SlideShare a Scribd company logo
1 of 10
Download to read offline
Database Normalization
●   Overview
    –   Definition of database normalization

    –   Why normalize?

    –   First normal form

    –   Second normal form

    –   Third normal form
        Information for this presentation borrowed from
         http://www.devshed.com/c/a/MySQL/An-Introduction-to-Database-Normalization/
Database Normalization
●   Definition
    –   Optimizing table structures

    –   Removing duplicate data entries

    –   Accomplished by thouroughly investigating the
        various data types and their relationships with
        one another

    –   Follows a series of normalization “forms” or
        states
Database Normalization
●   Why Normalize?
    –   Improved speed

    –   More efficient use of space

    –   Increased data integrity
         ●   (decreased chance that data can get messed up due
             to maintenance)
Database Normalization
●   A sad, sad database:
    –   Refer to the following poor database design:
         student_id   class_name      time      location   professor_id
        999-40-9876    Math 148     MWF 11:30   Rm. 432      prof145
        999-43-0987   Physics 113    TR 1:30    Rm. 12       prof143
        999-42-9842    Botany 42     F 12:45     Rm. 9       prof167
        999-41-9832    Matj 148     MWF 11:30   Rm. 432      prof145


    –   Problems
         ●   no need to repeatedly store the class time and
             professor ID
         ●   redundancy introduces the possibility for error (Matj
             148)
Database Normalization
●   First Normal Form
    –   calls for the elimination of repeated groups of
        data by creating separate tables of related data
    –   Student information:
           StudentID   StudentName   Major   college   collegeLocation


    –   Class information:
           StudentID   ClassID       ClassName


    –   Professor Information:
           ProfessorID ProfessorName
Database Normalization
●   Second Normal Form
    –   Elimination of redundant data
         ●   Example data in Class Information:
              studentID           classID   className
              134-56-7890         M148      Math 148
              123-45-7894         P113      Physics 113
              534-98-9009         H151      History 151
              134-56-7890         H151      History 151


             Use:                                      To get Class Information:
               ClassID      ClassName                        studentID     classID
               M148         Math 148                         134-56-7890   M148
               P113         Physics 113                      123-45-7894   P113
               H151         History 151                      534-98-9009   H151
                                                             134-56-7890   H151
Database Normalization
●   Third Normal Form
    –   eliminate all attributes(column headers) from a
        table that are not directly dependent upon the
        primary key
         ●   college and collegeLocation attributes are less
             dependent upon the studentID than they are on
             the major attribute
         ●   New college table:
                major   college   collegeLocation

         ●   Revised student table:
                studentID   studentName   Major
Database Normalization
●   Old Design:
            student_id     class_name         time             location      professor_id
          999-40-9876        Math 148       MWF 11:30          Rm. 432            prof145
          999-43-0987       Physics 113      TR 1:30           Rm. 12             prof143
          999-42-9842        Botany 42       F 12:45            Rm. 9             prof167
          999-41-9832        Matj 148       MWF 11:30          Rm. 432            prof145




    New Design:
    Student                                   Enrollment                  Class
    studentID      studentName      Major     studentID     classID       ClassID       ClassName   ProfessorID




    College                                   Professor
    major       college   collegeLocation     ProfessorID      ProfessorName
Database Normalization
●   Questions?
Normalization Assignment
●   For your tool:
    –   Compile list of all data items used

    –   Place all data into one table

    –   Complete 1NF and describe reasons why this is
        better (or if data is already in 1NF continue)

    –   Complete 2NF in the same manner

    –   Complete 3NF in the same manner

More Related Content

What's hot

PostGreSQL Performance Tuning
PostGreSQL Performance TuningPostGreSQL Performance Tuning
PostGreSQL Performance TuningMaven Logix
 
Bsc cs ii-dbms-u-iv-normalization
Bsc cs ii-dbms-u-iv-normalizationBsc cs ii-dbms-u-iv-normalization
Bsc cs ii-dbms-u-iv-normalizationRai University
 
