tool. The types of tool can be qualified in broadly four categories:

Simple reporting tools that either produce fixed or simple parameterised reports.
Complex ad hoc query tools that allow users to build and specify their own queries.
Statistical and data mining packages that allow users to delve into the information contained within the data.
.What-if. tools that allow users to extract data and then modify it to role play or simulate scenarios.
Additional Components
In addition to the core components a real data warehouse may require any or all of these components to deliver the
solution. The requirement to use a component should be considered by each programme on its own merits.

Literal Staging Area (LSA)

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Occasionally, the implementation of the data warehouse encounters environmental problems, particularly with
legacy systems (e.g. a mainframe system, which is not easily accessible by applications and tools). In this case it
might be necessary to implement a Literal Staging
Area, which creates a literal copy of the source system.s content but in a more convenient environment (e.g. moving
mainframe data into an ODBC accessible relational database). This literal staging area then acts as a surrogate for
the source system for use by the downstream ETL interfaces.
There are some important benefits associated with implementing an LSA:

It will make the system more accessible to downstream ETL products.
It creates a quick win for projects that have been trying to get data off, for example a Mainframe, in a more
laborious fashion.
It is a good place to perform data quality profiling.
It can be used as a point close to the source to perform data quality cleaning.
Transaction Repository Staging Area (TRS)
ETL loading will often need an area to put intermediate data sets, or working tables, Somewhere which for clarity
and ease of management should not be in the same area as the main model. This area is used when bringing data
from a source system or its surrogate into the transaction repository.

Data Mart Staging Area (DMS)
As with the transaction repository staging area there is a need for space between the transaction repository and data
marts for intermediate data sets. This area provides that space.
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Operational Data Store (ODS)
An operational data store is an area that is used to get data from a source and, if required, lightly aggregate it to
make it quickly available. This is required for certain types of reporting which need to be available in .realtime .
(updated within 15 minutes) or .near-time. (for example 15 to 60 minutes old). The ODS will not normally clean,
integrate, or fully aggregate
data (as the data warehouse does) but it will provide rapid answers, and the data will then become available via the
data warehouse once the cleaning, integration and aggregation has taken place in the next batch cycle.

Tools & Technology
The component diagrams above show all the areas and the elements needed. This translates into a significant list of
tools and technology that are required to build and operationally run a data warehouse solution. These include:
Operating system
Database
Backup and Recovery
Extract, Transform, Load (ETL)
Data Quality Profiling
Data Quality Cleansing
Scheduling
Analysis & Reporting
Data Modelling
Metadata Repository

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Source Code Control
Issue Tracking
Web based solution integration
The tools selected should operate together to cover all of these areas. The technology choices will also be influenced
by whether the organisation needs to operate a homogeneous
(all systems of the same type) or heterogeneous (systems may be of differing types)
environment, and also whether the solution is to be centralised or distributed.

Operating System
The server side operating system is usually an easy decision, normally following the recommendation in the
organisation.s Information System strategy. The operating system choice for enterprise data warehouses tends to be
a Unix/Linux variant, although some organisations do use Microsoft operating systems. It is not the purpose of this
paper to make any recommendations for the above and the choice should be the result of the organisation.s normal
procurement procedures.

Database
The database falls into a very similar category to the operating system in that for most organisations it is a given
from a select few including Oracle, Sybase, IBM DB2 or Microsoft SQLServer.
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Backup and Recovery
This may seem like an obvious requirement but is often overlooked or slipped in at the
end. From .Day 1. of development there will be a need to backup and recover the databases from time to time. The
backup poses a number of issues:
Ideally backups should be done whilst allowing the database to stay up.
It is not uncommon for elements to be backed up during the day as this is the point of least load on the system and it
is often read-only at that point.
It must handle large volumes of data.
It must cope with both databases and source data in flat files.
The recovery has to deal with the related consequence of the above:
Recovery of large databases quickly to a point in time.

Extract - Transform - Load (ETL)

The purpose of the extract, transform and load (ETL) software, to create interfaces, has been described above and is
at the core of the data warehouse. The market for such tools is constantly moving, with a trend for database vendors
to include this sort of technology in their core product. Some of the considerations for selection of an ETL tool
include:

Ability to access source systems
Ability to write to target systems
Cost of development (it is noticeable that some of the easy to deploy and operate tools are not easy to develop with)
Cost of deployment (it is also noticeable that some of the easiest tools to develop with are not easy to deploy or

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operate)
Integration with scheduling tools Typically, only one ETL is needed, however it is common for specialist tools to be
used from a source system to a literal staging area as a way of overcoming a limitation in the main ETL

Data Quality Profiling
Data profiling tools look at the data and identify issues with it. It does this by some of the following techniques:
Looking at individual values in a column to check that they are valid Validating data types within a column
Looking for rules about uniqueness or frequencies of certain values
Validating primary and foreign key constraints +++++
Validating that data within a row is consistent
Validating that data is consistent within a table
Validating that data is consistent across tables etc.
This is important for both the analysts when examining the system and developers
when building the system. It also will identify data quality cleansing rules that can be applied to the data before
loading. It is worth noting that good analysts will often do this without tools especially if good analysis templates
are available.

Data Quality Cleansing
This tool updates data to improve the overall data quality, often based on the output of the data quality profiling tool.
There are essentially two types of cleansing tools:

Rule-based cleansing; this performs updates on the data based on rules (e.g. make everything uppercase; replace
two spaces with a single space, etc.). These rules can be very simple or quite complex depending on the tool used
and the business requirement.

Heuristic cleansing; this performs cleansing by being given only an approximate method of solving the problem
within the context of some goal, and then uses feedback from the effects of the solution to improve its own
performance. This is commonly used for address matching type problems.
An important consideration when implementing a cleansing tool is that the process should be performed as closely
as possible to the source system. If it is performed further downstream, data will be repeatedly presented for
cleansing.

Scheduling
With backup, ETL and batch reporting runs the data warehouse environment has a large number of jobs to be
scheduled (typically in the hundreds per day) with many Dependencies, for example:

.The backup can only start at the end of the business day and provided that the source system has generated a flat
file, if the file does not exist then it must poll for thirty minutes to see if it arrives otherwise notify an operator. The
data mart load can not start until the transaction repository load is complete but then can run six different data mart
loads in parallel.

This should be done via a scheduling tool that integrates into the environment.

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Analysis & Reporting
The analysis and reporting tools are the user.s main interface into the system. As has already been discussed there
are four main types
Simple reporting tools
Complex ad hoc query tools
Statistical and data mining packages
What-if tools
Whilst the market for such tools changes constantly the recognised source of
information is The OLAP Report2.

Data Modelling
With all the data models that have been discussed it is obvious that a tool in which to
build data models is required. This will allow designers to graphically manage data models and generate the code to
create the database objects. The tool should be capable of both logical and physical data modelling. Metadata
Repository Metadata is data about data. In the case of the data warehouse this will include information about the
sources, targets, loading procedures, when those procedures were run, and information about what certain terms
mean and how they relate to the data in the database. The metadata required is defined in a subsequent section on
documentation however the information itself will need to be held somewhere. Most tools have some elements of a
metadata repository but there is a need to identify what constitutes the entire repository by identifying which parts
are held in which tools.
2 The OLAP Report by Nigel Pendse and Richard Creeth is an independent research resource for organizations
buying and implementing OLAP applications.

Source Code Control
Up to this point you will have noticed that we have steadfastly remained vendor independent and we remain so
here. However the issue of source control is one of the biggest impacts on a data warehouse. If the tools that you use
do not have versioning control or if your tools do not integrate to allow version control across them and your
organisation does not have a source code control tool then download and use CVS, it is free, multi-platform and we
have found can be made to work with most of the tools in other categories. There are also Microsoft Windows
clients for CVS and web based tools for CVS available.

Issue Tracking
In a similar vein to the issue of source code control most projects do not deal with issue tracking well. The worst
nightmare being a spreadsheet that is mailed around once a week to get updates. We again recommend that if a
suitable tool is not already available then you consider an open source tool called Bugzilla.

Web Based Solution Integration
Running a programme such as the one described will bring much information together. It is important to bring
everything together in an accessible fashion. Fortunately web technologies provide an easy way to do this.
An ideal environment would allow communities to see some or all of the following via a secure web based interface:
Static reports
Parameterised reports
Web based reporting tools

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Balanced Scorecards
Analysis
Documentation
Requirements Library
Business Terms Definitions
Schedules
Metadata Reports
Data Quality profiles
Data Quality rules
Data Quality Reports
Issue tracking
Source code
There are two similar but different technologies that are available to do this depending on the corporate approach or
philosophy:
Portals: these provide personalised websites and make use of distributed applications to provide a collaborative
workspace.
Wiki3: which provide a website that allows users to easily add and edit contents and link to other web applications

Both can be very effective in developing common understanding of what the data warehouse does and how it
operates which in turn leads to a more engaged user community and greater return on investment.
3 A wiki is a type of website that allows users to easily add and edit content and is especially suited for collaborative
writing. In essence, wiki is a simplification of the process of creating HTML web pages combined with a system that
records each individual change that occurs over time, so that at any time, a page can be reverted to any of its
previous states. A wiki system may also provide various tools that allow the user community to easily monitor the
constantly changing state of the wiki and discuss the issues that emerge in trying to achieve a general consensus
about wiki content.

Documentation Requirements

Given the size and complexity of the Enterprise Data Warehouse, a core set of documentation is required,
which is described in the following section. If a structured project approach is adopted, these documents would be
produced as a natural byproduct however we would recommend the following set of documents as a minimum. To
facilitate this, at Data Management & Warehousing, we have developed our own set of templates for this purpose.

Requirements Gathering
This is a document managed using a word-processor.
Timescales: At start of project 40 days effort plus on-going updates.
There are four sections to our requirement templates:
Facts: these are the key figures that a business requires. Often these will be associated with Key Performance
Indicators (KPIs) and the information required to calculate them i.e. the Metrics required for running the company.
An example of a fact might be the number of products sold in a store.
Dimensions: this is the information used to constrain or qualify the facts. An example of this might be the list of

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products or the date of a transaction or some attribute of the customer who purchased product.
Queries: these are the typical questions that a user might want to ask for example .How many cans of soft drink
were sold to male customers on the 2nd February?. This uses information from the requirements sections on
available facts and dimensions. Non-functional: these are the requirements that do not directly relate to the data,
such as when must the system be available to users, how often does it need to be refreshed, what quality metrics
should be recorded about the data, who should be able to access it, etc.
Note that whilst an initial requirements document will come early in the project it will undergo a number of versions
as the user community matures in both its use and understanding of the system and data available to it. Key Design
Decisions This is a document managed using a word-processor.
Timescales: 0.5 days effort as and when required.
This is a simple one or two page template used to record the design decisions that are
made during the project. It contains the issue, the proposed outcome, any counterarguments
and why they were rejected and the impact on the various teams within the
project. It is important because given the long term nature of such projects there is
often a revisionist element that queries why such decisions were made and spends
time revisiting them.

Data Model
This is held in the data modelling tool.s internal format.
Timescales: At start of project 20 days effort plus on-going updates. Both logical and physical data models will be
required. The logical data model is an abstract representation of a set of data entities and their relationship, usually
including their key attributes. The logical data model is intended to facilitate analysis of the function of the data
design, and is not intended to be a full representation of the physical database. It is typically produced early in
system design, and it is frequently a precursor to the physical data model that documents the actual implementation
of the database.

In parallel with the gathering of requirements the data models for the transaction repository and the initial data marts
will be developed. These will be constantly maintained throughout the life of the solution.

Analysis
These are documents managed using a word-processor. The analysis phase of the project is broken down into three
main templates, each serving as a step in the progression of understanding required to build the system. During the
system analysis part of the project, the following three areas must be covered and documented:

Source System Analysis (SSA)
Timescales: 2-3 days effort per source system.
This is a simple high-level overview of each source system to understand its value as a potential source of business
information, and to clarify its ownership and longevity. This is normally done for all systems that are potential
sources. As the name implies this looks at the .system. level and identifies .candidate. systems.
These documents are only updated at the start of each phase when candidate systems are being identified.

Source Entity Analysis (SEA)
Timescales: 7-10 days effort per system.

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This is a detailed look at the .candidate. systems, examining the data, the data quality issues, frequency of update,
access rights, etc. The output is a list of tables and fields that are required to populate the data warehouse. These
documents are updated at the start of each phase when candidate systems are being examined and as part of the
impact analysis of any upgrades to a system that has been used for a previous phase and is being upgraded.

Target Oriented Analysis (TOA)
Timescales: 15-20 days effort for the Transaction Repository, 3-5 days effort for each data mart. This is a document
that describes the mappings and transformations that are required to populate a target object. It is important that this
is target focused as a common failing is to look at the source and ask the question .Where do I put all these bits of
information ?. rather than the correct question which is .I need to populate this object where do I get the information
from ?.

