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E-raamat: Logical Database Design Principles

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Garmany and fellow consultants in the information industry Jeff Walker and Terry Clark describe database design in an era when modern database management systems have added powerful features that have caused a movement away from truly normalized relational database design. They begin by setting out the traditional logical design principles, normalizing, and modeling the data. Then they discuss designing for specific purposes such as object-oriented, online transaction processing, and data warehouses. As they move away from relational design, they also move away from the purely logical into some of the physical design, because of how many of the features of modern databases are implemented. Annotation ©2005 Book News, Inc., Portland, OR (booknews.com)

A proper logical data model is the single most important aspect of database performance, and this text is a complete guide to designing databases to utilize database structures, using examples from the Oracle database. More than the standard rehash of logical database design, Logical Database Design Principles is a complete guide to the logical design of databases for optimal performance. It combines IT theory with pragmatic techniques and covers all aspects of logical database design, including translation of data flow diagrams into data models, formal data normalization and object orientation within a logical data model.
1 Introduction to Logical Database Design
1(16)
Understanding a Database
1(1)
Database Architectures
2(2)
Relational Databases
4(1)
Creating the Database
4(1)
System Development Life Cycle (SDLC)
5(1)
Systems Planning: Assessment and Feasibility
5(1)
System Analysis: Requirements
6(2)
System Analysis: Requirements Checklist
8(1)
Models Tracking and Schedules
8(1)
Design Modeling
9(1)
Functional Decomposition Diagram
10(1)
Data Flow Diagrams
11(1)
Data Dictionary
12(2)
Logical Structures and Decision Trees
14(1)
System Design: Logical
15(2)
2 System Design and Implementation
17(18)
The ER Approach
17(1)
Entities and Entity Types
18(2)
Attribute Domains
20(1)
Attributes
20(1)
Set-Valued Attributes
20(2)
Weak Entities
22(1)
Constraints
23(2)
Key Constraints: Primary
23(1)
Key Constraints: Foreign
24(1)
Lookup Tables and Consistency
25(1)
Modeling Approaches
26(1)
Relationships
26(3)
Cardinality
29(2)
Schemas
31(1)
Implementation: Converting the ERD
32(2)
Specifications and Documentation
34(1)
3 Converting the Logical Data Model
35(20)
Database Architecture
35(3)
SQL
38(1)
DDL Commands
38(7)
Data Retrieval
45(1)
DML Statements and Implicit Cursors
46(1)
SELECT Statements
47(2)
Cartesian Product
49(1)
DML: INSERT Commands
50(1)
DML: UPDATE Command
51(1)
DML: DELETE Statements
52(1)
Transaction Commands
53(1)
DCL Commands
54(1)
4 Formal Data Normalization
55(18)
Definitions of Normal Forms
57(2)
First Normal Form
57(1)
Second Normal Form
57(1)
Third Normal Form
57(1)
Boyce-Codd Normal Form
58(1)
Fourth Normal Form
58(1)
Fifth Normal Form
58(1)
Introduction to Relational Algebra
59(6)
Comparison Operations and Logical Operators
65(1)
Applying Normal Forms
66(5)
First Normal Form
68(1)
Second Normal Form
68(2)
Third Normal Form
70(1)
Boyce-Codd Normal Form
71(1)
Fourth Normal Form
71(1)
Fifth Normal Form
71(1)
Summary
71(2)
5 Data Denormalization
73(10)
Historical Perspective on Disk Costs
73(1)
The Importance of Database Performance to the Logical Model
74(1)
Introducing Data Redundancy: Size versus Volatility
75(1)
The Theory of Dr. Ralph Kimball
76(1)
Pre Joining Entities Together
76(2)
Pre-Aggregating Summary Information
78(1)
Non-First Normal Form Entities
79(1)
Horizontal and Vertical Partitioning for Performance
79(1)
Summary
80(3)
6 Object-Oriented Design
83(18)
Object-Oriented Terminology
85(6)
Classes
85(1)
Objects
86(1)
Messages
86(1)
Abstract Classes
86(1)
Encapsulation
86(1)
Inheritance
87(1)
Polymorphism
87(1)
Containment
88(1)
Extensibility
88(1)
Object Identity
88(2)
Transparent Persistence
90(1)
Storage and Access of Objects
90(1)
Object-Oriented Design
91(4)
Object-Oriented Analysis
91(1)
Different Models for Object Analysis
91(2)
The Rumbaugh Method
93(1)
The Booch Method
93(1)
The Coad-Yourdon Method
93(1)
The Shlaer-Mellor Method
94(1)
The Unified Modeling Language
94(1)
Creating the Object Model
95(3)
Methods and Database Objects
98(2)
Summary
100(1)
7 Object-Relational Design
101(20)
Advantages of Object-Relational Databases
102(1)
Atomic, Normalized Data
102(1)
Ad hoc Queries
102(1)
Object Types
103(7)
Large Objects
105(2)
Collections
107(9)
Arrays
107(1)
Nested Tables
108(2)
Object Views
110(1)
Using Logical Pointers
111(4)
Comparison of Object-Oriented and Object-Relational Databases
115(1)
Creating the Object-Relational Design
116(3)
Logical Object Pointers
117(1)
Automated Tools
118(1)
Summary
119(2)
8 Designing Replicated Databases
121(18)
Manual Replication Techniques
122(1)
Standby Database
123(1)
Replication Using Triggers
124(1)
Replication Using Views
124(1)
One-Way Replication
125(1)
Writeable Replication
125(1)
Updateable Replication
126(4)
The Push-Pull Method
126(1)
The Multi-Master Method
127(2)
The Streams Method
129(1)
What Are Conflicts?
130(3)
Conflict Avoidance
131(1)
Database Conflict Resolution
132(1)
Which Form of Replication is Right for You?
133(2)
Planning Your Replication Schema
135(2)
Primary Keys
135(1)
Foreign Keys
135(1)
Indexes
136(1)
Denormalization
136(1)
Moving Large Datasets
136(1)
Resynching Data
137(1)
Summary
137(2)
9 Design of the OLTP Database
139(16)
Designing for Online Transaction Processing
139(1)
Normalization
140(4)
Data Storage
141(1)
Database Data Cache Efficiency
142(2)
Data Growth
144(2)
SQL Reuse
146(2)
Database Locking and Waiting
148(1)
Deadlocks
149(1)
Rollbacks
149(1)
Indexing
150(3)
Primary Keys
151(1)
Table with Three or More Columns
151(1)
Multiple Indexes with the Most Restrictive Lead Columns
151(1)
Large Multi-Column Indexes
152(1)
Organizing Tables as Indexes
152(1)
Summary
153(2)
10 Design of Data Warehouses and Decision Support System Databases 155(18)
What Is a Data Warehouse?
155(3)
OLAP, MOLAP, and ROLAP
156(1)
Using an Operational Database as a Data Warehouse
157(1)
Gathering Data
158(1)
Staging and Processing Data
158(1)
Loading and Unloading Data
159(2)
Data Marts
161(1)
Designing the Data Warehouse
161(10)
Data Granularity
162(1)
Partitioning Data
162(5)
Which Attribute to Partition On
163(1)
Where to Partition
164(3)
Use of Keys
167(1)
Normalization
168(1)
Dimensional Modeling
168(1)
Pre-Aggregation
169(2)
Protect the Data
171(1)
Summary
171(2)
Index 173


John Garmany, Jeff Walker, Terry Clark
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