Joe Celko's Data and Databases
Concepts in Practice
- 1st Edition - July 27, 1999
- Latest edition
- Author: Joe Celko
- Language: English
Do you need an introductory book on data and databases? If the book is by Joe Celko, the answer is yes. Data and Databases: Concepts in Practice is the first introduct… Read more
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Description
Description
Do you need an introductory book on data and databases? If the book is by Joe Celko, the answer is yes. Data and Databases: Concepts in Practice is the first introduction to relational database technology written especially for practicing IT professionals. If you work mostly outside the database world, this book will ground you in the concepts and overall framework you must master if your data-intensive projects are to be successful. If you're already an experienced database programmer, administrator, analyst, or user, it will let you take a step back from your work and examine the founding principles on which you rely every day-helping you to work smarter, faster, and problem-free.
Whatever your field or level of expertise, Data and Databases offers you the depth and breadth of vision for which Celko is famous. No one knows the topic as well as he, and no one conveys this knowledge as clearly, as effectively-or as engagingly. Filled with absorbing war stories and no-holds-barred commentary, this is a book you'll pick up again and again, both for the information it holds and for the distinctive style that marks it as genuine Celko.
Key features
Key features
* Supports its extensive conceptual information with example code and other practical illustrations.
* Explains fundamental issues such as the nature of data and data modeling, and moves to more specific technical questions such as scales, measurements, and encoding.
* Offers fresh, engaging approaches to basic and not-so-basic issues of database programming, including data entities, relationships and values, data structures, set operations, numeric data, character string data, logical data and operations, and missing data among others.
* Covers the conceptual foundations of modern RDBMS technology, making it an ideal choice for students.
* Explains fundamental issues such as the nature of data and data modeling, and moves to more specific technical questions such as scales, measurements, and encoding.
* Offers fresh, engaging approaches to basic and not-so-basic issues of database programming, including data entities, relationships and values, data structures, set operations, numeric data, character string data, logical data and operations, and missing data among others.
* Covers the conceptual foundations of modern RDBMS technology, making it an ideal choice for students.
Readership
Readership
Programmers and other developers working on database applications development.