Operations Guide
This is a document managed using a word-processor. Timescales: 20 days towards the end of the development
phase. This document describes how to operate the system; it will include the schedule for running all the ETL jobs,
including dependencies on other jobs and external factors such as the backups or a source system. It will also
include instructions on how to
recover from failure and what the escalation procedures for technical problem resolution are. Other sections will
include information on current sizing, predicted growth and key data inflection points (e.g. year end where there are
a particularly large number of journal entries) It will also include the backup and recovery plan identifying what
should be backed up and how to perform system recoveries from backup. Security Model This is a document
managed using a word-processor.
Timescales: 10 days effort after the data model is complete, 5 days effort toward the development phase.
This document should identify who can access what data when and where. This can be a complex issue, but the
above architecture can simplify this as most access control needs to be around the data marts and nearly everything
else will only be visible to the ETL tools extracting and loading data into them.
Issue log
This is held in the issue logging system’s internal format.
Timescales: Daily as required.
As has already been identified the project will require an issue log that tracks issues during the development and
operation of the system.

Metadata
There are two key categories of metadata as discussed below:
Business Metadata
This is a document managed using a word-processor or a Portal or Wiki if available.
Business Definitions Catalogue4
Timescales: 20 days effort after the requirements are complete and ongoing maintenance.
This is a catalogue of business terms and their definitions. It is all about adding context to data and making meaning
explicit and providing definitions to business terms, data elements, acronyms and abbreviations. It will often include
information about who owns the definition and who maintains it and where appropriate what formula is required to

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calculate it. Other useful elements will include synonyms, related terms and preferred terms. Typical examples can
include definitions of business terms such as .Net Sales Value. or .Average revenue per customer. as well as
definitions of hierarchies and common terms such as customer.
Technical Metadata This is the information created by the system as it is running. It will either be held in server log
files or databases.

Server & Database availability
This includes all information about which servers and databases were available when and serves two purposes,
firstly monitoring and management of service level agreements (SLAs) and secondly performance optimisation to fit
the ETL into the available batch window and to ensure that users have good reporting performance.

ETL Information
This is all the information generated by the ETL process and will include items such as:
When was a mapping created or changed?
When was it last run?
How long did it run for?
Did it succeed or fail?
How many records inserted, updated, deleted>
This information is again used to monitor the effective running and operation of the system not only in failure but
also by identifying trends such as mappings or transformations whose Performance characteristics are changing.
Query Information
This gathers information about which queries the users are making. The information will include:
What are the queries that are being run?
Which tables do they access?
Which fields are being used?
How long do queries take to execute?
This information is used to optimise the users experience but also to remove redundant information that is no longer
being queried by users.

Some additional high-level guidelines
The following items are just some of the common issues that arise in delivering data warehouse solutions. Whilst not
exhaustive they are some of the most important factors to
consider:
Programme or project?
For data warehouse solutions to be successful (and financially viable), it is important for organisations to view the
development as a long term programme of work and examine how the work can be broken up into smaller
component projects for delivery. This enables many smaller quick wins at different stages of the programme whilst
retaining focus on the overall objective.
Examples of this approach may include the development of tactical independent data marts, a literal staging area to
facilitate reporting from a legacy system, or prioritization of the Development of particular reports which can
significantly help a particular business function. Most successful data warehouse programmes will have an
operational life in excess of ten years with peaks and troughs in development.

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The technology trap
At the outset of any data warehouse project organisations frequently fall into the trap of wanting to design the
largest, most complex and functionally all-inclusive solution. This will often tempt the technical teams to use the
latest, greatest technology promised by a vendor.
However, building a data warehouse is not about creating the biggest database or using the cleverest technology, it is
about putting lots of different, often well established, components together so that they can function successfully to
meet the organisation.s data management requirements. It also requires sufficient design such that when the next
enhancement or extension of the requirement comes along, there is a known and well understood business process
and technology path to meet that requirement.
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Vendor Selection
This document presents a vendor-neutral view. However, it is important (and perhaps obvious) to note that the
products which an organisation chooses to buy will dramatically affect the design and development of the system. In
particular most vendors are looking to spread their coverage in the market space. This means that two selected
products may have overlapping functionality and therefore which product to use for a given piece of functionality
must be identified. It is also important to differentiate between strategic and tactical tools
The other major consideration is that this technology market space changes rapidly. The process, whereby
vendors constantly add features similar to those of another competing product, means that few vendors will have a
significant long term advantage on features alone. Most features that you will require (rather than those that are
sometimes desired) will become available during the lifetime of the programme in market leading products if they
are not already there.

The rule of thumb is therefore when assessing products to follow the basic Gartner5 type magic quadrant of
.ability to execute. and .completeness of vision. and combine that with your organisations view of the long term
relationship it has with the vendor and the fact that a series of rolling upgrades to the technology will be required
over the life of the programme.

Development partners
This is one of the thorniest issues for large organisations as they often have policies that outsource development
work to third parties and do not want to create internal teams.
In practice the issue can be broken down with programme management and business
requirements being sourced internally. Technical design authority is either an external domain expert who transitions
to an internal person or an internal person if suitable skills exist.
It is then possible for individual development projects to be outsourced to development partners. In general the
market place has more contractors with this type of experience than permanent staff with specialist
domain/technology knowledge and so some contractor base either internally or at the development partner is almost
inevitable. Ultimately it comes down to the individuals and how they come together as a team, regardless of the
supplier and the best teams will be a blend of the best people.

The development and implementation sequence
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Data Warehousing on this scale requires a top down approach to requirements and a bottom up approach to the
build. In order to deliver a solution it is important to understand what is required of the reports, where that is sourced
from in the transaction repository and how in turn the transaction repository is populated from the source system.
Conversely the build must start at the bottom and build up through the transaction repository and on to the data
marts.
Each build phase will look to either build up (i.e. add another level) or build out (i.e. add another source) This
approach means that the project manager can firstly be assured that the final destination will meet the users
requirement and that the build can be optimized by using different teams to build up in some areas whilst other
teams are building out the underlying levels. Using this model it is also possible to change direction after each
completed phase.

Homogeneous & Heterogeneous Environments
This architecture can be deployed using homogeneous or heterogeneous technologies. In a homogeneous
environment all the operating systems, databases and other components are built using the same technology, whilst a
heterogeneous solution would allow multiple technologies to be used, although it is usually advisable to limit this to
one technology per component.
For example using Oracle on UNIX everywhere would be a homogeneous environment, whilst using Sybase for the
transaction repository and all staging areas on a UNIX environment and Microsoft SQLServer on Microsoft
Windows for the data marts would be an example of a heterogeneous environment.
The trade off between the two deployments is the cost of integration and managing additional skills with a
heterogeneous environment compared with the suitability of a single product to fulfil all roles in a homogeneous
environment. There is obviously a spectrum of solutions Between the two end points, such as the same operating
system but different databases.
Centralised vs. Distributed solutions
This architecture also supports deployment in either a centralised or distributed mode. In a centralised solution all
the systems are held at a central data centre, this has the advantage of easy management but may result in a
performance impact where users that are remote from the central solution suffer problems over the network.
Conversely a distributed solution provides local solutions, which may have a better performance profile for local
users but might be more difficult to administer and will suffer from capacity issues when loading the data. Once
again there is a spectrum of solutions and therefore there are degrees to which this can be applied. It is normal that
centralised solutions are associated with homogeneous environments whilst distributed environments are usually
heterogeneous, however this need not always be the case.

Converting Data from Application Centric to User Centric Systems such as ERP systems are effectively systems
designed to pump data through a particular business process (application-centric). A data warehouse is designed to
look across systems (user-centric) to allow the user to view the data they need to perform their job.
As an example: raising a purchase order in the ERP system is optimised to get the purchase order from being raised,
through approval to being sent out. Whilst the data warehouse user may want to look at who is raising orders, the
average value, who approves them and how long do they take to do the approval. Requirements should therefore
reflect the view of the data warehouse user and not what a single application can provide.

Analysis and Reporting Tool Usage
When buying licences etc. for the analysis and reporting tools a common mistake is to require many thousands for a

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given reporting tool. Once delivered the number of users never rises to the original estimates. The diagram below
illustrates why this occurs:
Flexibility in data access and complexity of tool
Size of user community
Data
Mining
Ad Hoc
Reporting Tools
Parameterised Reporting
Fixed Reporting
Web Based Desktop Tools
Senior Analysts
Business Analysts
Business Users
Customers and Suppliers
Researchers
Figure 5 - Analysis and Reporting Tool Usage
What the diagram shows is that there is a direct, inverse relationship between the degree of reporting flexibility
required by a user and the number of users requiring this access.
There will be very few people, typically business analysts and planners at the top but these individuals will need to
have tools that really allow them to manipulate and mine the data. At the next level down, there will be a somewhat
larger group of users who require ad hoc reporting access, these people will normally be developing or improving
reports that get presented to management. The remainder but largest community of the user base will only have a
requirement to be presented with data in the form of pre-defined reports with varying degrees of inbuilt flexibility:
for instance, managers, sales staff or even suppliers and customers coming into the solution over the internet. This
broad community will also influence the choice of tool to reflect the skills of the users. Therefore no individual tool
will be perfect and it is a case of fitting the users and a selection of tools together to give the best results.

Data Warehouse .A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of
data in support of management’s decision making process. (Bill Inmon).
Design Pattern A design pattern provides a generic approach, rather than a specific solution for building a
particular system or systems.
Dimension Table Dimension tables contain attributes that describe fact records in the fact table.
Distributed Solution A system architecture where the system components are distributed over a number of sites to
provide local solutions. DMS Data Mart Staging, a component in the data warehouse architecture for staging data.
ERP Enterprise Resource Planning, a business management system that integrates all facets of the business,
including planning, manufacturing, sales, and marketing. ETL Extract, Transform and Load. Activities required to
populate data warehouses and OLAP applications with clean, consistent, integrated and properly summarized data.
Also a component in the data warehouse architecture. Fact Table In an organisation, the .facts. are the key figures
that a business requires. Within that organisation.s data mart, the fact table is the foundation from which everything
else arises.

Term Description

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Heterogeneous System An environment in which all or any of the operating systems, databases and other
components are built using the different technology, and are the integrated by means of customized interfaces.

Heuristic Cleansing Cleansing by means of an approximate method for solving a Problem within the context of
a goal. Heuristic cleansing then uses feedback from the effects of its solution to improve its own performance.
Homogeneous System An environment in which the operating systems, databases and other components are built
using the same technology. KDD Key Design Decision, a project template. KPI Key Performance Indicators. KPIs
help an organization define and measure progress toward organizational goals. LSA Literal Staging Area. Data from
a legacy system is taken and stored in a database in order to make this data more readily accessible to the
downstream systems. A component in the data warehouse architecture. Middleware Software that connects or
serves as the "glue" between two otherwise separate applications. Near-time Refers to data being updated by means
of batch processing at intervals of in between 15 minutes and 1 hour (in contrast to .Real-time. data, which needs to
be updated within 15 minute intervals). Normalisation Database normalization is a process of eliminating duplicated
data in a relational database. The key idea is to store data in one location, and provide links to it wherever needed.
ODS Operational Data Store, also a component in the data warehouse architecture that allows near-time reporting.
OLAP On-Line Analytical Processing. A category of applications and technologies for collecting, managing,
processing and presenting multidimensional data for analysis and management purposes.
OLTP OLTP (Online Transaction Processing) is a form of transaction processing conducted via a computer
network. Portal A Web site or service that offers a broad array of resources and services, such as e-mail, forums,
search engines. Process Neutral Model A Process Neutral Data Model is a data model in which all embedded
business rules have been removed. If this is done correctly then as business processes change there should be little or
no change required to the data model. Business
Intelligence solutions designed around such a model should therefore not be subject to limitations as the business
changes.
Rule Based Cleansing A data cleansing method, which performs updates on the data
based on rules.
SEA Source Entity Analysis, an analysis template.
Snowflake Schema A variant of the star schema with normalized dimension tables.
SSA Source System Analysis, an analysis template.
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Term Description
Star Schema A relational database schema for representing multidimensional data. The data is stored in a central
fact table, with one or more tables holding information on each dimension. Dimensions have levels, and all levels
are usually shown as colum ns in each dimension table.
TOA Target Oriented Analysis, an analysis template. TR Transactional Repository. The collated, clean
repository for the lowest level of data held by the organisation and a component in the data warehouse architecture.
TRS Transaction Repository Staging, a component in the data warehouse architecture used to stage data. Wiki A
wiki is a type of website, or the software needed to operate this website, that allows users to easily add and edit
content, and that is particularly suited to collaborative content creation. WSA Warehouse Support Application, a
component in the data warehouse architecture that supports missing data. Designing the Star Schema Database

Creating a Star Schema Database is one of the most important, and sometimes the final, step in creating a
data warehouse. Given how important this process is to our data warehouse, it is important to understand how me

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move from a standard, on-line transaction processing (OLTP) system to a final star schema (which here, we will call
an OLAP system).
This paper attempts to address some of the issues that have no doubt kept you awake at night. As you stared at the
ceiling, wondering how to build a data warehouse, questions began swirling in your mind:
c What is a Data Warehouse? What is a Data Mart?
What is a Star Schema Database?
Why do I want/need a Star Schema Database?
The Star Schema looks very denormalized. Won’t I get in trouble for that?
What do all these terms mean?
Should I repaint the ceiling?
These are certainly burning questions. This paper will attempt to answer these questions, and show you how to build
a star schema database to support decision support within your organization.

Usually, you are bored with terminology at the end of a chapter, or buried in an appendix at the back of the book.
Here, however, I have the thrill of presenting some terms up front. The intent is not to bore you earlier than usual,
but to present a baseline off of which we can operate. The problem in data warehousing is that the terms are often
used loosely by different parties. The Data Warehousing Institute (http://www.dw-institute.com) has attempted to
standardize some terms and concepts. I will present my best understanding of the terms I will use throughout this
lecture. Please note, however, that I do not speak for the Data Warehousing Institute.

