SlideShare ist ein Scribd-Unternehmen logo
1 von 14
Case studies oracle
Submitted on
P.Priyadharshini
II-MSC(IT)
Department of cs & IT
Nadar saraswathi college of arts & Science
Theni
Topic
• Oracle
• Database design tool
• Querying tool
• Sql variation and extension
• Storage and indexing
• Query processing amd optimizating
• Optimating
• External data sources
• External table
• Database administration tool
• Access path selection
Oracle
Oracle is American multinational computer
technologycorporation headquartered in redwood city,
califonia, united states.
Founded by larry elison, bob miner, ed oates in the year
1977.
Company specializes in developing and marketing computer
hardware systems and enterpriae software product
particularly bramds database management systems
Database Design Tools
• Oracle’s design tool include In the Oracle internet Development suite
Of Tools Various aspects Of application Development, including Tools
for forms development , data modeling , quering and Reporting . Class
modeling To generate code Bussiness coMponent for java framework
Modeling general purpose control folw modeling
• Support Xml data exchange other uml tool
• Support modeling techniques As er diagram, information engineering
and object analysis and Oracle.
Querying tools
• Relation Engine Can scales to much data sets
• A common security model used analytical application
datawarehouse
• Multidimensional modeling integrated datawarehouse modeling
• Relational database management systen larger set feature
functionality many area such as high availability, backup and
recovery and third patty tool support
• No need train database administrator two database Engines
Sql variations And extensions
• Connect by, form of tree traversal allow transitive clousure style calculation in
single sql statement
• Upset and multitable inserts :upset operation update and insert , and useful
merging new data old data in datawarehouse application.multitable inserts
allow multiple tables updated based on single scan of new data.
• Object relational feature
• Object types :single inheritance model for type hierarches.
• Collection tables oracle support varrays variable length arrays, and nested
tables
• Table features:table function oracle can be nested.
• Object view:virtual object table view data store in regular relational table
• Methods pl/sql java or c
• User define aggregate function:sqlstatement same way built in function sum
count
• Xmldata types :store inDex xml document
Storage and indexing
• Table space :system table space create data dictionary table
• Table space create store user data
• Segment:
• Data segment one data segment per partition.
• Index segment one index segment per partition
• Temporary segment data To disk or data inserted temporary table
• Rollback segment undo information uncommitted transaction can
be rolled back.
Query processing and optimization
• Execution methods
• Full table scan query processor entire table getting information blocks
makeup scanning those blocks
• Index scan processor creates start and /or stop key condition query scan
relevant part of index
• Index fast full scan traverses index leaf blocks order fast full scan does
not guarantee output preServes Sort above
• Inner join performs join hash join row id form different indices
• Cluster and hash cluster access processor access data using cluster key
Optimization
• Querytransformation
• Oracle does query optimatization in several stage .techniques
relating query transformation take place access path selection but
oracle several type of cost based query transformation staNdard
version
• Distributed databases
• Oracle als transparently supporttansaction spanning multiple by
built in two phase commit protocol
External data souces
• Data warehousing large amount of data regularly loaded from
transactional system
• Sql* loader
• Oracle direct load utility sql*loader fast parallel large amount of
data from external file.
• Filtering operation data being loaded
External Tables
• The external table primarily intended extraction transformation
loading(etl) in datawarehouSing environment. The data warehouse
flat file
• Create table table as
• Select …..from<external table>
• Where……
Database administration tool
• The decision use subquering for paticular dimensional cost
effective decisions query better original based optimizer cost
estimates
Access path selection
• Each operation optimizer cost function generatiom combinatio
operation lower overall cost.
• Cpu speed disk i/o performed optimizier statistics
• Nontrivialnumber of joinquery optimizer
• Smaller number of order in better response time
Case studies on Oracle database design and administration

Weitere ähnliche Inhalte

Was ist angesagt?