Table of contents
Table of contents
01.0 Preface
01.1 Organization of the Book
01.2 Corrections and Future Editions
02.0 The Nature of Data
02.1 Data versus Information
02.2 Information versus Wisdom
02.2.1 Innumeracy
02.2.2 Bad Math
02.2.3 Causation and Correlation
02.2.4 Testing the Model against Reality
02.3 Models versus Reality
02.3.1 Errors in Models
02.3.2 Assumptions about Reality
03.0 Entities, Attributes, Values and Relationships
03.1 Entities
03.1.1 Entities as Objects
03.1.2 Entities as Relationships
03.2 Attributes
03.3 Values
03.4 Relationships
03.5 ER Modeling
03.5.1 Mixed Models
03.6 FORM Modeling
04.0 Data Structures
04.1 Sets
04.2 Multi-sets
04.3 Simple Sequential Files
04.4 Lists
04.5 Arrays
04.6 Graphs
04.7 Trees
05.0 Relational Tables
05.1 Subsets
05.2 Union
05.3 Intersection
05.4 Set difference
05.5 Partitioning
05.5.1 Groups
05.5.2 Relational Division
05.6 Duplicates
05.6.1 Allow Duplicates
05.6.2 Disallow Duplicates
05.6.3 Consolidate Duplicates
05.6.4 Uniqueness
05.6.5 Levels of Aggregation
05.7 VIEWs
05.7.1 Updatable VIEWs
06.0 Access Structures
06.1 Indexes
06.1.1 Simple Indexes
06.2 Tree Structured Indexes
06.3 Covering Indexes
06.4 Hashing Functions
06.4.1 Uniform Hashing Function
06.4.2 Perfect Hashing Function
06.4.3 Minimal Hashing Functions
06.4.4 Multi-column Hashing Functions
06.5 Inverted Files
06.6 Bit Vector Indexes
06.7 Mixed Access Methods
06.8 Multiple Table Access Structures
06.9 An Informal Survey of Database Products
07.0 Numeric Data
07.1 Tag Numbers or Absolute Scales
07.2 Cardinal Numbers
07.3 Ordinal Numbers
07.3.1 Arithmetic with Ordinals, Cardinals and Tags
07.4.0 Computer Representations
07.4.1 Exact Numeric Representations
07.4.2 Approximate Numeric Representations versus the Continuum
07.5 Zero, NULL and Math
07.5.1 Zero, NULL and Division
07.5.2 Zero and Powers
07.6 Rounding and Truncating
07.6.1 Applied To Individual Values
07.6.2 Applied To Sets of Values
07.7 Addition and Summation are Different
07.8 Exotic Numbers
07.8.1 Fractions
07.8.2 Repeating Decimals
07.8.3 Infinite Precision Numbers
07.8.4 Complex Numbers
07.8.5 Coordinates
08.0 Character String Data
08.1 National Character Sets
08.2 Problems of String Equality
08.2.2 Empty String versus NULL String
08.3 Length
08.4 Concatenation
08.5 Position
08.6 Reversal
08.7 Pattern matching
08.8 Language as Data
08.8.1 Syntax versus Semantics
08.8.2 Computerized Language Translation
09.0 Logic and Databases
09.1 Boolean or Two Valued Logic
09.2 Multi-valued Logic
09.2.2 SQL-92 and Three Valued Logic
09.3 Fuzzy Sets, Logic and Math
09.3.1 Fuzzy Logic operators
09.3.2 Fuzzy Math Operators
09.4 Constructionist Logic
10.0 Temporal Data
10.1 Temporal Terminology
10.1.1 Events
10.1.2 Durations
10.1.3 Periods
10.1.4 Time in Databases
10.2 A Short History of the Calendar
10.3 The Julian Date
10.4 ISO Temporal Standards
10.4.1 ISO Date Formats
10.4.1.1 Calendar Date Format
10.4.1.2 Ordinal Date Format
10.4.1.3 Week Format
10.5 The Year 2000 Problem
10.5.1 The Odometer Problem
10.5.2 The Leap Year Problem
10.5.3 The Millennium Problem
10.5.4 Weird Dates
10.5.5 Solutions
10.6 ISO Time of Day
10.6.1 Local Time, lawful Time and UTC
10.7 Notes on SQL-92 and Temporal Data
10.7.1 Time Zones in SQL-92
10.7.2 the OVERLAPS Predicate
11.0 Textual Data
11.1 Terminology and the Basics
11.2. Indexing Text
11.3 Text Searching
11.4 The grep() Search Tool
11.5 ANSI and ISO Search Languages
11.5.1 ANSI Common Command Language
11.5.2 The ISO 8777 Language
12.0 Exotic Data