OLTP
OLTP stand for Online Transaction Processing. This is a standard, normalized database structure. OLTP is designed
for transactions, which means that inserts, updates, and deletes must be fast. Imagine a call center that takes orders.
Call takers are continually taking calls and entering orders that may contain numerous items. Each order and each
item must be inserted into a database. Since the performance of the database is critical, we want to maximize the
speed of inserts (and updates and deletes). To maximize performance, we typically try to hold as few records in the
database as possible.

OLAP and Star Schema
OLAP stands for Online Analytical Processing. OLAP is a term that means many things to many people.
Here, we will use the term OLAP and Star Schema pretty much interchangeably. We will assume that a star schema
database is an OLAP system. This is not the same thing that Microsoft calls OLAP; they extend OLAP to mean the
cube structures built using their product, OLAP Services. Here, we will assume that any system of read-only,
historical, aggregated data is an OLAP system.
In addition, we will assume an OLAP/Star Schema can be the same thing as a data warehouse. It can be, although
often data warehouses have cube structures built on top of them to speed queries.

Data Warehouse and Data Mart
Before you begin grumbling that I have taken two very different things and lumped them together, let me explain
that Data Warehouses and Data Marts are conceptually different – in scope. However, they are built using the exact
same methods and procedures, so I will define them together here, and then discuss the differences.
A data warehouse (or mart) is way of storing data for later retrieval. This retrieval is almost always used to support

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decision-making in the organization. That is why many data warehouses are considered to be DSS (Decision-
Support Systems). You will hear some people argue that not all data warehouses are DSS, and that’s fine. Some data
warehouses are merely archive copies of data. Still, the full benefit of taking the time to create a star schema, and
then possibly cube structures, is to speed the retrieval of data. In other words, it supports queries. These queries are
often across time. And why would anyone look at data across time? Perhaps they are looking for trends. And if they
are looking for trends, you can bet they are making decisions, such as how much raw material to order. Guess what:
that’s decision support!
Enough of the soap box. Both a data warehouse and a data mart are storage mechanisms for read-only, historical,
aggregated data. By read-only, we mean that the person looking at the data won’t be changing it. If a user wants to
look at the sales yesterday for a certain product, they should not have the ability to change that number. Of course, if
we know that number is wrong, we need to correct it, but more on that later.
The “historical” part may just be a few minutes old, but usually it is at least a day old. A data warehouse usually
holds data that goes back a certain period in time, such as five years. In contrast, standard OLTP systems usually
only hold data as long as it is “current” or active. An order table, for example, may move orders to an archive table
once they have been completed, shipped, and received by the customer.
When we say that data warehouses and data marts hold aggregated data, we need to stress that there are many levels
of aggregation in a typical data warehouse. In this section, on the star schema, we will just assume the “base” level
of aggregation: all the data in our data warehouse is aggregated to a certain point in time.
Let’s look at an example: we sell 2 products, dog food and cat food. Each day, we record sales of each product. At
the end of a couple of days, we might have data that looks like this:

Quantity Sold
Date Order Number Dog Food Cat Food
4/24/99 1 5 2
2 3 0
3 2 6
4 2 2
5 3 3

4/25/99 1 3 7
2 2 1
3 4 0
Table 1
Now, as you can see, there are several transactions. This is the data we would find in a standard OLTP system.
However, our data warehouse would usually not record this level of detail. Instead, we summarize, or aggregate, the
data to daily totals. Our records in the data warehouse might look something like this:

Quantity Sold
Date Dog Food Cat Food
4/24/99 15 13
4/25/99 9 8
Table 2
You can see that we have reduced the number of records by aggregating the individual transaction records into daily
records that show the number of each product purchased each day.
We can certainly get from the OLTP system to what we see in the OLAP system just by running a query. However,

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there are many reasons not to do this, as we will see later.
Aggregations
There is no magic to the term “aggregations.” It simply means a summarized, additive value. The level of
aggregation in our star schema is open for debate. We will talk about this later. Just realize that almost every star
schema is aggregated to some base level, called the grain.
OLTP Systems
OLTP, or Online Transaction Processing, systems are standard, normalized databases. OLTP systems are optimized
for inserts, updates, and deletes; in other words, transactions. Transactions in this context can be thought of as the
entry, update, or deletion of a record or set of records.
OLTP systems achieve greater speed of transactions through a couple of means: they minimize repeated data, and
they limit the number of indexes. First, let’s examine the minimization of repeated data.
If we take the concept of an order, we usually think of an order header and then a series of detail records. The header
contains information such as an order number, a bill-to address, a ship-to address, a PO number, and other fields. An
order detail record is usually a product number, a product description, the quantity ordered, the unit price, the total
price, and other fields. Here is what an order might look like:

Figure 1
Now, the data behind this looks very different. If we had a flat structure, we would see the detail records looking
like this:

Order Number Order Date Customer ID Customer Customer Customer
Name Address City
12345 4/24/99 451 ACME 123 Main Street Louisville
Products
Customer State Customer Contact Contact Product ID Product Name
Zip Name Number
KY 40202 Jane Doe 502-555-1212 A13J2 Widget
Product Category SubCategory Product Price Quantity Ordered Etc…
Description
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¼” Brass Widget Brass Goods Widgets $1.00 200 Etc…
Table 3
Notice, however, that for each detail, we are repeating a lot of information: the entire customer address, the contact
information, the product information, etc. We need all of this information for each detail record, but we don’t want
to have to enter the customer and product information for each record. Therefore, we use relational technology to tie
each detail to the header record, without having to repeat the header information in each detail record. The new
detail records might look like this:

Order Number Product Quantity
Number Ordered
12473 A4R12J 200
Table 4
A simplified logical view of the tables might look something like this:

Figure 2
Notice that we do not have the extended cost for each record in the OrderDetail table. This is because we store as
little data as possible to speed inserts, updates, and deletes. Therefore, any number that can be calculated is
calculated and not stored.
We also minimize the number of indexes in an OLTP system. Indexes are important, of course, but they slow down
inserts, updates, and deletes. Therefore, we use just enough indexes to get by. Over-indexing can significantly
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decrease performance.
Normalization
Database normalization is basically the process of removing repeated information. As we saw above, we do not want
to repeat the order header information in each order detail record. There are a number of rules in database
normalization, but we will not go through the entire process.
First and foremost, we want to remove repeated records in a table. For example, we don’t want an order table that
looks like this:

Figure 3
In this example, we will have to have some limit of order detail records in the Order table. If we add 20 repeated sets
of fields for detail records, we won’t be able to handle that order for 21 products. In addition, if an order just has one
product ordered, we still have all those fields wasting space.
So, the first thing we want to do is break those repeated fields into a separate table, and end up with this:

Figure 4
Now, our order can have any number of detail records.
OLTP Advantages
As stated before, OLTP allows us to minimize data entry. For each detail record, we only have to enter the primary
key value from the OrderHeader table, and the primary key of the Product table, and then add the order quantity.

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This greatly reduces the amount of data entry we have to perform to add a product to an order.
Not only does this approach reduce the data entry required, it greatly reduces the size of an OrderDetail record.
Compare the size of the records in Table 3 as to that in Table 4. You can see that the OrderDetail records take up
much less space when we have a normalized table structure. This means that the table is smaller, which helps speed
inserts, updates, and deletes.
In addition to keeping the table smaller, most of the fields that link to other tables are numeric. Queries generally
perform much better against numeric fields than they do against text fields. Therefore, replacing a series of text
fields with a numeric field can help speed queries. Numeric fields also index faster and more efficiently.
With normalization, we may also have fewer indexes per table. This means that inserts, updates, and deletes run
faster, because each insert, update, and delete may affect one or more indexes. Therefore, with each transaction,
these indexes must be updated along with the table. This overhead can significantly decrease our performance.

OLTP Disadvantages
There are some disadvantages to an OLTP structure, especially when we go to retrieve the data for analysis. For one,
we now must utilize joins and query multiple tables to get all the data we want. Joins tend to be slower than reading
from a single table, so we want to minimize the number of tables in any single query. With a normalized structure,
we have no choice but to query from multiple tables to get the detail we want on the report.
One of the advantages of OLTP is also a disadvantage: fewer indexes per table. Fewer indexes per table are great for
speeding up inserts, updates, and deletes. In general terms, the fewer indexes we have, the faster inserts, updates,
and deletes will be. However, again in general terms, the fewer indexes we have, the slower select queries will run.
For the purposes of data retrieval, we want a number of indexes available to help speed that retrieval. Since one of
our design goals to speed transactions is to minimize the number of indexes, we are limiting ourselves when it
comes to doing data retrieval. That is why we look at creating two separate database structures: an OLTP system for
transactions, and an OLAP system for data retrieval.
Last but not least, the data in an OLTP system is not user friendly. Most IT professionals would rather not have to
create custom reports all day long. Instead, we like to give our customers some query tools and have them create
reports without involving us. Most customers, however, don’t know how to make sense of the relational nature of
the database. Joins are something mysterious, and complex table structures (such as associative tables on a bill-of-
material system) are hard for the average customer to use. The structures seem obvious to us, and we sometimes
wonder why our customers can’t get the hang of it. Remember, however, that our customers know how to do a
FIFO-to-LIFO revaluation and other such tasks that we don’t want to deal with; therefore, understanding relational
concepts just isn’t something our customers should have to worry about.
If our customers want to spend the majority of their time performing analysis by looking at the data, we need to
support their desire for fast, easy queries. On the other hand, we need to meet the speed requirements of our
transaction-processing activities. If these two requirements seem to be in conflict, they are, at least partially. Many
companies have solved this by having a second copy of the data in a structure reserved for analysis. This copy is
more heavily indexed, and it allows customers to perform large queries against the data without impacting the
inserts, updates, and deletes on the main data. This copy of the data is often not just more heavily indexed, but also
denormalized to make it easier for customers to understand.
Reasons to Denormalize
Whenever I ask someone why you would ever want to denormalize, the first (and often only) answer is: speed.
We’ve already discussed some disadvantages to the OLTP structure; it is built for data inserts, updates, and deletes,

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but not data retrieval. Therefore, we can often squeeze some speed out of it by denormalizing some of the tables and
having queries go against fewer tables. These queries are faster because they perform fewer joins to retrieve the
same recordset.
Joins are slow, as we have already mentioned. Joins are also confusing to many end users. By denormalizing, we can
present the user with a view of the data that is far easier for them to understand. Which view of the data is easier for
a typical end-user to understand:

Figure 5

Figure 6
The second view is much easier for the end user to understand. We had to use joins to create this view, but if we put
all of this in one table, the user would be able to perform this query without using joins. We could create a view that
looks like this, but we are still using joins in the background and therefore not achieving the best performance on the
query.
How We View Information
All of this leads us to the real question: how do we view the data we have stored in our database? This is not the
question of how we view it with queries, but how do we logically view it? For example, are these intelligent

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questions to ask:
q How many bottles of Aniseed Syrup did we sell last week?
Are overall sales of Condiments up or down this year compared to previous years?
On a quarterly and then monthly basis, are Dairy Product sales cyclical?
In what regions are sales down this year compared to the same period last year? What products in those
regions account for the greatest percentage of the decrease?
All of these questions would be considered reasonable, perhaps even common. They all have a few things in
common. First, there is a time element to each one. Second, they all are looking for aggregated data; they are asking
for sums or counts, not individual transactions. Finally, they are looking at data in terms of “by” conditions.
When I talk about “by” conditions, I am referring to looking at data by certain conditions. For example, if we take
the question “On a quarterly and then monthly basis, are Dairy Product sales cyclical” we can break this down into
this: “We want to see total sales by category (just Dairy Products in this case), by quarter or by month.”
Here we are looking at an aggregated value, the sum of sales, by specific criteria. We could add further “by”
conditions by saying we wanted to see those sales by brand and then the individual products.
Figuring out the aggregated values we want to see, like the sum of sales dollars or the count of users buying a
product, and then figuring out these “by” conditions is what drives the design of our star schema.
Making the Database Match our Expectations
If we want to view our data as aggregated numbers broken down along a series of “by” criteria, why don’t we just
store data in this format?
That’s exactly what we do with the star schema. It is important to realize that OLTP is not meant to be the basis of a
decision support system. The “T” in OLTP stands for transactions, and a transaction is all about taking orders and
depleting inventory, and not about performing complex analysis to spot trends. Therefore, rather than tie up our
OLTP system by performing huge, expensive queries, we build a database structure that maps to the way we see the
world.
We see the world much like a cube. We won’t talk about cube structures for data storage just yet. Instead, we will
talk about building a database structure to support our queries, and we will speed it up further by creating cube
structures later.

Facts and Dimensions
When we talk about the way we want to look at data, we usually want to see some sort of aggregated data. These
data are called measures. These measures are numeric values that are measurable and additive. For example, our
sales dollars are a perfect measure. Every order that comes in generates a certain sales volume measured in some
currency. If we sell twenty products in one day, each for five dollars, we generate 100 dollars in total sales.
Therefore, sales dollars is one measure we may want to track. We may also want to know how many customers we
had that day. Did we have five customers buying an average of four products each, or did we have just one customer
buying twenty products? Sales dollars and customer counts are two measures we will want to track.
Just tracking measures isn’t enough, however. We need to look at our measures using those “by” conditions.
These “by” conditions are called dimensions. When we say we want to know our sales dollars, we almost always
mean by day, or by quarter, or by year. There is almost always a time dimension on anything we ask for. We may
also want to know sales by category or by product. These by conditions will map into dimensions: there is almost
always a time dimension, and product and geographic dimensions are very common as well.
Therefore, in designing a star schema, our first order of business is usually to determine what we want to see (our
measures) and how we want to see it (our dimensions).