Oracle architecture ppt
Oracle architecture pptOracle architecture ppt
Oracle architecture pptDeepak Shetty
 
Database design process
Database design processDatabase design process
Database design processTayyab Hameed
 
12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMSkoolkampus
 
Best Practices for Becoming an Exceptional Postgres DBA
Best Practices for Becoming an Exceptional Postgres DBA Best Practices for Becoming an Exceptional Postgres DBA
Best Practices for Becoming an Exceptional Postgres DBA EDB
 
The Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian BanduletThe Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian BanduletChristian Bandulet
 
Library management system
Library management systemLibrary management system
Library management systemashu6
 
Database fundamentals(database)
Database fundamentals(database)Database fundamentals(database)
Database fundamentals(database)welcometofacebook
 
Library Management System Waterfall Model
Library Management System Waterfall ModelLibrary Management System Waterfall Model
Library Management System Waterfall Modelmitwa1990
 
A Day In The Life Of A DBA Manager
A Day In The Life Of A DBA ManagerA Day In The Life Of A DBA Manager
A Day In The Life Of A DBA ManagerMahesh Vallampati
 
k Nearest Neighbor
k Nearest Neighbork Nearest Neighbor
k Nearest Neighborbutest
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithmparry prabhu
 
Difference between molap, rolap and holap in ssas
Difference between molap, rolap and holap  in ssasDifference between molap, rolap and holap  in ssas
Difference between molap, rolap and holap in ssasUmar Ali
 
Lecture 04 normalization
Lecture 04 normalization Lecture 04 normalization
Lecture 04 normalization emailharmeet
 

What's hot (20)

PostGreSQL Performance Tuning
PostGreSQL Performance TuningPostGreSQL Performance Tuning
PostGreSQL Performance Tuning
 
Dml and ddl
Dml and ddlDml and ddl
Dml and ddl
 
Bsc cs ii-dbms-u-iv-normalization
Bsc cs ii-dbms-u-iv-normalizationBsc cs ii-dbms-u-iv-normalization
Bsc cs ii-dbms-u-iv-normalization
 
Normalization in DBMS
Normalization in DBMSNormalization in DBMS
Normalization in DBMS
 
Oracle architecture ppt
Oracle architecture pptOracle architecture ppt
Oracle architecture ppt
 
Database design process
Database design processDatabase design process
Database design process
 
12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS12. Indexing and Hashing in DBMS
12. Indexing and Hashing in DBMS
 
Database Management System 1
Database Management System 1Database Management System 1
Database Management System 1
 
Active database
Active databaseActive database
Active database
 
Best Practices for Becoming an Exceptional Postgres DBA
Best Practices for Becoming an Exceptional Postgres DBA Best Practices for Becoming an Exceptional Postgres DBA
Best Practices for Becoming an Exceptional Postgres DBA
 
The Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian BanduletThe Evolution of Database Technologies Christian Bandulet
The Evolution of Database Technologies Christian Bandulet
 
Library management system
Library management systemLibrary management system
Library management system
 
Database fundamentals(database)
Database fundamentals(database)Database fundamentals(database)
Database fundamentals(database)
 
Data administration
Data administrationData administration
Data administration
 
Library Management System Waterfall Model
Library Management System Waterfall ModelLibrary Management System Waterfall Model
Library Management System Waterfall Model
 
A Day In The Life Of A DBA Manager
A Day In The Life Of A DBA ManagerA Day In The Life Of A DBA Manager
A Day In The Life Of A DBA Manager
 
k Nearest Neighbor
k Nearest Neighbork Nearest Neighbor
k Nearest Neighbor
 
K mean-clustering algorithm
K mean-clustering algorithmK mean-clustering algorithm
K mean-clustering algorithm
 
Difference between molap, rolap and holap in ssas
Difference between molap, rolap and holap  in ssasDifference between molap, rolap and holap  in ssas
Difference between molap, rolap and holap in ssas
 