12.1 Nature of Data Manipulation
12.2 Physical versus Perceptual Data
12.2.1 Physical Data
12.2.2 Perceptual Data
12.3 Stored versus Constructed Data
12.4 SQL/MM
13.0 Missing data
13.1 Types of missing Data
13.1.1 Unknown
13.1.2 Not Applicable
13.1.3 Missing
13.1.4 Not Classified
13.1.5 Erroneous Data
13.1.5.1 Illegal Results
13.1.5.2 Error in Representation
13.1.5.3 Limited Values
13.2 The NULL in SQL
14.0 Scales and Measurements
14.1 Range and Granularity
14.1 Scales and Measurements
14.1.1. Range
14.1.2. Granularity and Precision
14.2 Types of Scales
14.3 Scale Conversion
14.4 Derived Units
14.5 Punctuation and Standard Units
14.6 General Guidelines for Using Scales in a Database
15.0 Data Encoding Schemes
15.1 Bad Encoding Schemes
15.1.2 Characteristics of a Bad Encoding Scheme
15.2 Encoding Scheme Types
15.2.1 Enumeration Encoding
15.2.2 Scale Encoding
15.2.3 Abbreviation Encoding
15.2.4 Algorithmic Encoding
15.2.5 Hierarchical Encoding
15.2.6 Vector Encoding
15.2.6.1 Social Security Numbers
15.2.7 Concatenation Encoding
15.3 General Guidelines for Designing Encoding Schemes
15.3.1 Existing Encoding Standards
15.3.2 Allow for Expansion
15.3.3 Use Explicit Missing Values To Avoid Nulls
15.3.4 Translate Codes for the User
15.3.5 Keep the Codes in the Database
16. Check Digits
16.1 Error Detection versus Error Correction
16.2 Mod Functions and Casting Functions
16.2.1 Mod Function
16.2.2 Casting Functions
16.3 Classes of Algorithms
16.3.1 Weighted-Sum Algorithms
16.3.2 Power-Sum Check Digits
16.3.3 Bull Check Digits
16.3.4 Dihedral Five Check Digit
16.4 Check Digit Algorithms
16.4.1 Casting Nines
16.4.2 Dihedral Five Check Digits
16.4.3 Bull Function
16.4.4 Power Function
16.4.5 SKU Code Algorithm
16.4.5 ISBN Function
16.4.6 Code 39 Algorithm
17.0 The Basic Relational Model
17.1 Tables versus Files
17.1.1 Tables as Entities
17.1.2 Tables as Relationships
17.2 Rows versus Records
17.3 Columns versus Fields
17.4 Relationships among Tables within a database
17.5 Codd's Rules for a Relational Database
18.0 Keys
18.1 Types of keys
18.2 Intelligent Keys versus Surrogate Keys.
18.2.1 Arguments against Intelligent Keys
18.2.1.1 Autoincremented Surrogate Keys
18.2.1.2 (MAX() + 1) Surrogate key generators
18.2.1.2 Random Number Surrogate keys
18.2.1.3 Surrogate Key Tables
18.2.2 Arguments for Intelligent Keys
18.2.2.1 Saving Space in the Database
18.2.2.2 Verification of the Data
18.2.2.3 Changing Surrogate Keys is Hard
18.2.2.4 Getting New Keys
18.2.2.5 Clustering Versus Random Distribution
18.2.2.6 Standards Versus "Roll Your Own"
18.2.2.7 Data Warehousing
18.2.2.8 Replicated and Distributed Databases
18.3 Simple versus Compound Keys
18.3.1 Super keys and Overlapping Keys
18.3.2 Queries with Compound keys
19.0 Different Relational Models
19.1 Chris Date = no duplicates, no NULLs
19.2 E.F Codd, RM version I
19.3 E.F Codd, RM version II
19.4 SQL-92 = duplicates, one NULL
19.5 Rick Snodgrass = Temporal SQL
19.5 Roth & Korth = duplicates, one NULL, non-1NF Tables
20.0 Basic Relational Operations
20.1 Projection
20.2 Restriction
20.3 Computations
20.3.1 Expressions Using Other Tables
20.3.1 Simple Operators and Expressions
20.4 Joins
20.4.1 Inner Join
20.4.2 Outer Join
20.4.2.1 Left Outer Join
20.4.2.2 Right Outer Join
20.4.2.3 Full Outer Join
20.4.3 Self Join
20.4.4 Union Join
21.0 Transactions and Concurrency Control
21.1 Sessions
21.2 Transactions and ACID
21.3 Concurrency
21.3.1 The Three Phenomena
21.3.2 The Four Isolation Levels