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Mapping Dimensions into Tables
Dimension tables answer the “why” portion of our question: how do we want to slice the data? For example, we
almost always want to view data by time. We often don’t care what the grand total for all data happens to be. If our
data happen to start on June 14, 1989, do we really care how much our sales have been since that date, or do we
really care how one year compares to other years? Comparing one year to a previous year is a form of trend analysis
and one of the most common things we do with data in a star schema.
We may also have a location dimension. This allows us to compare the sales in one region to those in another. We
may see that sales are weaker in one region than any other region. This may indicate the presence of a new
competitor in that area, or a lack of advertising, or some other factor that bears investigation.
When we start building dimension tables, there are a few rules to keep in mind. First, all dimension tables should
have a single-field primary key. This key is often just an identity column, consisting of an automatically
incrementing number. The value of the primary key is meaningless; our information is stored in the other fields.
These other fields contain the full descriptions of what we are after. For example, if we have a Product dimension
(which is common) we have fields in it that contain the description, the category name, the sub-category name, etc.
These fields do not contain codes that link us to other tables. Because the fields are the full descriptions, the
dimension tables are often fat; they contain many large fields.
Dimension tables are often short, however. We may have many products, but even so, the dimension table cannot
compare in size to a normal fact table. For example, even if we have 30,000 products in our product table, we may
track sales for these products each day for several years. Assuming we actually only sell 3,000 products in any given
day, if we track these sales each day for ten years, we end up with this equation: 3,000 products sold X 365 day/year
* 10 years equals almost 11,000,000 records! Therefore, in relative terms, a dimension table with 30,000 records
will be short compared to the fact table.
Given that a dimension table is fat, it may be tempting to denormalize the dimension table. Resist the urge to do so;
we will see why in a little while when we talk about the snowflake schema.
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Dimensional Hierarchies
We have been building hierarchical structures in OLTP systems for years. However, hierarchical structures in an
OLAP system are different because the hierarchy for the dimension is actually all stored in the dimension table.
The product dimension, for example, contains individual products. Products are normally grouped into categories,
and these categories may well contain sub-categories. For instance, a product with a product number of X12JC may
actually be a refrigerator. Therefore, it falls into the category of major appliance, and the sub-category of
refrigerator. We may have more levels of sub-categories, where we would further classify this product. The key here
is that all of this information is stored in the dimension table.
Our dimension table might look something like this:

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Figure 7
Notice that both Category and Subcategory are stored in the table and not linked in through joined tables that store
the hierarchy information. This hierarchy allows us to perform “drill-down” functions on the data. We can perform a
query that performs sums by category. We can then drill-down into that category by calculating sums for the
subcategories for that category. We can the calculate the sums for the individual products in a particular
subcategory.
The actual sums we are calculating are based on numbers stored in the fact table. We will examine the fact table in
more detail later.
Consolidated Dimensional Hierarchies (Star Schemas)
The above example (Figure 7) shows a hierarchy in a dimension table. This is how the dimension tables are built in
a star schema; the hierarchies are contained in the individual dimension tables. No additional tables are needed to
hold hierarchical information.
Storing the hierarchy in a dimension table allows for the easiest browsing of our dimensional data. In the above
example, we could easily choose a category and then list all of that category’s subcategories. We would drill-down
into the data by choosing an individual subcategory from within the same table. There is no need to join to an
external table for any of the hierarchical informaion.
In this overly-simplified example, we have two dimension tables joined to the fact table. We will examine the fact
table later. For now, we will assume the fact table has only one number: SalesDollars.

Figure 8
In order to see the total sales for a particular month for a particular category, our SQL query has to be defined.

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Snowflake Schemas
Sometimes, the dimension tables have the hierarchies broken out into separate tables. This is a more normalized
structure, but leads to more difficult queries and slower response times.
Figure 9 represents the beginning of the snowflake process. The category hierarchy is being broken out of the
ProductDimension table. You can see that this structure increases the number of joins and can slow queries. Since
the purpose of our OLAP system is to speed queries, snowflaking is usually not something we want to do. Some
people try to normalize the dimension tables to save space. However, in the overall scheme of the data warehouse,
the dimension tables usually only hold about 1% of the records. Therefore, any space savings from normalizing, or
snowflaking, are negligible.

igure 9
Building the Fact Table
The Fact Table holds our measures, or facts. The measures are numeric and additive across some or all of the
dimensions. For example, sales are numeric and we can look at total sales for a product, or category, and we can
look at total sales by any time period. The sales figures are valid no matter how we slice the data.
While the dimension tables are short and fat, the fact tables are generally long and skinny. They are long because
they can hold the number of records represented by the product of the counts in all the dimension tables.

For example, take the following simplified star schema:

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Figure 10
In this schema, we have product, time and store dimensions. If we assume we have ten years of daily data, 200
stores, and we sell 500 products, we have a potential of 365,000,000 records (3650 days * 200 stores * 500
products). As you can see, this makes the fact table long.
The fact table is skinny because of the fields it holds. The primary key is made up of foreign keys that have migrated
from the dimension tables. These fields are just some sort of numeric value. In addition, our measures are also
numeric. Therefore, the size of each record is generally much smaller than those in our dimension tables. However,
we have many, many more records in our fact table.
Fact Granularity
One of the most important decisions in building a star schema is the granularity of the fact table. The granularity, or
frequency, of the data is usually determined by the time dimension. For example, you may want to only store weekly
or monthly totals. The lower the granularity, the more records you will have in the fact table. The granularity also
determines how far you can drill down without returning to the base, transaction-level data.
Many OLAP systems have a daily grain to them. The lower the grain, the more records that we have in the fact
table. However, we must also make sure that the grain is low enough to support our decision support needs.
One of the major benefits of the star schema is that the low-level transactions are summarized to the fact table grain.
This greatly speeds the queries we perform as part of our decision support. This aggregation is the heart of our
OLAP system.
Fact Table Size
We have already seen how 500 products sold in 200 stores and tracked for 10 years could produce 365,000,000
records in a fact table with a daily grain. This, however, is the maximum size for the table. Most of the time, we do
not have this many records in the table. One of the things we do not want to do is store zero values. So, if a product
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did not sell at a particular store for a particular day, we would not store a zero value. We only store the records that
have a value. Therefore, our fact table is often sparsely populated.
Even though the fact table is sparsely populated, it still holds the vast majority of the records in our database and is
responsible for almost all of our disk space used. The lower our granularity, the larger the fact table. You can see
from the previous example that moving from a daily to weekly grain would reduce our potential number of records
to only slightly more than 52,000,000 records.
The data types for the fields in the fact table do help keep it as small as possible. In most fact tables, all of the fields
are numeric, which can require less storage space than the long descriptions we find in the dimension tables.
Finally, be aware that each added dimension can greatly increase the size of our fact table. If we added one
dimension to the previous example that included 20 possible values, our potential number of records would reach
7.3 billion.
Changing Attributes
One of the greatest challenges in a star schema is the problem of changing attributes. As an example, we will use the
simplified star schema in Figure 10. In the StoreDimension table, we have each store being in a particular region,
territory, and zone. Some companies realign their sales regions, territories, and zones occasionally to reflect
changing business conditions. However, if we simply go in and update the table, and then try to look at historical
sales for a region, the numbers will not be accurate. By simply updating the region for a store, our total sales for that
region will not be historically accurate.
In some cases, we do not care. In fact, we want to see what the sales would have been had this store been in that
other region in prior years. More often, however, we do not want to change the historical data. In this case, we may
need to create a new record for the store. This new record contains the new region, but leaves the old store record,
and therefore the old regional sales data, intact. This approach, however, prevents us from comparing this stores
current sales to its historical sales unless we keep track of it’s previous StoreID. This can require an extra field
called PreviousStoreID or something similar.
There are no right and wrong answers. Each case will require a different solution to handle changing attributes.
Aggregations
Finally, we need to discuss how to handle aggregations. The data in the fact table is already aggregated to the fact
table’s grain. However, we often want to aggregate to a higher level. For example, we may want to sum sales to a
monthly or quarterly number. In addition, we may be looking for total just for a product or a category.
These numbers must be calculated on the fly using a standard SQL statement. This calculation takes time, and
therefore some people will want to decrease the time required to retrieve higher-level aggregations.
Some people store higher-level aggregations in the database by pre-calculating them and storing them in the
database. This requires that the lowest-level records have special values put in them. For example, a TimeDimension
record that actually holds weekly totals might have a 9 in the DayOfWeek field to indicate that this particular record
holds the total for the week.
This approach has been used in the past, but better alternatives exist. These alternatives usually consist of building a
cube structure to hold pre-calculated values. We will examine Microsoft’s OLAP Services, a tool designed to build
cube structures to speed our access to warehouse data.
Slowly changing dimensions are used to describe the date effectivity of the data. It describe the dimensions whose
attribute value vary over time.
This term is commonly used in the Data Warehousing world. However, the problem exists in the OLTP, relational
data modeling as well.
Example:

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The sales representative assigned to a customer may change over time. Linda was the salesrep for ABC, inc. before
March last year. Kathy later becomes the representative for this account.
You may want to track the data “as is”, “as was”, or both. If you show the year total sales, you can either report as
the sales are all generated by Kathy, or actually break the number down between Linda and Kathy.

Slowly changing dimensions
Slowly changing dimensions (1)
S The dimensional attribute record is overwritten with the new value
T No changes are needed elsewhere in the dimension record
N No keys are affected anywhere in the database
N Very easy to implement but the historical data is now inconsistent

S Introduce a new record for the same dimensional entity in order to reflect its changed state
I A new instance of the dimensional key is created which references the new record
A In order to is best dealt with by using version digits at the end of the key.
I All these keys need to be created, maintained and managed by someone and tracked in the metadata
A The database maintains its consistency and the versions can be said to partition history

) Use slightly different design of dimension table which has fields for:
o original status of dimensional attribute
o current status of dimensional attribute
o an effective date of change field
a This allows the analyst to compare the as-is and as-was states against each other
T Only two states can be traced, the current and the original
O Some inconsistencies are created in the data as time is not properly partitioned
Introduction to the Series
Oracle9i provides a new set of ETL options that can be effectively integrated into the ETL architecture. In order to
develop the correct approach to implementing new technology in the ETL architecture, it is important to understand
the components, architectural options and best practices when designing and developing a data warehouse. With
this background, each option will be explored and how it is best suited for the ETL architecture.
Through this series of articles, an overview of the ETL architecture will be discussed as well as a detailed look at
each option. Each ETL option’s syntax, behavior and performance (where appropriate) will be examined. Based on
the results of examples, combined with a solid understanding of the ETL architecture, strategies and approaches to
leverage the new options in the ETL architecture will be outlined. The final article in the series will provide a look at
all of the ETL options working together, stemming from examples throughout the series.
Individual articles in the series include:
I Part 1 – Overview of the Extract, Transform and Load (ETL) Architecture
P Part 2 – External Tables
P Part 3 – Multiple Table Insert

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o Part 4 – Upsert /MERGE INTO (Add and Update Combined Statement)
P Part 5 – Table Functions
P Part 6 – Bring it All Together: A Look at the Combined Use of the ETL Options.
The information in the series is targeted for data warehouse developers, data warehouse architects and information
technology managers.
Overview of the Extract, Transform and Load Architecture (ETL)
The warehouse architect can assemble ETL architectures in many different forms using an endless variety of
technologies. Due to this fact, the warehouse can take advantage of the software, skill sets, hardware and standards
already in place within an organization. The potential weakness of the warehouse arises when a loosely managed
project, which does not adhere to a standard approach, results in an increase in scope, budget and maintenance. This
weakness may result in vulnerabilities to unforeseen data integrity limitations in the source systems as well. The key
to eliminating this weakness is to develop a technical design that employs solid warehouse expertise and data
warehouse best practices. Professional experience and the data warehouse fundamentals are key elements to
eliminating failure on a warehouse project.
Potential problems are exposed in this article not to deliver fear or confirm the popular cliché that “warehouse
projects fail.” It is simply important to understand that new technologies, such as database options, are not a
replacement for the principles of data warehousing and ETL processing. New technologies should, and many times
will, advance or complement the warehouse. They should make its architecture more efficient, scalable and stable.
That is where the new Oracle9i features play nicely. These features will be explored while looking at their
appropriate uses in the ETL architecture. In order to determine where the new Oracle9i features may fit into the ETL
architecture, it is important to look at ETL approaches and components.
Approaches to ETL Architecture
Within the ETL architecture two distinct, but not mutually exclusive, approaches are traditionally used in the ETL
design. The custom approach is the oldest and was once the only approach for data warehousing. In effect, this
approach takes the technologies and hardware that an organization has on hand and develops a data warehouse using
those technologies. The second approach includes the use of packaged ETL software. This approach focuses on
performing the majority of connectivity, extraction, transformation and data loading within the ETL tool itself.
However, this software comes with an additional cost. The potential benefits of an ETL package include a reduction
in development time as well as a reduction in maintenance overhead.