Lecture 04 normalization
Lecture 04 normalization Lecture 04 normalization
Lecture 04 normalization
 

Viewers also liked

Viewers also liked (14)

Database normalization
Database normalizationDatabase normalization
Database normalization
 
Database design & Normalization (1NF, 2NF, 3NF)
Database design & Normalization (1NF, 2NF, 3NF)Database design & Normalization (1NF, 2NF, 3NF)
Database design & Normalization (1NF, 2NF, 3NF)
 
Normalization in a Database
Normalization in a DatabaseNormalization in a Database
Normalization in a Database
 
Dbms normalization
Dbms normalizationDbms normalization
Dbms normalization
 
Database - Normalization
Database - NormalizationDatabase - Normalization
Database - Normalization
 
Database Normalization
Database NormalizationDatabase Normalization
Database Normalization
 
Normalization in Database
Normalization in DatabaseNormalization in Database
Normalization in Database
 
Anomalies in database
Anomalies in databaseAnomalies in database
Anomalies in database
 
Relational Database Design
Relational Database DesignRelational Database Design
Relational Database Design
 
Normalization in DBMS
Normalization in DBMSNormalization in DBMS
Normalization in DBMS
 
Normalization 방법
Normalization 방법 Normalization 방법
Normalization 방법
 
Normalization
NormalizationNormalization
Normalization
 
Entity Relationship Diagram
Entity Relationship DiagramEntity Relationship Diagram
Entity Relationship Diagram
 
DBMS - Normalization
DBMS - NormalizationDBMS - Normalization
DBMS - Normalization
 

Recently uploaded

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...FIDO Alliance
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024Stephen Perrenod
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?Mark Billinghurst
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...ScyllaDB
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfFIDO Alliance
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!Memoori
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Hiroshi SHIBATA
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Patrick Viafore
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Skynet Technologies
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch TuesdayIvanti
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewDianaGray10
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024Lorenzo Miniero
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfFIDO Alliance
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfalexjohnson7307
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераMark Opanasiuk
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingScyllaDB
 
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdfMuhammad Subhan
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsLeah Henrickson
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfFIDO Alliance
 

Recently uploaded (20)

ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
ASRock Industrial FDO Solutions in Action for Industrial Edge AI _ Kenny at A...
 
TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024TopCryptoSupers 12thReport OrionX May2024
TopCryptoSupers 12thReport OrionX May2024
 
The Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and InsightThe Zero-ETL Approach: Enhancing Data Agility and Insight
The Zero-ETL Approach: Enhancing Data Agility and Insight
 
The Metaverse: Are We There Yet?
The  Metaverse:    Are   We  There  Yet?The  Metaverse:    Are   We  There  Yet?
The Metaverse: Are We There Yet?
 
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
Event-Driven Architecture Masterclass: Integrating Distributed Data Stores Ac...
 
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdfThe Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
The Value of Certifying Products for FDO _ Paul at FIDO Alliance.pdf
 
State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!State of the Smart Building Startup Landscape 2024!
State of the Smart Building Startup Landscape 2024!
 
Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024Long journey of Ruby Standard library at RubyKaigi 2024
Long journey of Ruby Standard library at RubyKaigi 2024
 
Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024Extensible Python: Robustness through Addition - PyCon 2024
Extensible Python: Robustness through Addition - PyCon 2024
 
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
Human Expert Website Manual WCAG 2.0 2.1 2.2 Audit - Digital Accessibility Au...
 
