Database and Data Warehouse
October 8, 2015
LEARNING GOALS
Explain basic concepts of data management.
Describe traditional file systems and identify their problems.
Define database management systems and describe their various functions.
Explain how the relational database model works.
Explain Object‐Oriented databases.
Explain Data Warehouse, Data Mart
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Mini case: Bank Accounts’ data
What basic data/info are required for opening bank account?
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What is a database?
Collection of related files containing records on entities like people, places, events, things, etc.
Databases make data easy to access and manage.
Customers Info
Accounts Info
Employees Info
Access and Management tools
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Basic Concepts of Data Management
Table 1
Table 2
Form 1
Acc #:_______
Name:_______
Table 3
Report
Database:
Collection of data
organized in
different
containers
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Basic Concepts of Database systems
Accounts table
AccountID
Customer
Type
Balance
660001
John Smith
Checking
$120.00
660002
Linda Martin
Saving
$9450.00
660003
Paul Graham
Checking
$3400.00
Each table has:
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Fields
Records
1 Primary key
 Table
– Two‐dimensional structure composed of rows and columns
 Field
– Like a column in a spreadsheet
 Field name
– Like a column name in a spreadsheet
– Examples: AccountID, Customer, Type, Balance
 Field values
– Actual data for the field
 Record
– Set of fields that describe an entity (a person, an account, etc.)
 Primary key – A field, or group of fields, that uniquely identifies a record
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Basic Concepts in Data Management
 A Primary key could be a single field like in these tables
Primary key
AccountID
Customer
Type
Balance
660001
John Smith
Checking
$120.00
660002
Linda Martin
Saving
$9450.00
660003
Paul Graham
Checking
$3400.00
 Primary key could be a composite key, i.e. multiple fields
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Traditional File Systems
Early attempt to computerize manual filling system
System of files that store groups of records used by a particular software application
Simple but with a cost
– Inability to share data
– Inadequate security
– Difficulties in maintenance and expansion
– Allows data duplication (e.g. redundancy)
Application 1
Application 2
Program 1
Program 2
Program 1
Program 2
File 1
File 1
File 1
File 1
File 2
File 2
File 2
File 2
File 3
File 3
File 3
File 3
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Traditional File System Anomalies
Insertion anomaly
– Data needs to be entered more than once if located in multiple file systems
Modification anomaly
– Redundant data in separate file systems
– Inconsistent data in your system
Deletion anomaly
– Failure to simultaneously delete all copies of redundant data
– Deletion of critical data
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Database Advantages
Database advantages from a business perspective include:
– Ease of data insertion
• Example: can insert a new address once; and the address is updated in all forms, reports, etc.
– Increased flexibility
• Handling changes quickly and easily
– Increased scalability and performance
• Scalability: how the DB can adapt to increased demand
– Reduced information redundancy & inconsistency
– Increased information integrity (quality)
• Can’t delete a record if related info is used in other container
– Increased information security
Common Database software
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Desktop
Types of DBMSs
Server / Enterprise
Desktop
Handheld
– Designed to run on desktop computers
– Used by individuals or small businesses
– Requires little or no formal training
– Does not have all the capabilities of larger DBMSs
– Examples: Microsoft Access, FileMaker
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Types of DBMSs (Cont.)
Server / Enterprise
– Designed for managing larger and complex databases by large organizations
– Typically operate in a client/server setup
– Either centralized or distributed
• Centralized – all data on one server
– Easy to maintain
– Prone to run slowly when many simultaneous users
– No access if the one server goes down
• Distributed – each location has part of the database
– Very complex database administration
– Usually faster than centralized
– If one server crashes, others can still continue to operate.
– Examples: Oracle Enterprise, DB2, Microsoft SQL Server
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Types of DBMSs (Cont.)
Handheld
– Designed to run on handheld devices
– Less complex and have less capabilities than Desktop or Server DBMSs
– Example: Oracle Database Lite, IBM’s DB2 Everywhere. 14
Database Management System (DBMS)
Combination of software and data for
– Collecting, storing and managing data in a database environment.
A DBMS includes:
– Database
– Database engine (for accessing and modifying the DB content)
– Data Manipulation Language
Application 1
Program-1
Program-2
Application 2
Program-1
DBMS
Program-2
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Database Management System (DBMS)
Software through which users and application programs interact with a database
DBMS Functions
Create database structure (tables, relationships, schema, etc.)
Transform data into information (reports, ..)