ETL Components
The ETL architecture is traditionally designed into two components:

T The source to stage component is intended to focus the efforts of reading the source data (sourcing) and
replicating the data to the staging area. The staging area is typically comprised of several schemas that house
individual source systems or sets of related source systems. Within each schema, all of the source system tables are
usually “mirrored.” The structure of the stage table is identical to that of the source table with the addition of data
elements to support referential integrity and future ETL processing.
e The stage to warehouse component focuses the effort of standardizing and centralizing the data from the
source systems into a single view of the organization’s information. This centralized target can be a data warehouse,
data mart, operational data store, customer list store, reporting database or any other reporting/data environment.
(The examples in this article assume the final target is a data warehouse.) This portion of the architecture should not
be concerned with translation, data formats, or data type conversion. It can now focus on the complex task of

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cleansing, standardizing and transforming the source data according to the business rules.

It is important to note that an ETL tool strictly “extracts, transforms and loads.” Separate tools or external service
organizations, which may require additional cost, accomplish the work of name and address cleansing and
standardization. These data cleansing tools can work in conjunction with the ETL packaged software in a variety of
ways. Many organizations exist that are able to perform the same work offsite under a contractual basis. The task of
data cleansing can occur in the staging environment prior to or during stage to warehouse processing. In any case, it
is a good practice to house a copy of the data cleansing output in the staging area for auditing purposes.

The following sections include diagrams and overviews of:
T Custom source to stage,
C Packaged ETL tool source to stage,
P Custom stage to warehouse, and
C Packaged ETL tool stage to warehouse architectures.
This article assumes that the staging and warehouse databases are Oracle9i instances hosted on separate systems.
Custom ETL – Source to Stage

Figur e 1: Source to Stage Portion of Custom ETL Architecture
Figure 1 outlines the source to stage portion of a custom ETL architecture and exposes several methods of data
“connections.” These methods include:
“ Replicating data through the use of data replication software (mirroring software) that detects or “sniffs”
changes from a database or file system logs.
c Generating flat files by pulling or pushing data from a client program connected to the source system.
G ”FTPing” internal data from the source system in a native or altered format.
” Connecting natively to source system data and/or files (i.e., a DB2 connection to a AS/400 file systems).
C Reading data from a native database connection.
R Reading data over a database link from an Oracle instance to the target Oracle staging instance and FTPing
data from an external site to the staging host system.
Other data connection options may include a tape delivered on site and copied, reading data from a queue (i.e.,

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MQSeries), reading data from an enterprise application integration (EAI) message, reading data via a database
bridge or other third-party broker for data access (i.e., DB2 Connect, DBAnywhere), etc …
After a connection is established to the source systems, many methods are used to read and load the data into the
staging area as described in the diagram. These methods include the use of:
s Replication software (combines read and write replication into a single software package),
R A shell or other scripting tool such as KSH, CSH, PERL and SQL reading data from a flat file,
A A shell or other scripting tool reading data from a database connection (i.e., over PERL DBI),
A A packaged or custom executable such as C, C++, AWK, SED or Java reading data from a flat file,
A A packaged or custom executable reading data from a database connection and SQL*Loader reading from a
flat file.
Packaged ETL Tool – Source to Stage

Figure 2: Source to Stage Portion Using a Packaged ETL Tool
Figure 2 outlines the source to stage portion of an ETL architecture using a packaged ETL tool and exposes several
methods of data “connections” which are similar to those used in the custom source to stage processing model. The
connection method with a packaged ETL tool typically allows for all of the connections one would expect from a
custom development effort. In most cases, each type of source connection requires a license. For example if a
connection is required to a Sybase, DB2 and Oracle database, three separate licenses are needed. If licensing is an
issue, the ETL architecture typically embraces a hybrid solution using other custom methods to replicate source data
in addition to the packaged ETL tool.
Connection methods include:
C Replicating data using data replication software (mirroring software) that detects or sniffs changes. from
the database or file system logs.
t FTPing internal data from the source system in native or altered format.
F Connecting natively to the system data and/or files (i.e., a DB2 connection to a AS/400 file systems).
C Reading data from a native database connection.
R Reading data over a database link by an Oracle staging database from the target Oracle instance.
R FTPing data from an external site to the staging host system.
Other options may include a tape delivered on site and copied, reading data from a queue (i.e., MQSeries), reading

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data from an enterprise application integration (EAI) message/queue, reading data via a database bridge or other
third-party broker for data access (i.e., DB2 Connect, DBAnywhere), etc…
After a connection is established to the source systems, the ETL tool is used to read, perform simple transformations
such as rudimentary cleansing (i.e., trimming spaces), perform data type conversion, convert data formats and load
the data into the staging area. Advanced transformations are recommended to take place in the stage to warehouse
component and not in the source to stage processing (explained in the next section). Because the package ETL tool
is designed to handle all of the transformations and conversions, all the work is done within the ETL server itself.
Within the ETL tool’s server repository, separate mappings exist to perform the individual ETL tasks.
Custom ETL – Stage to Warehouse

Figure 3: Stage to Warehouse Portion of Custom ETL Architecture
Figure 3 outlines the stage to warehouse portion of a custom ETL architecture. The ETL stage to warehouse
component is where the data standardization and centralization occur. The work of gathering, formatting and
converting data types is has been completed by the source to stage component. Now the ETL work can focus on the
task of creating a single view of the organization’s data in the warehouse.
This diagram exposes several typical methods of standardizing and/or centralizing data to the data warehouse. These
methods include the use of a:
m PL/SQL procedure reading and writing directly to the data warehouse from the staging database (this could
be done just as easily if the procedure was located in the warehouse database).
b PL/SQL procedure reading from the staging database and writing to flat files (i.e., via a SQL script).
P SQL*Plus client writing data to a flat file from stage, SQL*Loader importing files into the warehouse for
loading or additional processing by a PL/SQL procedure.
l An Oracle table export-import process from staging to the warehouse for loading or additional processing
by a PL/SQL procedure.
b Shell or other scripting tool such as KSH, CSH, PERL or SQL reading data natively or from a flat file and
writing data into the warehouse.

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o Packaged or custom executable such as C, C++, AWK, SED or Java reading data natively or from a flat file
and writing data into the warehouse.
Packaged ETL Tool – Stage to Warehouse

Figure 4: Stage to Warehouse Portion of a Packaged ETL Tool Architecture
Figure 4 outlines the stage to warehouse portion of a packaged ETL tool architecture. Figure 4 diagrams the
packaged ETL application performing the standardization and centralization of data to the warehouse all within one
application. This is the strength of a packaged ETL tool. In addition, this is the component of ETL architecture
where the ETL tool is best suited to apply the organization’s business rules. The packaged ETL tool will source the
data through a native connection to the staging database. It will perform transformations on each record after pulling
the data from the stage database through a pipe. From there it will load each record into the warehouse through a
native connection to the database. Again, not all packaged ETL architectures look like this due to many factors.
Typically a deviation in the architecture is due to requirements that the ETL software cannot, or is not licensed, to
fulfill. In these instances one of the custom stage to warehouse methods is most commonly used.
Business Logic and the ETL Architecture
In any warehouse development effort, the business logic is the core of the warehouse. The business logic is applied
to the proprietary data from the organization’s internal and external data sources. The application process combines
the heterogeneous data into a single view of the organization’s information. The logic to create a central view of the
information is often a complex task. In order to properly manage this task, it is important to consolidate the business
rules into the stage to warehouse ETL component, regardless of the ETL architecture. If this best practice is ignored,
much of the business logic may be spread throughout the source to stage and stage to warehouse components. This
will ultimately hamper the organization’s ability to maintain the warehouse solution long term and may lead to an
error prone system.
Within the packaged ETL tool architecture, the centralization of the business logic becomes a less complex task.
Due to the fact that the mapping and transformation logic is managed by the ETL software package, the
centralization of rules is offered as a feature of the software. However, using packaged ETL tools does not guarantee
a proper ETL implementation. Good warehouse development practices are still necessary when developing any type
of ETL architecture.
In the custom ETL architecture, it becomes critical to place the application of business logic in the stage to
warehouse component due to the large number of individual modules. The custom solution will typically store
business logic in a custom repository or in the code of the ETL transformations. This is the greatest disadvantage to
the custom warehouse. Developing a custom repository requires additional development effort, solid warehousing
design experience and strict attention to detail. Due to this difficulty, the choice may be made to develop the rules

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into the ETL transformation code to speed the time of delivery. Whether or not the decision is made to store the
rules in a custom repository or not, it is important to have a well-thought-out design. The business rules are the heart
of the warehouse. Any problems with the rules will create errors in the system.
It is important to understand some of the best practices and risks when developing ETL architectures to better
appreciate how the new technology will fit into the architecture. With this background it is apparent that new
technology or database options will not be silver bullet for ETL processing. New technology will not increase a
solution’s effectiveness nor replace the need for management of the business rules. However, the new Oracle 9i ETL
options provide a great complement to custom and packaged ETL tool architectures.
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Extract, transform, and load (ETL) is a process in data warehousing that involves extracting data from outside
sources,
transforming it to fit business needs, and ultimately loading it into the data warehouse.
ETL is important, as it is the way data actually gets loaded into the warehouse. This article assumes that data is
always loaded into a data warehouse, whereas the term ETL can in fact refer to a process that loads any database.
Contents
[hide]
1 Extract
2 Transform
3 Load
4 Challenges
5 Tools

Extract
The first part of an ETL process is to extract the data from the source systems. Most data warehousing projects
consolidate data from different source systems. Each separate system may also use a different data organization /
format. Common data source formats are relational databases and flat files, but may include non-relational database
structures such as IMS or other data structures such as VSAM or ISAM. Extraction converts the data into a format
for transformation processing.

Transform
The transform stage applies a series of rules or functions to the extracted data to derive the data to be loaded. Some
data sources will require very little manipulation of data. In other cases, one or more of the following
transformations types may be required:
Selecting only certain columns to load (or selecting null columns not to load)
Translating coded values (e.g., if the source system stores M for male and F for female, but the warehouse stores 1
for male and 2 for female)
Encoding free-form values (e.g., mapping "Male" and "M" and "Mr" onto 1)
Deriving a new calculated value (e.g., sale_amount = qty * unit_price)
Joining together data from multiple sources (e.g., lookup, merge, etc.)
Summarizing multiple rows of data (e.g., total sales for each region)
Generating surrogate key values
Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
Splitting a column into multiple columns (e.g., putting a comma-separated list specified as a string in one column as

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individual values in different columns)

Load
The load phase loads the data into the data warehouse. Depending on the requirements of the organization, this
process ranges widely. Some data warehouses merely overwrite old information with new data. More complex
systems can maintain a history and audit trail of all changes to the data.

Challenges
ETL processes can be quite complex, and significant operational problems can occur with improperly designed ETL
systems.
The range of data values or data quality in an operational system may be outside the expectations of designers at the
time validation and transformation rules are specified. Data profiling of a source during data analysis is
recommended to identify the data conditions that will need to be managed by transform rules specifications.
The scalability of an ETL system across the lifetime of its usage needs to be established during analysis. This
includes understanding the volumes of data that will have to be processed within Service Level Agreements,
(SLAs). The time available to extract from source systems may change, which may mean the same amount of data
may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data
warehouses with tens of terabytes of data. Increasing volumes of data may require designs that can scale from daily
batch to intra-day micro-batch to integration with message queues for continuous transformation and update.
A recent development in ETL software is the implementation of parallel processing. This has enabled a number of
methods to improve overall performance of ETL processes when dealing with large volumes of data.
There are 3 main types of parallelisms as implemented in ETL applications:
Data: By splitting a single sequential file into smaller data files to provide parallel access.

Pipeline: Allowing the simultaneous running of several components on the same data stream. An example would be
looking up a value on record 1 at the same time as adding together two fields on record 2.

Component: The simultaneous running of multiple processes on different data streams in the same job. Sorting one
input file while performing a deduplication on another file would be an example of component parallelism.
All three types of parallelism are usually combined in a single job.An additional difficulty is making sure the data
being uploaded is relatively consistent. Since multiple source databases all have different update cycles (some may
be updated every few minutes, while others may take days or weeks), an ETL system may be required to hold back
certain data until all sources are synchronized. Likewise, where a warehouse may have to be reconciled to the
contents in a source system or with the general ledger, establishing synchronization and reconciliation points is
necessary.

Tools
While an ETL process can be created using almost any programming language, creating them from scratch is quite
complex. Increasingly, companies are buying ETL tools to help in the creation of ETL processes.
A good ETL tool must be able to communicate with the many different relational databases and read the various file
formats used throughout an organization. ETL tools have started to migrate into Enterprise Application Integration,
or even Enterprise Service Bus, systems that now cover much more than just the extraction, transformation and
loading of data. Many ETL vendors now have data profiling, data quality and metadata capabilities

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WHAT IS AN ETL PROCESS?

WHAT IS AN ETL PROCESS?
ETL process - acronymic for extraction, transformation and loading operations are a fundamental phenomenon in
a data warehouse. Whenever DML (data manipulation language) operations such as INSERT, UPDATE OR
DELETE are issued on the source database, data extraction occurs.
After data extraction and transformation have taken place, data are loaded into the data warehouse. Incremental
loading is beneficial in the sense that only that have changed after the last data extraction and transformation are
loaded.
ORACLE CHANGE DATA CAPTURE FRAMEWORK
The change data framework is designed for capturing only insert, delete and update operations on the oracle
database, that is to say they are 'DML sensitive'. Below is architecture of change data capture framework. Below
is architecture illustrating the flow of information in an oracle data capture framework.