ADL/U-SQL Introduction (SQLBits 2016)
ADL/U-SQL Introduction (SQLBits 2016)ADL/U-SQL Introduction (SQLBits 2016)
ADL/U-SQL Introduction (SQLBits 2016)Michael Rys
 
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Michael Rys
 
Microsoft's Hadoop Story
Microsoft's Hadoop StoryMicrosoft's Hadoop Story
Microsoft's Hadoop StoryMichael Rys
 
U-SQL Partitioned Data and Tables (SQLBits 2016)
U-SQL Partitioned Data and Tables (SQLBits 2016)U-SQL Partitioned Data and Tables (SQLBits 2016)
U-SQL Partitioned Data and Tables (SQLBits 2016)Michael Rys
 
U-SQL Learning Resources (SQLBits 2016)
U-SQL Learning Resources (SQLBits 2016)U-SQL Learning Resources (SQLBits 2016)
U-SQL Learning Resources (SQLBits 2016)Michael Rys
 
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)Michael Rys
 
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
Taming the Data Science Monster with A New ‘Sword’ – U-SQLTaming the Data Science Monster with A New ‘Sword’ – U-SQL
Taming the Data Science Monster with A New ‘Sword’ – U-SQLMichael Rys
 
U-SQL Reading & Writing Files (SQLBits 2016)
U-SQL Reading & Writing Files (SQLBits 2016)U-SQL Reading & Writing Files (SQLBits 2016)
U-SQL Reading & Writing Files (SQLBits 2016)Michael Rys
 
U-SQL Intro (SQLBits 2016)
U-SQL Intro (SQLBits 2016)U-SQL Intro (SQLBits 2016)
U-SQL Intro (SQLBits 2016)Michael Rys
 
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)U-SQL User-Defined Operators (UDOs) (SQLBits 2016)
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)Michael Rys
 
Azure Data Factory Data Flow Limited Preview for January 2019
Azure Data Factory Data Flow Limited Preview for January 2019Azure Data Factory Data Flow Limited Preview for January 2019
Azure Data Factory Data Flow Limited Preview for January 2019Mark Kromer
 
Online Analytical Processing
Online Analytical ProcessingOnline Analytical Processing
Online Analytical Processingnayakslideshare
 
Killer Scenarios with Data Lake in Azure with U-SQL
Killer Scenarios with Data Lake in Azure with U-SQLKiller Scenarios with Data Lake in Azure with U-SQL
Killer Scenarios with Data Lake in Azure with U-SQLMichael Rys
 
Using C# with U-SQL (SQLBits 2016)
Using C# with U-SQL (SQLBits 2016)Using C# with U-SQL (SQLBits 2016)
Using C# with U-SQL (SQLBits 2016)Michael Rys
 
SQL Server Index and Partition Strategy
SQL Server Index and Partition StrategySQL Server Index and Partition Strategy
SQL Server Index and Partition StrategyHamid J. Fard
 
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012Andrew Brust
 
Appache Cassandra
Appache Cassandra  Appache Cassandra
Appache Cassandra nehabsairam
 
Comparing sql and nosql dbs
Comparing sql and nosql dbsComparing sql and nosql dbs
Comparing sql and nosql dbsVasilios Kuznos
 

Was ist angesagt? (20)

ADL/U-SQL Introduction (SQLBits 2016)
ADL/U-SQL Introduction (SQLBits 2016)ADL/U-SQL Introduction (SQLBits 2016)
ADL/U-SQL Introduction (SQLBits 2016)
 
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...
 
Microsoft's Hadoop Story
Microsoft's Hadoop StoryMicrosoft's Hadoop Story
Microsoft's Hadoop Story
 
U-SQL Partitioned Data and Tables (SQLBits 2016)
U-SQL Partitioned Data and Tables (SQLBits 2016)U-SQL Partitioned Data and Tables (SQLBits 2016)
U-SQL Partitioned Data and Tables (SQLBits 2016)
 
U-SQL Learning Resources (SQLBits 2016)
U-SQL Learning Resources (SQLBits 2016)U-SQL Learning Resources (SQLBits 2016)
U-SQL Learning Resources (SQLBits 2016)
 
Cassandra Learning
Cassandra LearningCassandra Learning
Cassandra Learning
 
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)
 
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
Taming the Data Science Monster with A New ‘Sword’ – U-SQLTaming the Data Science Monster with A New ‘Sword’ – U-SQL
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
 
