CHAPTER 4
WHAT IS A DATA WAREHOUSE?
A large reservoir of detailed and summary data
that describes the organization and its activities,
organized by the various business dimensions in
a way to facilitate easy retrieval of information
describing activities
data mart
–a subset of the data warehouse,
tailored to meet the specialized needs of a
particular group of users
Top-down approach
bottom-up approach to data warehouse
development—the data marts are created first
and then integrated.
Data Warehousing Objectives
(1)
keep the warehouse data current;
(2)
ensure that the warehouse data is accurate;
(3)
keep the warehouse data secure;
(4)
make the warehouse data easily available to
authorized users;
(5)
maintain descriptions of the warehouse data
so that users and system developers can
understand the meaning of each element
Data Warehouse vs. DBMS
OLTP (on-line transaction processing)
Major task of traditional relational DBMS
Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
OLAP (on-line analytical processing)
Major task of data warehouse system
Data analysis and decision making
Distinct features (OLTP vs. OLAP):
User and system orientation: customer vs. market
Data contents: current, detailed vs. historical, consolidated
Database design: ER + application vs. star + subject
View: current, local vs. evolutionary, integrated
OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date detailed, flat relational isolated
historical,
summarized, multidimensional integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
DATA WAREHOUSE ARCHITECTURE
staging area
— data is prepared to be
moved into the warehouse data repository
and the metadata repository
metadata
— data about data, or
descriptions of the data in the data
warehouse
Exhibit 4.1: A Data Warehouse System
Model
EXHIBIT 4.1 A DATA WAREHOUSE SYSTEM MODEL Data gathering system Staging area Warehouse data repository Information Delivery system Management and control Metadata repository
Data Warehouse System
Legend : Data flow Control flow
A Data Warehouse System Model
Management and Control
management and control component—like a traffic
officer standing in the middle of a street intersection,
controlling the flow of traffic through the intersection
Staging Area
ETL
— extraction, transformation, and loading as the
activities of this staging area
extraction
— obtaining data from the internal databases
and files of systems, accomplished according to a
schedule
transformation
— a process that includes cleaning,
standardizing, reformatting, and summarizing
A Data Warehouse System Model
WAREHOUSE DATA REPOSITORY
where the warehouse data is stored within the computer system or systems
Data Content
customer picture—a compilation of geographic, demographic, activity, psychographic, and behavioral data
Data Characteristics
the types of data to be processed, including considerations of data granularity, data hierarchies, and data dimensions
Data Types
fixed-length format
A Data Warehouse System Model
Data Granularity
the degree of detail that is represented by the data, where the greater the detail, the finer the granularity
Data Hierarchies
since multiple attributes can describe a single entity, an attribute is a data element that identifies or describes an occurrence of a data entity (i.e., a particular customer is identified by a customer number attribute)
Exhibit 4.2: An Example of a Data Hierarchy
Data Dimensions
for example, a manager can query the data warehouse for a display of data according to salesperson, customer, product, and time
EXHIBIT 4.2 AN EXAMPLE OF A DATA HIERARCHY
Customer
Customer number
Customer age
Customer gender
Customer marital status
Customer number of dependents
Customer education
Customer dwelling type
Customer state
Customer city
Customer zip code
EXHIBIT 4.3 EVERY DATA RECORD CONTAINS THE TIME ELEMENT
Warehouse shipping order
Sales order date Statement date Date shipped Customer Payment date Invoice date
Customer sales order
Customer payment Customer statement
A Data Warehouse System Model
METADATA REPOSITORY
describes the flow of data from the time that the data is captured until it is archived, i.e., metadata in the metadata repository for the customer number attribute would describe its format, editing rules, and so on
TYPES OF METADATA
Metadata for Users
(analysis) identification of the source systems, the time of the last update, the different report formats that are available, and how to find data in the data warehouse
Metadata for Systems Developers
data to allow developers to maintain, revise, and reengineer the data warehouse system, including the various rules that were employed in creating the warehouse data repository, and the rules for extraction, cleansing, transforming, purging, and archiving
A Data Warehouse System Model
Data and Process Models
object diagrams and entity-relationship diagrams
use cases, use case diagrams, and data flow
diagrams
CASE Tools
stands for computer-aided system engineering and is
a way to use the computer to develop systems
DBMS Systems
include a data dictionary component, which contains
excellent descriptions of the data in the database or
data warehouse.