Figure 1.Change data capture framework architecture.
Implementing oracle change data capture is very simple. Following the following steps, guides you through the
whole implementation process.
Source table identification: Firstly, the source tables must be identified.

Choose a publisher: The publisher is responsible for creating and managing the change tables. Note that the
publisher must be granted SELECT_CATALOG_ROLE, which enables the publisher to select data from any
SYS-owned dictionary tables or views and EXECUTE_CATALOG_ROLE, which enables the publisher to
receive execute privileges on any SYS-owned packages. He also needs select privilege on the source tables

Change tables creation: When data extraction occurs, change data are stored in the change tables. Also stored in
the change tables are system metadata, imperative for the smooth functioning of the change tables. In order to
create the change tables, the procedure DBMS_LOGMNR_CDC_PUBLISH.CREATE_CHANGE_TABLE is
executed. It is important to note that each source table must have its own change table.
Choose the subscriber: The publisher must grant select privilege on the change tables and source tables to the
subscriber. You might have more than one subscriber as the case may be.
Subscription handle creation: Creating the subscription handle is very pertinent because it is used to specifically
identify a particular subscription. Irrespective of the number of tables subscribed to, one and only one subscription
handle must be created. To create a subscription handle, first define a variable, and then execute the
DBMS_LOGMNR_CDC_SUBSCRIBE.GET_SUBSCRIPTION HANDLE procedure.
Subscribe to the change tables: The data in the change tables are usually enormous, thus only data of interest
should be subscribed to. To subscribe, the
DBMS_LOGMNR_CDC_SUBSCRIBE.SUBSCRIBE procedure is executed.
Subscription activation: Subscription is activated only once and after activation, subscription cannot be
modified. Activate your subscription using the
DBMS_LOGMNR_CDC_SUBSCRIBE.ACTIVATE_SUBSCRIPTION procedure.
Subscription window creation: Since subscription to the change tables does not stop data extraction from the

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source table, a window is set up using the
DBMS_LOGMNR_CDC_SUBSCRIBE.EXTEND_WINDOW procedure. However, it is to be noted that changes
effected on the source system after this procedure is executed will not be available until the window is flushed and
re-extended.
Subscription views creation: In order to view and query the change data, a subscriber view is prepared for
individual source tables that the subscriber subscribes to using
DBMS_LOGMNR_CDC_SUBSCRIBE.PREPARE_SUBSCRIBER_VIEW procedure. However, you need to
define the variable in which the subscriber view name would be returned. Also, you would be prompted for the
subscription handle, source schema name and source table name.
Query the change tables: Resident in the subscriber view are not only the change data needed but also metadata,
fundamental to the efficient use of the change data such as OPERATION$, CSCN$, USERNAME$ etc. Since you
already know the view name, you can describe the view and then query it using the conventional select statement.

Drop the subscriber view: The dropping of the subscriber view is carried out only when you are sure you are
done with the data in the view and they are no longer needed (i.e. they've been viewed and extracted). It is
imperative to note that each subscriber view must be dropped individually using the
DBMS_LOGMNR_CDC_SUBSCRIBE.DROP_SUBSCRIBE_VIEW procedure. Purge the subscription view:
To facilitate the extraction of change data again, the subscription window must be purged using the
DBMS_LOGMNR_CDC_SUBSCRIBE.PURGE_WINDOW procedure.

ETL Process
Here is the typical ETL Process:
Specify metadata for sources, such as tables in an operational system
Specify metadata for targets—the tables and other data stores in a data warehouse
Specify how data is extracted, transformed, and loaded from sources to targets
Schedule and execute the processes
Monitor the execution
A ETL tool thus involves the following components:
A design tool for building the mapping and the process flows
A monitor tool for executing and monitoring the process
The process flows are sequences of steps for the extraction, transformation, and loading of data. The data is
extracted from sources (inputs to an operation) and loaded into a set of targets (outputs of an operation) that make up
a data warehouse or a data mart.
A good ETL design tool should provide the change management features that satisfies the following criteria:
A metadata respository that stores the metdata about sources, targets, and the transformations that connect them.
Enforce metadata source control for team-based development : Multiple designers should be able to work with the
same metadata repository at the same time without overwriting each other’s changes. Each developer should be able
to check out metadata from the respository into their project or workspace, modify them, and check the changes
back into the respository.
After a metadata object has been checked out by one person, it is locked so that it cannot be updated by another
person until the object has been checked back in.

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Overview of ETL in Data Warehouses
You need to load your data warehouse regularly so that it can serve its purpose of facilitating business analysis.
To do this, data from one or more operational systems needs to be extracted and copied into the data warehouse. The
process of extracting data from source systems and bringing it into the data warehouse is commonly called ETL,
which stands for extraction, transformation, and loading. The acronym ETL is perhaps too simplistic, because it
omits the transportation phase and implies that each of the other phases of the process is distinct. We refer to the
entire process, including data loading, as ETL. You should understand that ETL refers to a broad process, and not
three well-defined steps.
The methodology and tasks of ETL have been well known for many years, and are not necessarily unique to data
warehouse environments: a wide variety of proprietary applications and database systems are the IT backbone of
any enterprise. Data has to be shared between applications or systems, trying to integrate them, giving at least two
applications the same picture of the world. This data sharing was mostly addressed by mechanisms similar to what
we now call ETL.
Data warehouse environments face the same challenge with the additional burden that they not only have to
exchange but to integrate, rearrange and consolidate data over many systems, thereby providing a new unified
information base for business intelligence. Additionally, the data volume in data warehouse environments tends to
be very large.
What happens during the ETL process? During extraction, the desired data is identified and extracted from many
different sources, including database systems and applications. Very often, it is not possible to identify the specific
subset of interest, therefore more data than necessary has to be extracted, so the identification of the relevant data
will be done at a later point in time. Depending on the source system's capabilities (for example, operating system
resources), some transformations may take place during this extraction process. The size of the extracted data varies
from hundreds of kilobytes up to gigabytes, depending on the source system and the business situation. The same is
true for the time delta between two (logically) identical extractions: the time span may vary between days/hours and
minutes to near real-time. Web server log files for example can easily become hundreds of megabytes in a very short
period of time.
After extracting data, it has to be physically transported to the target system or an intermediate system for further
processing. Depending on the chosen way of transportation, some transformations can be done during this process,
too. For example, a SQL statement which directly accesses a remote target through a gateway can concatenate two
columns as part of the SELECT statement.
The emphasis in many of the examples in this section is scalability. Many long-time users of Oracle Database are
experts in programming complex data transformation logic using PL/SQL. These chapters suggest alternatives for
many such data manipulation operations, with a particular emphasis on implementations that take advantage of
Oracle's new SQL functionality, especially for ETL and the parallel query infrastructure.

ETL Tools for Data Warehouses
Designing and maintaining the ETL process is often considered one of the most difficult and resource-intensive
portions of a data warehouse project. Many data warehousing projects use ETL tools to manage this process. Oracle
Warehouse Builder (OWB), for example, provides ETL capabilities and takes advantage of inherent database
abilities. Other data warehouse builders create their own ETL tools and processes, either inside or outside the
database.
Besides the support of extraction, transformation, and loading, there are some other tasks that are important for a

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successful ETL implementation as part of the daily operations of the data warehouse and its support for further
enhancements. Besides the support for designing a data warehouse and the data flow, these tasks are typically
addressed by ETL tools such as OWB.
Oracle is not an ETL tool and does not provide a complete solution for ETL. However, Oracle does provide a rich
set of capabilities that can be used by both ETL tools and customized ETL solutions. Oracle offers techniques for
transporting data between Oracle databases, for transforming large volumes of data, and for quickly loading new
data into a data warehouse.

Daily Operations in Data Warehouses
The successive loads and transformations must be scheduled and processed in a specific order. Depending on the
success or failure of the operation or parts of it, the result must be tracked and subsequent, alternative processes
might be started. The control of the progress as well as the definition of a business workflow of the operations are
typically addressed by ETL tools such as Oracle Warehouse Builder.

Evolution of the Data Warehouse
As the data warehouse is a living IT system, sources and targets might change. Those changes must be maintained
and tracked through the lifespan of the system without overwriting or deleting the old ETL process flow
information. To build and keep a level of trust about the information in the warehouse, the process flow of each
individual record in the warehouse can be reconstructed at any point in time in the future in an ideal case.

Overview of Extraction in Data Warehouses
Extraction is the operation of extracting data from a source system for further use in a data warehouse environment.
This is the first step of the ETL process. After the extraction, this data can be transformed and loaded into the data
warehouse.
The source systems for a data warehouse are typically transaction processing applications. For example, one of the
source systems for a sales analysis data warehouse might be an order entry system that records all of the current
order activities.
Designing and creating the extraction process is often one of the most time-consuming tasks in the ETL process and,
indeed, in the entire data warehousing process. The source systems might be very complex and poorly documented,
and thus determining which data needs to be extracted can be difficult. The data has to be extracted normally not
only once, but several times in a periodic manner to supply all changed data to the data warehouse and keep it up-to-
date. Moreover, the source system typically cannot be modified, nor can its performance or availability be adjusted,
to accommodate the needs of the data warehouse extraction process.
These are important considerations for extraction and ETL in general. This chapter, however, focuses on the
technical considerations of having different kinds of sources and extraction methods. It assumes that the data
warehouse team has already identified the data that will be extracted, and discusses common techniques used for
extracting data from source databases.
Designing this process means making decisions about the following two main aspects:
Which extraction method do I choose?
This influences the source system, the transportation process, and the time needed for refreshing the warehouse.
How do I provide the extracted data for further processing?
This influences the transportation method, and the need for cleaning and transforming the data.

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Introduction to Extraction Methods in Data Warehouses
The extraction method you should choose is highly dependent on the source system and also from the business
needs in the target data warehouse environment. Very often, there is no possibility to add additional logic to the
source systems to enhance an incremental extraction of data due to the performance or the increased workload of
these systems. Sometimes even the customer is not allowed to add anything to an out-of-the-box application system.
The estimated amount of the data to be extracted and the stage in the ETL process (initial load or maintenance of
data) may also impact the decision of how to extract, from a logical and a physical perspective. Basically, you have
to decide how to extract data logically and physically.

Logical Extraction Methods
There are two types of logical extraction:
Full Extraction
Incremental Extraction

Full Extraction
The data is extracted completely from the source system. Because this extraction reflects all the data currently
available on the source system, there's no need to keep track of changes to the data source since the last successful
extraction. The source data will be provided as-is and no additional logical information (for example, timestamps) is
necessary on the source site. An example for a full extraction may be an export file of a distinct table or a remote
SQL statement scanning the complete source table.

Incremental Extraction
At a specific point in time, only the data that has changed since a well-defined event back in history will be
extracted. This event may be the last time of extraction or a more complex business event like the last booking day
of a fiscal period. To identify this delta change there must be a possibility to identify all the changed information
since this specific time event. This information can be either provided by the source data itself such as an application
column, reflecting the last-changed timestamp or a change table where an appropriate additional mechanism keeps
track of the changes besides the originating transactions. In most cases, using the latter method means adding
extraction logic to the source system.
Many data warehouses do not use any change-capture techniques as part of the extraction process. Instead, entire
tables from the source systems are extracted to the data warehouse or staging area, and these tables are compared
with a previous extract from the source system to identify the changed data. This approach may not have significant
impact on the source systems, but it clearly can place a considerable burden on the data warehouse processes,
particularly if the data volumes are large.
Oracle's Change Data Capture mechanism can extract and maintain such delta information.

Physical Extraction Methods
Depending on the chosen logical extraction method and the capabilities and restrictions on the source side, the
extracted data can be physically extracted by two mechanisms. The data can either be extracted online from the
source system or from an offline structure. Such an offline structure might already exist or it might be generated by
an extraction routine.

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There are the following methods of physical extraction:
Online Extraction
Offline Extraction

Online Extraction
The data is extracted directly from the source system itself. The extraction process can connect directly to the source
system to access the source tables themselves or to an intermediate system that stores the data in a preconfigured
manner (for example, snapshot logs or change tables). Note that the intermediate system is not necessarily
physically different from the source system.
With online extractions, you need to consider whether the distributed transactions are using original source objects
or prepared source objects.

Offline Extraction
The data is not extracted directly from the source system but is staged explicitly outside the original source system.
The data already has an existing structure (for example, redo logs, archive logs or transportable tablespaces) or was
created by an extraction routine.
You should consider the following structures:
Flat files
Data in a defined, generic format. Additional information about the source object is necessary for further processing.

Dump files
Oracle-specific format. Information about the containing objects may or may not be included, depending on the
chosen utility. Redo and archive logs Information is in a special, additional dump file.
Transportable tablespaces
A powerful way to extract and move large volumes of data between Oracle databases. Oracle Corporation
recommends that you use transportable tablespaces whenever possible, because they can provide considerable
advantages in performance and manageability over other extraction techniques.