U-SQL Reading & Writing Files (SQLBits 2016)
U-SQL Reading & Writing Files (SQLBits 2016)U-SQL Reading & Writing Files (SQLBits 2016)
U-SQL Reading & Writing Files (SQLBits 2016)
 
U-SQL Intro (SQLBits 2016)
U-SQL Intro (SQLBits 2016)U-SQL Intro (SQLBits 2016)
U-SQL Intro (SQLBits 2016)
 
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)U-SQL User-Defined Operators (UDOs) (SQLBits 2016)
U-SQL User-Defined Operators (UDOs) (SQLBits 2016)
 
Azure Data Factory Data Flow Limited Preview for January 2019
Azure Data Factory Data Flow Limited Preview for January 2019Azure Data Factory Data Flow Limited Preview for January 2019
Azure Data Factory Data Flow Limited Preview for January 2019
 
Online Analytical Processing
Online Analytical ProcessingOnline Analytical Processing
Online Analytical Processing
 
Killer Scenarios with Data Lake in Azure with U-SQL
Killer Scenarios with Data Lake in Azure with U-SQLKiller Scenarios with Data Lake in Azure with U-SQL
Killer Scenarios with Data Lake in Azure with U-SQL
 
Using C# with U-SQL (SQLBits 2016)
Using C# with U-SQL (SQLBits 2016)Using C# with U-SQL (SQLBits 2016)
Using C# with U-SQL (SQLBits 2016)
 
Olap operations
Olap operationsOlap operations
Olap operations
 
SQL Server Index and Partition Strategy
SQL Server Index and Partition StrategySQL Server Index and Partition Strategy
SQL Server Index and Partition Strategy
 
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
 
Appache Cassandra
Appache Cassandra  Appache Cassandra
Appache Cassandra
 
Comparing sql and nosql dbs
Comparing sql and nosql dbsComparing sql and nosql dbs
Comparing sql and nosql dbs
 

Ähnlich wie Case studies on Oracle database design and administration

Build a modern data platform.pptx
Build a modern data platform.pptxBuild a modern data platform.pptx
Build a modern data platform.pptxIke Ellis
 
U-SQL - Azure Data Lake Analytics for Developers
U-SQL - Azure Data Lake Analytics for DevelopersU-SQL - Azure Data Lake Analytics for Developers
U-SQL - Azure Data Lake Analytics for DevelopersMichael Rys
 
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)Michael Rys
 
Survey of the Microsoft Azure Data Landscape
Survey of the Microsoft Azure Data LandscapeSurvey of the Microsoft Azure Data Landscape
Survey of the Microsoft Azure Data LandscapeIke Ellis
 
Data warehouse introduction
Data warehouse introductionData warehouse introduction
Data warehouse introductionMurli Jha
 
10 Big Data Technologies you Didn't Know About
10 Big Data Technologies you Didn't Know About 10 Big Data Technologies you Didn't Know About
10 Big Data Technologies you Didn't Know About Jesus Rodriguez
 
How to use Big Data and Data Lake concept in business using Hadoop and Spark...
 How to use Big Data and Data Lake concept in business using Hadoop and Spark... How to use Big Data and Data Lake concept in business using Hadoop and Spark...
How to use Big Data and Data Lake concept in business using Hadoop and Spark...Institute of Contemporary Sciences
 
Data modeling trends for analytics
Data modeling trends for analyticsData modeling trends for analytics
Data modeling trends for analyticsIke Ellis
 
Building better SQL Server Databases
Building better SQL Server DatabasesBuilding better SQL Server Databases
Building better SQL Server DatabasesColdFusionConference
 
CRM UG Belux March 2017 - Power BI and Dynamics 365
CRM UG Belux March 2017 - Power BI and Dynamics 365CRM UG Belux March 2017 - Power BI and Dynamics 365
CRM UG Belux March 2017 - Power BI and Dynamics 365Joris Poelmans
 
Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Vinay Kumar
 
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Accelerating Business Intelligence Solutions with Microsoft Azure   passAccelerating Business Intelligence Solutions with Microsoft Azure   pass
Accelerating Business Intelligence Solutions with Microsoft Azure passJason Strate
 