21.4 Pessimistic Concurrency Control
21.5 Optimistic Concurrency Control
21.6 Logical Concurrency Control
21.7 Deadlock and Livelocks
22.0 Functional Dependencies
22.1 Armstrong's Axioms
22.2 Multi-valued Dependencies
22.3 Mappings
23.0 Normalization
23.1 Anomalies
23.2 First Normal Form (1NF)
23.2.1 Note on Repeated Groups
23.3 Second Normal Form (2NF)
23.4 Third Normal Form (3NF)
23.5 Boyce-Codd Normal Form (BCNF)
23.6 Fourth Normal Form (4NF)
23.7 Fifth Normal Form (5NF)
23.8 Domain-Key Normal Form (DKNF)
23.9 Functional Dependency Axioms
23.10 Practical Hints for Normalization
23.10.1 Case Tools for Normalization
24.0 Denormalization
24.1. Criteria for Denormalization
24.2 Types of Denormalization
24.2.1 Pre-joined Tables
24.2.2 Reports
24.2.3 Mirrored Tables
24.2.4 Table Splitting
24.2.5 Combined Tables
24.2.6 Redundant Data
24.2.7 Repeating Groups
24.2.8 Derivable Data
24.2.9 Hierarchy Tables
24.2.10. Overloaded Datatypes
25.0 Metadata
25.1 Data
25.1.1 Data Assets
25.1.2 Data Engineering Assets
25.1.3 Core Data
25.1.3 Data Management Assets
25.2 Metadata Management
25.2.1 Database Management Systems
25.2.2 Data Dictionary
25.2.3 Data Directory
25.2.4 Data Encyclopedia
25.2.5 Data Element Registry
25.2.6 Repository
25.3 Data Dictionary Environments
25.3.1 Application Data Dictionary
25.3.2 Functional Area Data Dictionaries
25.3.3 The ITS Data Dictionary/Registry
25.4 NCITS L8 Standards
25.4.1 Naming Data Elements
25.4.1.1 Levels of Abstraction
25.4.2 Registering Standards
01.1 Organization of the Book
01.2 Corrections and Future Editions
02.0 The Nature of Data
02.1 Data versus Information
02.2 Information versus Wisdom
02.2.1 Innumeracy
02.2.2 Bad Math
02.2.3 Causation and Correlation
02.2.4 Testing the Model against Reality
02.3 Models versus Reality
02.3.1 Errors in Models
02.3.2 Assumptions about Reality
03.0 Entities, Attributes, Values and Relationships
03.1 Entities
03.1.1 Entities as Objects
03.1.2 Entities as Relationships
03.2 Attributes
03.3 Values
03.4 Relationships
03.5 ER Modeling
03.5.1 Mixed Models
03.6 FORM Modeling
04.0 Data Structures
04.1 Sets
04.2 Multi-sets
04.3 Simple Sequential Files
04.4 Lists
04.5 Arrays
04.6 Graphs
04.7 Trees
05.0 Relational Tables
05.1 Subsets
05.2 Union
05.3 Intersection
05.4 Set difference
05.5 Partitioning
05.5.1 Groups
05.5.2 Relational Division
05.6 Duplicates
05.6.1 Allow Duplicates
05.6.2 Disallow Duplicates
05.6.3 Consolidate Duplicates
05.6.4 Uniqueness
05.6.5 Levels of Aggregation
05.7 VIEWs
05.7.1 Updatable VIEWs
06.0 Access Structures
06.1 Indexes
06.1.1 Simple Indexes
06.2 Tree Structured Indexes
06.3 Covering Indexes
06.4 Hashing Functions
06.4.1 Uniform Hashing Function
06.4.2 Perfect Hashing Function
06.4.3 Minimal Hashing Functions
06.4.4 Multi-column Hashing Functions
06.5 Inverted Files
06.6 Bit Vector Indexes
06.7 Mixed Access Methods
06.8 Multiple Table Access Structures
06.9 An Informal Survey of Database Products
07.0 Numeric Data
07.1 Tag Numbers or Absolute Scales
07.2 Cardinal Numbers
07.3 Ordinal Numbers
07.3.1 Arithmetic with Ordinals, Cardinals and Tags
07.4.0 Computer Representations
07.4.1 Exact Numeric Representations
07.4.2 Approximate Numeric Representations versus the Continuum
07.5 Zero, NULL and Math
07.5.1 Zero, NULL and Division
07.5.2 Zero and Powers
07.6 Rounding and Truncating
07.6.1 Applied To Individual Values
07.6.2 Applied To Sets of Values
07.7 Addition and Summation are Different
07.8 Exotic Numbers
07.8.1 Fractions
07.8.2 Repeating Decimals
07.8.3 Infinite Precision Numbers