Change Data Capture
An important consideration for extraction is incremental extraction, also called Change Data Capture. If a data
warehouse extracts data from an operational system on a nightly basis, then the data warehouse requires only the
data that has changed since the last extraction (that is, the data that has been modified in the past 24 hours). Change
Data Capture is also the key-enabling technology for providing near real-time, or on-time, data warehousing.
When it is possible to efficiently identify and extract only the most recently changed data, the extraction process (as
well as all downstream operations in the ETL process) can be much more efficient, because it must extract a much
smaller volume of data. Unfortunately, for many source systems, identifying the recently modified data may be
difficult or intrusive to the operation of the system. Change Data Capture is typically the most challenging technical
issue in data extraction.
Because change data capture is often desirable as part of the extraction process and it might not be possible to use
the Change Data Capture mechanism, this section describes several techniques for implementing a self-developed
change capture on Oracle Database source systems:
Timestamps
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Partitioning
Triggers

These techniques are based upon the characteristics of the source systems, or may require modifications to the
source systems. Thus, each of these techniques must be carefully evaluated by the owners of the source system prior
to implementation.
Each of these techniques can work in conjunction with the data extraction technique discussed previously. For
example, timestamps can be used whether the data is being unloaded to a file or accessed through a distributed
query.

Timestamps
The tables in some operational systems have timestamp columns. The timestamp specifies the time and date that a
given row was last modified. If the tables in an operational system have columns containing timestamps, then the
latest data can easily be identified using the timestamp columns. For example, the following query might be useful
for extracting today's data from an orders table:
SELECT * FROM orders
WHERE TRUNC(CAST(order_date AS date),'dd') =
TO_DATE(SYSDATE,'dd-mon-yyyy');

If the timestamp information is not available in an operational source system, you will not always be able to modify
the system to include timestamps. Such modification would require, first, modifying the operational system's tables
to include a new timestamp column and then creating a trigger to update the timestamp column following every
operation that modifies a given row.

Partitioning
Some source systems might use range partitioning, such that the source tables are partitioned along a date key,
which allows for easy identification of new data. For example, if you are extracting from an orders table, and the
orders table is partitioned by week, then it is easy to identify the current week's data.

Data Warehousing Extraction ways
You can extract data in two ways:
Extraction Using Data Files
Extraction Through Distributed Operations

Extraction Using Data Files
Most database systems provide mechanisms for exporting or unloading data from the internal database format into
flat files. Extracts from mainframe systems often use COBOL programs, but many databases, as well as third-party
software vendors, provide export or unload utilities.
Data extraction does not necessarily mean that entire database structures are unloaded in flat files. In many cases, it
may be appropriate to unload entire database tables or objects. In other cases, it may be more appropriate to unload
only a subset of a given table such as the changes on the source system since the last extraction or the results of
joining multiple tables together. Different extraction techniques vary in their capabilities to support these two

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scenarios.
When the source system is an Oracle database, several alternatives are available for extracting data into files:
Extracting into Flat Files Using SQL*Plus
Extracting into Flat Files Using OCI or Pro*C Programs
Exporting into Export Files Using the Export Utility
Extracting into Export Files Using External Tables

Extracting into Flat Files Using SQL*Plus
The most basic technique for extracting data is to execute a SQL query in SQL*Plus and direct the output of the
query to a file. For example, to extract a flat file, country_city.log, with the pipe sign as delimiter between column
values, containing a list of the cities in the US in the tables countries and customers, the following SQL script could
be run:
SET echo off SET pagesize 0 SPOOL country_city.log
SELECT distinct t1.country_name ||'|'|| t2.cust_city
FROM countries t1, customers t2 WHERE t1.country_id = t2.country_id
AND t1.country_name= 'United States of America';
SPOOL off

The exact format of the output file can be specified using SQL*Plus system variables.
This extraction technique offers the advantage of storing the result in a customized format. Note that using the
external table data pump unload facility, you can also extract the result of an arbitrary SQL operation. The example
previously extracts the results of a join.
This extraction technique can be parallelized by initiating multiple, concurrent SQL*Plus sessions, each session
running a separate query representing a different portion of the data to be extracted. For example, suppose that you
wish to extract data from an orders table, and that the orders table has been range partitioned by month, with
partitions orders_jan1998, orders_feb1998, and so on. To extract a single year of data from the orders table, you
could initiate 12 concurrent SQL*Plus sessions, each extracting a single partition. The SQL script for one such
session could be:
SPOOL order_jan.dat
SELECT * FROM orders PARTITION (orders_jan1998);
SPOOL OFF

These 12 SQL*Plus processes would concurrently spool data to 12 separate files. You can then concatenate them if
necessary (using operating system utilities) following the extraction. If you are planning to use SQL*Loader for
loading into the target, these 12 files can be used as is for a parallel load with 12 SQL*Loader sessions.
Even if the orders table is not partitioned, it is still possible to parallelize the extraction either based on logical or
physical criteria. The logical method is based on logical ranges of column values, for example:
SELECT ... WHERE order_date
BETWEEN TO_DATE('01-JAN-99') AND TO_DATE('31-JAN-99');

The physical method is based on a range of values. By viewing the data dictionary, it is possible to identify the
Oracle Database data blocks that make up the orders table. Using this information, you could then derive a set of

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rowid-range queries for extracting data from the orders table:
SELECT * FROM orders WHERE rowid BETWEEN value1 and value2;

Parallelizing the extraction of complex SQL queries is sometimes possible, although the process of breaking a single
complex query into multiple components can be challenging. In particular, the coordination of independent
processes to guarantee a globally consistent view can be difficult. Unlike the SQL*Plus approach, using the new
external table data pump unload functionality provides transparent parallel capabilities.
Note that all parallel techniques can use considerably more CPU and I/O resources on the source system, and the
impact on the source system should be evaluated before parallelizing any extraction technique.

Extracting into Flat Files Using OCI or Pro*C Programs
OCI programs (or other programs using Oracle call interfaces, such as Pro*C programs), can also be used to extract
data. These techniques typically provide improved performance over the SQL*Plus approach, although they also
require additional programming. Like the SQL*Plus approach, an OCI program can extract the results of any SQL
query. Furthermore, the parallelization techniques described for the SQL*Plus approach can be readily applied to
OCI programs as well.
When using OCI or SQL*Plus for extraction, you need additional information besides the data itself. At minimum,
you need information about the extracted columns. It is also helpful to know the extraction format, which might be
the separator between distinct columns.

Exporting into Export Files Using the Export Utility
The Export utility allows tables (including data) to be exported into Oracle Database export files. Unlike the
SQL*Plus and OCI approaches, which describe the extraction of the results of a SQL statement, Export provides a
mechanism for extracting database objects. Thus, Export differs from the previous approaches in several important
ways:
The export files contain metadata as well as data. An export file contains not only the raw data of a table, but also
information on how to re-create the table, potentially including any indexes, constraints, grants, and other attributes
associated with that table.
A single export file may contain a subset of a single object, many database objects, or even an entire schema.
Export cannot be directly used to export the results of a complex SQL query. Export can be used only to extract
subsets of distinct database objects.
The output of the Export utility must be processed using the Import utility.
Oracle provides the original Export and Import utilities for backward compatibility and the data pump export/import
infrastructure for high-performant, scalable and parallel extraction. See Oracle Database Utilities for further details.

Extracting into Export Files Using External Tables
In addition to the Export Utility, you can use external tables to extract the results from any SELECT operation. The
data is stored in the platform independent, Oracle-internal data pump format and can be processed as regular
external table on the target system. The following example extracts the result of a join operation in parallel into the
four specified files. The only allowed external table type for extracting data is the Oracle-internal format
ORACLE_DATAPUMP.
CREATE DIRECTORY def_dir AS '/net/dlsun48/private/hbaer/WORK/FEATURES/et';
DROP TABLE extract_cust;

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CREATE TABLE extract_cust
ORGANIZATION EXTERNAL
(TYPE ORACLE_DATAPUMP DEFAULT DIRECTORY def_dir ACCESS PARAMETERS
(NOBADFILE NOLOGFILE)
LOCATION ('extract_cust1.exp', 'extract_cust2.exp', 'extract_cust3.exp',
'extract_cust4.exp'))
PARALLEL 4 REJECT LIMIT UNLIMITED AS
SELECT c.*, co.country_name, co.country_subregion, co.country_region
FROM customers c, countries co where co.country_id=c.country_id;

The total number of extraction files specified limits the maximum degree of parallelism for the write operation. Note
that the parallelizing of the extraction does not automatically parallelize the SELECT portion of the statement.
Unlike using any kind of export/import, the metadata for the external table is not part of the created files when using
the external table data pump unload. To extract the appropriate metadata for the external table, use the
DBMS_METADATA package, as illustrated in the following statement:
SET LONG 2000
SELECT DBMS_METADATA.GET_DDL('TABLE','EXTRACT_CUST') FROM DUAL;

Extraction Through Distributed Operations
Using distributed-query technology, one Oracle database can directly query tables located in various different source
systems, such as another Oracle database or a legacy system connected with the Oracle gateway technology.
Specifically, a data warehouse or staging database can directly access tables and data located in a connected source
system. Gateways are another form of distributed-query technology. Gateways allow an Oracle database (such as a
data warehouse) to access database tables stored in remote, non-Oracle databases. This is the simplest method for
moving data between two Oracle databases because it combines the extraction and transformation into a single step,
and requires minimal programming. However, this is not always feasible.
Suppose that you wanted to extract a list of employee names with department names from a source database and
store this data into the data warehouse. Using an Oracle Net connection and distributed-query technology, this can
be achieved using a single SQL statement:
CREATE TABLE country_city AS SELECT distinct t1.country_name, t2.cust_city
FROM countries@source_db t1, customers@source_db t2
WHERE t1.country_id = t2.country_id
AND t1.country_name='United States of America';

This statement creates a local table in a data mart, country_city, and populates it with data from the countries and
customers tables on the source system.
This technique is ideal for moving small volumes of data. However, the data is transported from the source system
to the data warehouse through a single Oracle Net connection. Thus, the scalability of this technique is limited. For
larger data volumes, file-based data extraction and transportation techniques are often more scalable and thus more
appropriate.
13 Transportation in Data Warehouses
The following topics provide information about transporting data into a data warehouse:
Overview of Transportation in Data Warehouses

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Introduction to Transportation Mechanisms in Data Warehouses

Transportation in Data Warehouses
Transportation is the operation of moving data from one system to another system. In a data warehouse
environment, the most common requirements for transportation are in moving data from:
A source system to a staging database or a data warehouse database
A staging database to a data warehouse
A data warehouse to a data mart
Transportation is often one of the simpler portions of the ETL process, and can be integrated with other portions of
the process.

Introduction to Transportation Mechanisms in Data Warehouses
You have three basic choices for transporting data in warehouses:
Transportation Using Flat Files
Transportation Through Distributed Operations
Transportation Using Transportable Tablespaces

Transportation Using Flat Files
The most common method for transporting data is by the transfer of flat files, using mechanisms such as FTP or
other remote file system access protocols. Data is unloaded or exported from the source system into flat files using
techniques, and is then transported to the target platform using FTP or similar mechanisms.
Because source systems and data warehouses often use different operating systems and database systems, using flat
files is often the simplest way to exchange data between heterogeneous systems with minimal transformations.
However, even when transporting data between homogeneous systems, flat files are often the most efficient and
most easy-to-manage mechanism for data transfer.

Transportation Through Distributed Operations
Distributed queries, either with or without gateways, can be an effective mechanism for extracting data. These
mechanisms also transport the data directly to the target systems, thus providing both extraction and transformation
in a single step. Depending on the tolerable impact on time and system resources, these mechanisms can be well
suited for both extraction and transformation.
As opposed to flat file transportation, the success or failure of the transportation is recognized immediately with the
result of the distributed query or transaction.

Transportation Using Transportable Tablespaces
Oracle transportable tablespaces are the fastest way for moving large volumes of data between two Oracle databases.
Previous to the introduction of transportable tablespaces, the most scalable data transportation mechanisms relied on
moving flat files containing raw data. These mechanisms required that data be unloaded or exported into files from
the source database, Then, after transportation, these files were loaded or imported into the target database.
Transportable tablespaces entirely bypass the unload and reload steps.
Using transportable tablespaces, Oracle data files (containing table data, indexes, and almost every other Oracle
database object) can be directly transported from one database to another. Furthermore, like import and export,

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transportable tablespaces provide a mechanism for transporting metadata in addition to transporting data.
Transportable tablespaces have some limitations: source and target systems must be running Oracle8i (or higher),
must use the same character set, and, prior to Oracle Database 10g, must run on the same operating system. For
details on how to transport tablespace between operating systems.
The most common applications of transportable tablespaces in data warehouses are in moving data from a staging
database to a data warehouse, or in moving data from a data warehouse to a data mart.

Transportable Tablespaces Example
Suppose that you have a data warehouse containing sales data, and several data marts that are refreshed monthly.
Also suppose that you are going to move one month of sales data from the data warehouse to the data mart.