Data Modeling on Azure for Analytics
Data Modeling on Azure for AnalyticsData Modeling on Azure for Analytics
Data Modeling on Azure for AnalyticsIke Ellis
 
Choosing the Right Business Intelligence Tools for Your Data and Architectura...
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Choosing the Right Business Intelligence Tools for Your Data and Architectura...
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Victor Holman
 

Ähnlich wie Case studies on Oracle database design and administration (20)

Build a modern data platform.pptx
Build a modern data platform.pptxBuild a modern data platform.pptx
Build a modern data platform.pptx
 
U-SQL - Azure Data Lake Analytics for Developers
U-SQL - Azure Data Lake Analytics for DevelopersU-SQL - Azure Data Lake Analytics for Developers
U-SQL - Azure Data Lake Analytics for Developers
 
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
 
Survey of the Microsoft Azure Data Landscape
Survey of the Microsoft Azure Data LandscapeSurvey of the Microsoft Azure Data Landscape
Survey of the Microsoft Azure Data Landscape
 
Data warehouse introduction
Data warehouse introductionData warehouse introduction
Data warehouse introduction
 
Elasticsearch Introduction at BigData meetup
Elasticsearch Introduction at BigData meetupElasticsearch Introduction at BigData meetup
Elasticsearch Introduction at BigData meetup
 
10 Big Data Technologies you Didn't Know About
10 Big Data Technologies you Didn't Know About 10 Big Data Technologies you Didn't Know About
10 Big Data Technologies you Didn't Know About
 
How to use Big Data and Data Lake concept in business using Hadoop and Spark...
 How to use Big Data and Data Lake concept in business using Hadoop and Spark... How to use Big Data and Data Lake concept in business using Hadoop and Spark...
How to use Big Data and Data Lake concept in business using Hadoop and Spark...
 
Taming the shrew Power BI
Taming the shrew Power BITaming the shrew Power BI
Taming the shrew Power BI
 
Oracle OpenWo2014 review part 03 three_paa_s_database
Oracle OpenWo2014 review part 03 three_paa_s_databaseOracle OpenWo2014 review part 03 three_paa_s_database
Oracle OpenWo2014 review part 03 three_paa_s_database
 
Oracle DB
Oracle DBOracle DB
Oracle DB
 
Revision
RevisionRevision
Revision
 
Data modeling trends for analytics
Data modeling trends for analyticsData modeling trends for analytics
Data modeling trends for analytics
 
Building better SQL Server Databases
Building better SQL Server DatabasesBuilding better SQL Server Databases
Building better SQL Server Databases
 
CRM UG Belux March 2017 - Power BI and Dynamics 365
CRM UG Belux March 2017 - Power BI and Dynamics 365CRM UG Belux March 2017 - Power BI and Dynamics 365
CRM UG Belux March 2017 - Power BI and Dynamics 365
 
Roaring with elastic search sangam2018
Roaring with elastic search sangam2018Roaring with elastic search sangam2018
Roaring with elastic search sangam2018
 
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Accelerating Business Intelligence Solutions with Microsoft Azure   passAccelerating Business Intelligence Solutions with Microsoft Azure   pass
Accelerating Business Intelligence Solutions with Microsoft Azure pass
 
Data Modeling on Azure for Analytics
Data Modeling on Azure for AnalyticsData Modeling on Azure for Analytics
Data Modeling on Azure for Analytics
 
Micro strategy 7i
Micro strategy 7iMicro strategy 7i
Micro strategy 7i
 
Choosing the Right Business Intelligence Tools for Your Data and Architectura...
Choosing the Right Business Intelligence Tools for Your Data and Architectura...Choosing the Right Business Intelligence Tools for Your Data and Architectura...
Choosing the Right Business Intelligence Tools for Your Data and Architectura...
 