07.8.4 Complex Numbers
07.8.5 Coordinates
08.0 Character String Data
08.1 National Character Sets
08.2 Problems of String Equality
08.2.2 Empty String versus NULL String
08.3 Length
08.4 Concatenation
08.5 Position
08.6 Reversal
08.7 Pattern matching
08.8 Language as Data
08.8.1 Syntax versus Semantics
08.8.2 Computerized Language Translation
09.0 Logic and Databases
09.1 Boolean or Two Valued Logic
09.2 Multi-valued Logic
09.2.2 SQL-92 and Three Valued Logic
09.3 Fuzzy Sets, Logic and Math
09.3.1 Fuzzy Logic operators
09.3.2 Fuzzy Math Operators
09.4 Constructionist Logic
10.0 Temporal Data
10.1 Temporal Terminology
10.1.1 Events
10.1.2 Durations
10.1.3 Periods
10.1.4 Time in Databases
10.2 A Short History of the Calendar
10.3 The Julian Date
10.4 ISO Temporal Standards
10.4.1 ISO Date Formats
10.4.1.1 Calendar Date Format
10.4.1.2 Ordinal Date Format
10.4.1.3 Week Format
10.5 The Year 2000 Problem
10.5.1 The Odometer Problem
10.5.2 The Leap Year Problem
10.5.3 The Millennium Problem
10.5.4 Weird Dates
10.5.5 Solutions
10.6 ISO Time of Day
10.6.1 Local Time, lawful Time and UTC
10.7 Notes on SQL-92 and Temporal Data
10.7.1 Time Zones in SQL-92
10.7.2 the OVERLAPS Predicate
11.0 Textual Data
11.1 Terminology and the Basics
11.2. Indexing Text
11.3 Text Searching
11.4 The grep() Search Tool
11.5 ANSI and ISO Search Languages
11.5.1 ANSI Common Command Language
11.5.2 The ISO 8777 Language
12.0 Exotic Data
12.1 Nature of Data Manipulation
12.2 Physical versus Perceptual Data
12.2.1 Physical Data
12.2.2 Perceptual Data
12.3 Stored versus Constructed Data
12.4 SQL/MM
13.0 Missing data
13.1 Types of missing Data
13.1.1 Unknown
13.1.2 Not Applicable
13.1.3 Missing
13.1.4 Not Classified
13.1.5 Erroneous Data
13.1.5.1 Illegal Results
13.1.5.2 Error in Representation
13.1.5.3 Limited Values
13.2 The NULL in SQL
14.0 Scales and Measurements
14.1 Range and Granularity
14.1 Scales and Measurements
14.1.1. Range
14.1.2. Granularity and Precision
14.2 Types of Scales
14.3 Scale Conversion
14.4 Derived Units
14.5 Punctuation and Standard Units
14.6 General Guidelines for Using Scales in a Database
15.0 Data Encoding Schemes
15.1 Bad Encoding Schemes
15.1.2 Characteristics of a Bad Encoding Scheme
15.2 Encoding Scheme Types
15.2.1 Enumeration Encoding
15.2.2 Scale Encoding
15.2.3 Abbreviation Encoding
15.2.4 Algorithmic Encoding
15.2.5 Hierarchical Encoding
15.2.6 Vector Encoding
15.2.6.1 Social Security Numbers
15.2.7 Concatenation Encoding
15.3 General Guidelines for Designing Encoding Schemes
15.3.1 Existing Encoding Standards
15.3.2 Allow for Expansion
15.3.3 Use Explicit Missing Values To Avoid Nulls
15.3.4 Translate Codes for the User
15.3.5 Keep the Codes in the Database
16. Check Digits
16.1 Error Detection versus Error Correction
16.2 Mod Functions and Casting Functions
16.2.1 Mod Function
16.2.2 Casting Functions
16.3 Classes of Algorithms
16.3.1 Weighted-Sum Algorithms
16.3.2 Power-Sum Check Digits
16.3.3 Bull Check Digits
16.3.4 Dihedral Five Check Digit
16.4 Check Digit Algorithms
16.4.1 Casting Nines
16.4.2 Dihedral Five Check Digits
16.4.3 Bull Function
16.4.4 Power Function
16.4.5 SKU Code Algorithm
16.4.5 ISBN Function
16.4.6 Code 39 Algorithm
17.0 The Basic Relational Model
17.1 Tables versus Files
17.1.1 Tables as Entities
17.1.2 Tables as Relationships
17.2 Rows versus Records
17.3 Columns versus Fields
17.4 Relationships among Tables within a database
17.5 Codd's Rules for a Relational Database
18.0 Keys
18.1 Types of keys