HOW DATA IS STORED IN THE DATA
WAREHOUSE
dimension table
— a list of all of the
attributes that identify and describe a
particular entity
Exhibit 4.4: A Sample Dimension Table
fact table
— a list of all the facts that relate
to some type of the organization’s activity
EXHIBIT 4.4 A SAMPLE DIMENSION TABLE
Customer
Customer number
Customer name
Customer phone number
Customer e-mail address
Customer territory
Customer credit code
Customer standard industry code
Customer city
Customer state
EXHIBIT 4.5 A SAMPLE FACT TABLE
Commercial Sales Facts
Actual sales units
Budgeted sales units
Actual sales amount
Budgeted sales amount
Sales discount amount
Net sales amount
Sales commission amount
Sales bonus amount
INFORMATION PACKAGES
a table that is maintained in the data warehouse
repository that identifies both the dimensions
and the facts that relate to a business activity
Exhibit 4.6: Information Package Format
key—a number, such as a customer number,
that identifies a particular occurrence of the
dimension
EXHIBIT 4.6 INFORMATION PACKAGE FORMAT
Subject : Name of business activity being measured
Dimension Name Dimension Name Dimension Name Dimension Name
Dimension Key Dimension Key Dimension Key Dimension Key
Dimension 1 Dimension 1 Dimension 1 Dimension 1
Dimension 2 Dimension 2 Dimension 2 Dimension 2
Dimension 3 Dimension 3 Dimension n Dimension 3
Dimension 4 Dimension n Dimension 4
Dimension n Dimension n
EXHIBIT 4.7 A SAMPLE INFORMATION PACKAGE
Subject : Commercial sales
Time Salesperson Customer Product
Time Key Salesperson key Customer key Product key Hour Salesperson name Customer name Product name Day Sales branch Customer territory Product model Month Sales region Customer credit code Product brand Quarter Subsidiary Product line
Year
Facts : Actual sales units, budgeted sales units, actual sales amount, budgeted sales amount, sales discount amount, net sales amount, sales commission amount, sales bonus amount, sales tax amount
STAR SCHEMAS
the arrangement of an information package that usually
identifies multiple dimension tables for a single fact table
and has the appearance of a star, with the fact table in
the center and the dimension tables forming the points
Exhibit 4.8: Star Schema Format
foreign keys— a means of linking the fact table to the
dimension tables by means of the keys identified at the
top of the fact table where the keys identify other,
―foreign‖ tables as opposed to the fact table
EXHIBIT 4.8 STAR SCHEMA FORMAT Dimension 1 name Dimension 2 name Dimension n name Dimension 1 key Dimension 1 hierarchy Dimension 2 key Dimension 2 hierarchy Dimension 1 key Dimension 2 key Dimension n key Measurable fact 2 Measurable fact 4 Measurable fact 5 Measurable fact n Dimension n key Dimension n hierarchy
EXHIBIT 4.9 A SAMPLE STAR SCHEMA
Customer key Customer name Customer type
Customer credit code Salesperson number Sales territory
Standard industry code
Product key Customer key Salesperson key Time key
Sales units
Gross sales amount Sales discount amount Net sales amount
Sales commission amount Salesperson key Salesperson name Sales region Sales branch Product key Product name Product unit price Product quantity Time key Day Month Quarter Year Customer Customer payment Product sales facts
Time
Example of Star Schema
time_key day day_of_the_week month quarter year time location_key street city province_or_street country location Sales Fact Table
time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type
item
branch_key branch_name branch_typebranch
Example of Snowflake Schema
time_key day day_of_the_week month quarter year time location_key street city_key location Sales Fact Tabletime_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_key
item
branch_key branch_name branch_typebranch
supplier_key supplier_typesupplier
city_key city province_or_street country cityExample of Fact
Constellation
time_key day day_of_the_week month quarter year time location_key street city province_or_street country location Sales Fact Tabletime_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped shipper_key shipper_name location_key shipper_type shipper
DATA WAREHOUSE NAVIGATION
summary information— preprocessed data that provides
the user with exactly the content that is needed
top-down
navigation— the user seeks more detail in an
effort to understand the summary information
roll up
navigation— the user summarizes data to ―see
the forest rather than the trees‖ or to prepare summary
graphs
drill across
navigation— the user moves from one data
hierarchy to another, i.e., information on customer sales,
salesperson sales, and then product sales
EXHIBIT 4.10 NAVIGATION PATHS
Summary information
(Net sales for the Western sales region)
Hierarchy 1 (customer) Hierarchy 2 (salesperson) Hierarchy n (product) Detailed information
(Net sales for salesperson 3742)
Detailed data
(Sales units for salesperson 3742)
Roll up
Drill across
Drill down
DATA WAREHOUSE SECURITY
information systems security— damage, destruction, theft, andmisuse
Exhibit 4.11: The Security Action Cycle
The Corporate Security Environment
deterrence— security policies and procedures that are intended to
deter security violations, such as guidelines for proper system use and the requirement that users change their passwords periodically
prevention— measures aimed at those persons who ignore
deterrence, and include such things as locks on computer rooms, user passwords, file permissions
detection— proactive actions include system audits, reports of
suspicious activity, and virus scanning software and reactive actions take the form of investigations
remedies— respond with warnings, reprimands, termination of
EXHIBIT 4.11 THE SECURITY ACTION CYCLE 1. Deterrence 2. Prevention 3. Detection 4. Remedies Maximize Deterred abuse Maximize Prevented abuse Maximize Undetected abuse Maximize Unpunished abuse Deterred abuse Prevented abuse Undetected abuse Unpunished abuse Deterrence feedback