2024 May Patch Tuesday
2024 May Patch Tuesday2024 May Patch Tuesday
2024 May Patch Tuesday
 
UiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overviewUiPath manufacturing technology benefits and AI overview
UiPath manufacturing technology benefits and AI overview
 
WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024WebRTC and SIP not just audio and video @ OpenSIPS 2024
WebRTC and SIP not just audio and video @ OpenSIPS 2024
 
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdfWhere to Learn More About FDO _ Richard at FIDO Alliance.pdf
Where to Learn More About FDO _ Richard at FIDO Alliance.pdf
 
Generative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdfGenerative AI Use Cases and Applications.pdf
Generative AI Use Cases and Applications.pdf
 
Intro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджераIntro in Product Management - Коротко про професію продакт менеджера
Intro in Product Management - Коротко про професію продакт менеджера
 
Event-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream ProcessingEvent-Driven Architecture Masterclass: Challenges in Stream Processing
Event-Driven Architecture Masterclass: Challenges in Stream Processing
 
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf
“Iamnobody89757” Understanding the Mysterious of Digital Identity.pdf
 
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on ThanabotsContinuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
Continuing Bonds Through AI: A Hermeneutic Reflection on Thanabots
 
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdfIntroduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
Introduction to FDO and How It works Applications _ Richard at FIDO Alliance.pdf
 

Db normalization

  • 1. Database Normalization ● Overview – Definition of database normalization – Why normalize? – First normal form – Second normal form – Third normal form Information for this presentation borrowed from http://www.devshed.com/c/a/MySQL/An-Introduction-to-Database-Normalization/
  • 2. Database Normalization ● Definition – Optimizing table structures – Removing duplicate data entries – Accomplished by thouroughly investigating the various data types and their relationships with one another – Follows a series of normalization “forms” or states
  • 3. Database Normalization ● Why Normalize? – Improved speed – More efficient use of space – Increased data integrity ● (decreased chance that data can get messed up due to maintenance)
  • 4. Database Normalization ● A sad, sad database: – Refer to the following poor database design: student_id class_name time location professor_id 999-40-9876 Math 148 MWF 11:30 Rm. 432 prof145 999-43-0987 Physics 113 TR 1:30 Rm. 12 prof143 999-42-9842 Botany 42 F 12:45 Rm. 9 prof167 999-41-9832 Matj 148 MWF 11:30 Rm. 432 prof145 – Problems ● no need to repeatedly store the class time and professor ID ● redundancy introduces the possibility for error (Matj 148)
  • 5. Database Normalization ● First Normal Form – calls for the elimination of repeated groups of data by creating separate tables of related data – Student information: StudentID StudentName Major college collegeLocation – Class information: StudentID ClassID ClassName – Professor Information: ProfessorID ProfessorName
  • 6. Database Normalization ● Second Normal Form – Elimination of redundant data ● Example data in Class Information: studentID classID className 134-56-7890 M148 Math 148 123-45-7894 P113 Physics 113 534-98-9009 H151 History 151 134-56-7890 H151 History 151 Use: To get Class Information: ClassID ClassName studentID classID M148 Math 148 134-56-7890 M148 P113 Physics 113 123-45-7894 P113 H151 History 151 534-98-9009 H151 134-56-7890 H151
  • 7. Database Normalization ● Third Normal Form – eliminate all attributes(column headers) from a table that are not directly dependent upon the primary key ● college and collegeLocation attributes are less dependent upon the studentID than they are on the major attribute ● New college table: major college collegeLocation ● Revised student table: studentID studentName Major
  • 8. Database Normalization ● Old Design: student_id class_name time location professor_id 999-40-9876 Math 148 MWF 11:30 Rm. 432 prof145 999-43-0987 Physics 113 TR 1:30 Rm. 12 prof143 999-42-9842 Botany 42 F 12:45 Rm. 9 prof167 999-41-9832 Matj 148 MWF 11:30 Rm. 432 prof145 New Design: Student Enrollment Class studentID studentName Major studentID classID ClassID ClassName ProfessorID College Professor major college collegeLocation ProfessorID ProfessorName
  • 10. Normalization Assignment ● For your tool: – Compile list of all data items used – Place all data into one table – Complete 1NF and describe reasons why this is better (or if data is already in 1NF continue) – Complete 2NF in the same manner – Complete 3NF in the same manner