Provide user with different logical views of actual database content
Provide security: password authentication, access control
– DBMSs control who can add, view, change, or delete data in the database
Physical view
ID Name Amt
01 John 23.00
02 Linda 3.00
03 Paul 53.00
Logical views
ID
02
Name
Paul
Name
Linda
ID Name Amt
01 John 23.00
02 Linda 3.00
Amt
53.00
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DBMS Functions (cont.)
Allowing multi‐user access with control
– Control concurrency of access to data
– Prevent one user from accessing data that has not been completely updated
• When selling tickets online, Ticketmaster allows you to hold a ticket for only 2 minutes to make your purchase decision, then the ticket is released to sell to someone else – that is concurrency control
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Database Models
Database model = a representation of the relationship between structures (e.g. tables) in a database
Common database models
– Flat file model
– Relational model (the most common, today)
– Object‐oriented database model
– Hierarchical model
– Network model
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Flat File Database model
Stores data in basic table structures
No relationship between tables
 Used on PDAs for address book
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Relational Database Model
 Multiple two‐dimensional tables related by common fields
 Uses controlled redundancy to create fields that provide linkage relationships between tables in the database
– These fields are called foreign keys – the secret to a relational database
– A foreign key is a field, or group of fields, in one table that is the primary key of another table
 Handles One‐to‐Many and One‐to‐One
relationships
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Object‐Oriented Database model
Needed for multimedia applications that manage images, voice, videos, graphics, etc.
Used in conjunction with Object‐oriented programming languages
Slower compared to relational DBMS for processing large volume of transactions
Hybrid object‐relational Databases are emerging
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Hierarchical Database Model
 Data is organized into a tree‐like structure using parent‐
child relationships.
 Created in the 1960s by IBM
 Limited to storing data in One‐to‐Many relationships
– One parent segment to many child segments
 Not very flexible
 Examples: IBM’s Information Management System (IMS) and Windows registry.
Network Database model
Developed in 1969
Many‐to‐Many relationships between entities
Any record may be linked to any other record
Highly flexible but also highly complex
Hard to maintain
Rarely used today
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Data Warehouse
a logical collection of information gathered from many different data sources
Stores current and historical data supports business analysis activities and decision‐
making tasks
The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision‐making purposes
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Data Warehouse Fundamentals
Many organizations need internal, external, current, and historical data
Data Warehouse are designed to, typically, store and manage data from operational transaction systems, Web site transactions, external sources, etc.
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Data Warehouse: A Multi‐Tiered Architecture
Other
sources
Operational
DBs
Metadata
Extract
Transform
Load
Refresh
Monitor
&
Integrator
Data
Warehouse
OLAP Server
Serve
Analysis
Query
Reports
Data mining
Data Marts
Data Sources
Data Storage
OLAP Engine Front-End Tools
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Multidimensional Analysis Data mining – the process of analyzing data to extract information not offered by the raw data alone
Data‐mining tools use a variety of techniques (fuzzy‐
logic, neural networks, intelligent agents) in order to
 find patterns and relationships in large volumes of data
and infer rules that predict future behavior and guide decision making
Other analytical tools: query tools, statistical tools, etc. used to
Analyze data, determine relationships, and test hypotheses about the data
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Data Warehouse Fundamentals
 Extraction, transformation, and loading (ETL) – a process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.
Information Cleansing or Scrubbing Organizations must maintain high‐quality data in the data warehouse
Information cleansing or scrubbing
– a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
– first, occurs during ETL. Then, when the data is in the Data Warehouse using Information cleansing or scrubbing tools.
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Data Mart
Subset of data warehouses that is highly focused and isolated for a specific population of users
Example: Marketing data mart, Sales data mart, etc.
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Database vs. Data Warehouse
Databases contain information in a series of two‐dimensional tables
In a Data Warehouse and data mart, information is multidimensional, it contains layers of columns and rows
Total annual sales
of TV in U.S.A.
Date
2Qtr 3Qtr
4Qtr
sum
U.S.A
Canada
Mexico
Country
TV
PC
VCR
sum
1Qtr
sum
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Summary Questions
Notes
1) What is a database, a table, a field, a record, a primary key, a composite key?
2) What are the problems with traditional file systems?
3) What are the major functions of a DBMS?
4) (a) Name some Desktop DBMSs. (b) Name some Enterprise DBMSs. (c) Handheld DBMSs
5) Describe hierarchical database model, network model
6)
What are the differences between Flat File, Relational, and Object‐
oriented database models?
7)
What is Data warehouse? Data Mart?
8)
What is Extraction, transformation, and loading (ETL)? What is data‐
mining? What is Information cleansing or scrubbing?
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