Step 1 Place the Data to be Transported into its own Tablespace
The current month's data must be placed into a separate tablespace in order to be transported. In this example, you
have a tablespace ts_temp_sales, which will hold a copy of the current month's data. Using the CREATE TABLE ...
AS SELECT statement, the current month's data can be efficiently copied to this tablespace:
CREATE TABLE temp_jan_sales NOLOGGING TABLESPACE ts_temp_sales
AS SELECT * FROM sales
WHERE time_id BETWEEN '31-DEC-1999' AND '01-FEB-2000';

Following this operation, the tablespace ts_temp_sales is set to read-only:
ALTER TABLESPACE ts_temp_sales READ ONLY;

A tablespace cannot be transported unless there are no active transactions modifying the tablespace. Setting the
tablespace to read-only enforces this.
The tablespace ts_temp_sales may be a tablespace that has been especially created to temporarily store data for use
by the transportable tablespace features. this tablespace can be set to read/write, and, if desired, the table
temp_jan_sales can be dropped, or the tablespace can be re-used for other transportations or for other purposes.
In a given transportable tablespace operation, all of the objects in a given tablespace are transported. Although only
one table is being transported in this example, the tablespace ts_temp_sales could contain multiple tables. For
example, perhaps the data mart is refreshed not only with the new month's worth of sales transactions, but also with
a new copy of the customer table. Both of these tables could be transported in the same tablespace. Moreover, this
tablespace could also contain other database objects such as indexes, which would also be transported.
Additionally, in a given transportable-tablespace operation, multiple tablespaces can be transported at the same time.
This makes it easier to move very large volumes of data between databases. Note, however, that the transportable
tablespace feature can only transport a set of tablespaces which contain a complete set of database objects without
dependencies on other tablespaces. For example, an index cannot be transported without its table, nor can a partition
be transported without the rest of the table. You can use the DBMS_TTS package to check that a tablespace is
transportable.

In this step, we have copied the January sales data into a separate tablespace; however, in some cases, it may be
possible to leverage the transportable tablespace feature without even moving data to a separate tablespace. If the
sales table has been partitioned by month in the data warehouse and if each partition is in its own tablespace, then it
may be possible to directly transport the tablespace containing the January data. Suppose the January partition,

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sales_jan2000, is located in the tablespace ts_sales_jan2000. Then the tablespace ts_sales_jan2000 could potentially
be transported, rather than creating a temporary copy of the January sales data in the ts_temp_sales.
However, the same conditions must be satisfied in order to transport the tablespace ts_sales_jan2000 as are required
for the specially created tablespace. First, this tablespace must be set to READ ONLY. Second, because a single
partition of a partitioned table cannot be transported without the remainder of the partitioned table also being
transported, it is necessary to exchange the January partition into a separate table (using the ALTER TABLE
statement) to transport the January data. The EXCHANGE operation is very quick, but the January data will no
longer be a part of the underlying sales table, and thus may be unavailable to users until this data is exchanged back
into the sales table after the export of the metadata. The January data can be exchanged back into the sales table after
you complete step 3.

Step 2 Export the Metadata
The Export utility is used to export the metadata describing the objects contained in the transported tablespace. For
our example scenario, the Export command could be:
EXP TRANSPORT_TABLESPACE=y TABLESPACES=ts_temp_sales FILE=jan_sales.dmp
This operation will generate an export file, jan_sales.dmp. The export file will be small, because it contains only
metadata. In this case, the export file will contain information describing the table temp_jan_sales, such as the
column names, column datatype, and all other information that the target Oracle database will need in order to
access the objects in ts_temp_sales.

Step 3 Copy the Datafiles and Export File to the Target System
Copy the data files that make up ts_temp_sales, as well as the export file jan_sales.dmp to the data mart platform,
using any transportation mechanism for flat files. Once the datafiles have been copied, the tablespace ts_temp_sales
can be set to READ WRITE mode if desired.
Step 4 Import the Metadata
Once the files have been copied to the data mart, the metadata should be imported into the data mart:
IMP TRANSPORT_TABLESPACE=y DATAFILES='/db/tempjan.f'
TABLESPACES=ts_temp_sales FILE=jan_sales.dmp

At this point, the tablespace ts_temp_sales and the table temp_sales_jan are accessible in the data mart. You can
incorporate this new data into the data mart's tables.
You can insert the data from the temp_sales_jan table into the data mart's sales table in one of two ways:
INSERT /*+ APPEND */ INTO sales SELECT * FROM temp_sales_jan;

Following this operation, you can delete the temp_sales_jan table (and even the entire ts_temp_sales tablespace).
Alternatively, if the data mart's sales table is partitioned by month, then the new transported tablespace and the
temp_sales_jan table can become a permanent part of the data mart. The temp_sales_jan table can become a
partition of the data mart's sales table:
ALTER TABLE sales ADD PARTITION sales_00jan VALUES
LESS THAN (TO_DATE('01-feb-2000','dd-mon-yyyy'));
ALTER TABLE sales EXCHANGE PARTITION sales_00jan
WITH TABLE temp_sales_jan INCLUDING INDEXES WITH VALIDATION;

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Other Uses of Transportable Tablespaces
The previous example illustrates a typical scenario for transporting data in a data warehouse. However, transportable
tablespaces can be used for many other purposes. In a data warehousing environment, transportable tablespaces
should be viewed as a utility (much like Import/Export or SQL*Loader), whose purpose is to move large volumes of
data between Oracle databases. When used in conjunction with parallel data movement operations such as the
CREATE TABLE ... AS SELECT and INSERT ... AS SELECT statements, transportable tablespaces provide an
important mechanism for quickly transporting data for many purposes.

Overview of Loading and Transformation in Data Warehouses

Data transformations are often the most complex and, in terms of processing time, the most costly part of the
extraction, transformation, and loading (ETL) process. They can range from simple data conversions to extremely
complex data scrubbing techniques. Many, if not all, data transformations can occur within an Oracle database,
although transformations are often implemented outside of the database (for example, on flat files) as well.

This chapter introduces techniques for implementing scalable and efficient data transformations within the
Oracle Database. The examples in this chapter are relatively simple. Real-world data transformations are often
considerably more complex. However, the transformation techniques introduced in this chapter meet the majority of
real-world data transformation requirements, often with more scalability and less programming than alternative
approaches.

This chapter does not seek to illustrate all of the typical transformations that would be encountered in a data
warehouse, but to demonstrate the types of fundamental technology that can be applied to implement these
transformations and to provide guidance in how to choose the best techniques.

Transformation Flow
From an architectural perspective, you can transform your data in two ways:

1) Multistage Data Transformation
2) Pipelined Data Transformation

Multistage Data Transformation

The data transformation logic for most data warehouses consists of multiple steps. For example, in
transforming new records to be inserted into a sales table, there may be separate logical transformation steps to
validate each dimension key.

Figure 14-1 offers a graphical way of looking at the transformation logic.

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Figure 14-1 Multistage Data Transformation

When using Oracle Database as a transformation engine, a common strategy is to implement each transformation as
a separate SQL operation and to create a separate, temporary staging table (such as the tables new_sales_step1 and
new_sales_step2 in Figure 14-1) to store the incremental results for each step. This load-then-transform strategy also
provides a natural checkpointing scheme to the entire transformation process, which enables to the process to be
more easily monitored and restarted. However, a disadvantage to multistaging is that the space and time
requirements increase.
It may also be possible to combine many simple logical transformations into a single SQL statement or single
PL/SQL procedure. Doing so may provide better performance than performing each step independently, but it may
also introduce difficulties in modifying, adding, or dropping individual transformations, as well as recovering from
failed transformations.
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Pipelined Data Transformation
The ETL process flow can be changed dramatically and the database becomes an integral part of the ETL solution.
The new functionality renders some of the former necessary process steps obsolete while some others can be
remodeled to enhance the data flow and the data transformation to become more scalable and non-interruptive. The
task shifts from serial transform-then-load process (with most of the tasks done outside the database) or load-then-
transform process, to an enhanced transform-while-loading.
Oracle offers a wide variety of new capabilities to address all the issues and tasks relevant in an ETL scenario. It is
important to understand that the database offers toolkit functionality rather than trying to address a one-size-fits-all
solution. The underlying database has to enable the most appropriate ETL process flow for a specific customer need,
and not dictate or constrain it from a technical perspective. Figure 14-2 illustrates the new functionality, which is
discussed throughout later sections.

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Figure 14-2 Pipelined Data Transformation

Description of the illustration dwhsg107.gif

Loading Mechanisms

You can use the following mechanisms for loading a data warehouse:

• Loading a Data Warehouse with SQL*Loader
• Loading a Data Warehouse with External Tables
• Loading a Data Warehouse with OCI and Direct-Path APIs
• Loading a Data Warehouse with Export/Import

Schemas in Data Warehouses
A schema is a collection of database objects, including tables, views, indexes, and synonyms.

There is a variety of ways of arranging schema objects in the schema models designed for data warehousing. One
data warehouse schema model is a star schema.

Star Schemas
The star schema is perhaps the simplest data warehouse schema. It is called a star schema because the entity-
relationship diagram of this schema resembles a star, with points radiating from a central table. The center of the star
consists of a large fact table and the points of the star are the dimension tables.

A star query is a join between a fact table and a number of dimension tables. Each dimension table is joined to the
fact table using a primary key to foreign key join, but the dimension tables are not joined to each other. The
optimizer recognizes star queries and generates efficient execution plans for them.

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A typical fact table contains keys and measures. For example, in the sh sample schema, the fact table, sales, contain
the measures quantity_sold, amount, and cost, and the keys cust_id, time_id, prod_id, channel_id, and promo_id.
The dimension tables are customers, times, products, channels, and promotions. The products dimension table, for
example, contains information about each product number that appears in the fact table.

A star join is a primary key to foreign key join of the dimension tables to a fact table.

The main advantages of star schemas are that they:

Provide a direct and intuitive mapping between the business entities being analyzed by end users and the
schema design.

Provide highly optimized performance for typical star queries.

Are widely supported by a large number of business intelligence tools, which may anticipate or even require
that the data warehouse schema contain dimension tables.

Star schemas are used for both simple data marts and very large data warehouses.

Figure 19-2 presents a graphical representation of a star schema.

Figure 19-2 Star Schema

Snowflake Schemas
The snowflake schema is a more complex data warehouse model than a star schema, and is a type of star schema. It
is called a snowflake schema because the diagram of the schema resembles a snowflake.

Snowflake schemas normalize dimensions to eliminate redundancy. That is, the dimension data has been grouped
into multiple tables instead of one large table. For example, a product dimension table in a star schema might be
normalized into a products table, a product_category table, and a product_manufacturer table in a snowflake

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schema. While this saves space, it increases the number of dimension tables and requires more foreign key joins.
The result is more complex queries and reduced query performance. Figure 19-3 presents a graphical representation
of a snowflake schema.

Figure 19-3 Snowflake Schema

Description of the illustration dwhsg008.gif

Optimizing Star Queries

You should consider the following when using star queries:

Tuning Star Queries

Using Star Transformation

Tuning Star Queries

To get the best possible performance for star queries, it is important to follow some basic guidelines:

A bitmap index should be built on each of the foreign key columns of the fact table or tables.

The initialization parameter STAR_TRANSFORMATION_ENABLED should be set to TRUE. This enables an
important optimizer feature for star-queries. It is set to FALSE by default for backward-compatibility.

When a data warehouse satisfies these conditions, the majority of the star queries running in the data warehouse will
use a query execution strategy known as the star transformation. The star transformation provides very efficient
query performance for star queries.

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Using Star Transformation
The star transformation is a powerful optimization technique that relies upon implicitly rewriting (or transforming)
the SQL of the original star query. The end user never needs to know any of the details about the star transformation.
Oracle's query optimizer automatically chooses the star transformation where appropriate.

The star transformation is a query transformation aimed at executing star queries efficiently. Oracle processes
a star query using two basic phases. The first phase retrieves exactly the necessary rows from the fact table (the
result set). Because this retrieval utilizes bitmap indexes, it is very efficient. The second phase joins this result set to
the dimension tables. An example of an end user query is: "What were the sales and profits for the grocery
department of stores in the west and southwest sales districts over the last three quarters?" This is a simple star
query.

How Oracle Chooses to Use Star Transformation

The optimizer generates and saves the best plan it can produce without the transformation. If the transformation
is enabled, the optimizer then tries to apply it to the query and, if applicable, generates the best plan using the
transformed query. Based on a comparison of the cost estimates between the best plans for the two versions of the
query, the optimizer will then decide whether to use the best plan for the transformed or untransformed version.

If the query requires accessing a large percentage of the rows in the fact table, it might be better to use a full
table scan and not use the transformations. However, if the constraining predicates on the dimension tables are
sufficiently selective that only a small portion of the fact table needs to be retrieved, the plan based on the
transformation will probably be superior.

Note that the optimizer generates a subquery for a dimension table only if it decides that it is reasonable to do
so based on a number of criteria. There is no guarantee that subqueries will be generated for all dimension tables.
The optimizer may also decide, based on the properties of the tables and the query, that the transformation does not
merit being applied to a particular query. In this case the best regular plan will be used.

Star Transformation Restrictions
Star transformation is not supported for tables with any of the following characteristics:

Queries with a table hint that is incompatible with a bitmap access path

Queries that contain bind variables

Tables with too few bitmap indexes. There must be a bitmap index on a fact table column for the optimizer to
generate a subquery for it.

Remote fact tables. However, remote dimension tables are allowed in the subqueries that are generated.

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Anti-joined tables

Tables that are already used as a dimension table in a subquery

Tables that are really unmerged views, which are not view partitions

The star transformation may not be chosen by the optimizer for the following cases:

Tables that have a good single-table access path

Tables that are too small for the transformation to be worthwhile

In addition, temporary tables will not be used by star transformation under the following conditions:

The database is in read-only mode

The star query is part of a transaction that is in serializable mode

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