18.2 Intelligent Keys versus Surrogate Keys.
18.2.1 Arguments against Intelligent Keys
18.2.1.1 Autoincremented Surrogate Keys
18.2.1.2 (MAX() + 1) Surrogate key generators
18.2.1.2 Random Number Surrogate keys
18.2.1.3 Surrogate Key Tables
18.2.2 Arguments for Intelligent Keys
18.2.2.1 Saving Space in the Database
18.2.2.2 Verification of the Data
18.2.2.3 Changing Surrogate Keys is Hard
18.2.2.4 Getting New Keys
18.2.2.5 Clustering Versus Random Distribution
18.2.2.6 Standards Versus "Roll Your Own"
18.2.2.7 Data Warehousing
18.2.2.8 Replicated and Distributed Databases
18.3 Simple versus Compound Keys
18.3.1 Super keys and Overlapping Keys
18.3.2 Queries with Compound keys
19.0 Different Relational Models
19.1 Chris Date = no duplicates, no NULLs
19.2 E.F Codd, RM version I
19.3 E.F Codd, RM version II
19.4 SQL-92 = duplicates, one NULL
19.5 Rick Snodgrass = Temporal SQL
19.5 Roth & Korth = duplicates, one NULL, non-1NF Tables
20.0 Basic Relational Operations
20.1 Projection
20.2 Restriction
20.3 Computations
20.3.1 Expressions Using Other Tables
20.3.1 Simple Operators and Expressions
20.4 Joins
20.4.1 Inner Join
20.4.2 Outer Join
20.4.2.1 Left Outer Join
20.4.2.2 Right Outer Join
20.4.2.3 Full Outer Join
20.4.3 Self Join
20.4.4 Union Join
21.0 Transactions and Concurrency Control
21.1 Sessions
21.2 Transactions and ACID
21.3 Concurrency
21.3.1 The Three Phenomena
21.3.2 The Four Isolation Levels
21.4 Pessimistic Concurrency Control
21.5 Optimistic Concurrency Control
21.6 Logical Concurrency Control
21.7 Deadlock and Livelocks
22.0 Functional Dependencies
22.1 Armstrong's Axioms
22.2 Multi-valued Dependencies
22.3 Mappings
23.0 Normalization
23.1 Anomalies
23.2 First Normal Form (1NF)
23.2.1 Note on Repeated Groups
23.3 Second Normal Form (2NF)
23.4 Third Normal Form (3NF)
23.5 Boyce-Codd Normal Form (BCNF)
23.6 Fourth Normal Form (4NF)
23.7 Fifth Normal Form (5NF)
23.8 Domain-Key Normal Form (DKNF)
23.9 Functional Dependency Axioms
23.10 Practical Hints for Normalization
23.10.1 Case Tools for Normalization
24.0 Denormalization
24.1. Criteria for Denormalization
24.2 Types of Denormalization
24.2.1 Pre-joined Tables
24.2.2 Reports
24.2.3 Mirrored Tables
24.2.4 Table Splitting
24.2.5 Combined Tables
24.2.6 Redundant Data
24.2.7 Repeating Groups
24.2.8 Derivable Data
24.2.9 Hierarchy Tables
24.2.10. Overloaded Datatypes
25.0 Metadata
25.1 Data
25.1.1 Data Assets
25.1.2 Data Engineering Assets
25.1.3 Core Data
25.1.3 Data Management Assets
25.2 Metadata Management
25.2.1 Database Management Systems
25.2.2 Data Dictionary
25.2.3 Data Directory
25.2.4 Data Encyclopedia
25.2.5 Data Element Registry
25.2.6 Repository
25.3 Data Dictionary Environments
25.3.1 Application Data Dictionary
25.3.2 Functional Area Data Dictionaries
25.3.3 The ITS Data Dictionary/Registry
25.4 NCITS L8 Standards
25.4.1 Naming Data Elements
25.4.1.1 Levels of Abstraction
25.4.2 Registering Standards
Product details
Product details
- Edition: 1
- Latest edition
- Published: July 27, 1999
- Language: English
About the author
About the author
JC
Joe Celko
Joe Celko served 10 years on ANSI/ISO SQL Standards Committee and contributed to the SQL-89 and SQL-92 Standards.
Mr. Celko is author a series of books on SQL and RDBMS for Elsevier/MKP. He is an independent consultant based in Austin, Texas.
He has written over 1200 columns in the computer trade and academic press, mostly dealing with data and databases.
Affiliations and expertise
Independent Consultant, Austin, Texas