Kürzlich hochgeladen

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slidespraypatel2
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024The Digital Insurer
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxKatpro Technologies
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking MenDelhi Call girls
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountPuma Security, LLC
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilV3cube
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Igalia
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...gurkirankumar98700
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking MenDelhi Call girls
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdfhans926745
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Servicegiselly40
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Scriptwesley chun
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 

Kürzlich hochgeladen (20)

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law DevelopmentsTrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
 
Slack Application Development 101 Slides
Slack Application Development 101 SlidesSlack Application Development 101 Slides
Slack Application Development 101 Slides
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptxFactors to Consider When Choosing Accounts Payable Services Providers.pptx
Factors to Consider When Choosing Accounts Payable Services Providers.pptx
 
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
 
Breaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path MountBreaking the Kubernetes Kill Chain: Host Path Mount
Breaking the Kubernetes Kill Chain: Host Path Mount
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
Developing An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of BrazilDeveloping An App To Navigate The Roads of Brazil
Developing An App To Navigate The Roads of Brazil
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
Kalyanpur ) Call Girls in Lucknow Finest Escorts Service 🍸 8923113531 🎰 Avail...
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men08448380779 Call Girls In Greater Kailash - I Women Seeking Men
08448380779 Call Girls In Greater Kailash - I Women Seeking Men
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 

Case studies on Oracle database design and administration

  • 1. Case studies oracle Submitted on P.Priyadharshini II-MSC(IT) Department of cs & IT Nadar saraswathi college of arts & Science Theni
  • 2. Topic • Oracle • Database design tool • Querying tool • Sql variation and extension • Storage and indexing • Query processing amd optimizating • Optimating • External data sources • External table • Database administration tool • Access path selection
  • 3. Oracle Oracle is American multinational computer technologycorporation headquartered in redwood city, califonia, united states. Founded by larry elison, bob miner, ed oates in the year 1977. Company specializes in developing and marketing computer hardware systems and enterpriae software product particularly bramds database management systems
  • 4. Database Design Tools • Oracle’s design tool include In the Oracle internet Development suite Of Tools Various aspects Of application Development, including Tools for forms development , data modeling , quering and Reporting . Class modeling To generate code Bussiness coMponent for java framework Modeling general purpose control folw modeling • Support Xml data exchange other uml tool • Support modeling techniques As er diagram, information engineering and object analysis and Oracle.
  • 5. Querying tools • Relation Engine Can scales to much data sets • A common security model used analytical application datawarehouse • Multidimensional modeling integrated datawarehouse modeling • Relational database management systen larger set feature functionality many area such as high availability, backup and recovery and third patty tool support • No need train database administrator two database Engines
  • 6. Sql variations And extensions • Connect by, form of tree traversal allow transitive clousure style calculation in single sql statement • Upset and multitable inserts :upset operation update and insert , and useful merging new data old data in datawarehouse application.multitable inserts allow multiple tables updated based on single scan of new data. • Object relational feature • Object types :single inheritance model for type hierarches. • Collection tables oracle support varrays variable length arrays, and nested tables • Table features:table function oracle can be nested. • Object view:virtual object table view data store in regular relational table • Methods pl/sql java or c • User define aggregate function:sqlstatement same way built in function sum count • Xmldata types :store inDex xml document
  • 7. Storage and indexing • Table space :system table space create data dictionary table • Table space create store user data • Segment: • Data segment one data segment per partition. • Index segment one index segment per partition • Temporary segment data To disk or data inserted temporary table • Rollback segment undo information uncommitted transaction can be rolled back.
  • 8. Query processing and optimization • Execution methods • Full table scan query processor entire table getting information blocks makeup scanning those blocks • Index scan processor creates start and /or stop key condition query scan relevant part of index • Index fast full scan traverses index leaf blocks order fast full scan does not guarantee output preServes Sort above • Inner join performs join hash join row id form different indices • Cluster and hash cluster access processor access data using cluster key
  • 9. Optimization • Querytransformation • Oracle does query optimatization in several stage .techniques relating query transformation take place access path selection but oracle several type of cost based query transformation staNdard version • Distributed databases • Oracle als transparently supporttansaction spanning multiple by built in two phase commit protocol
  • 10. External data souces • Data warehousing large amount of data regularly loaded from transactional system • Sql* loader • Oracle direct load utility sql*loader fast parallel large amount of data from external file. • Filtering operation data being loaded
  • 11. External Tables • The external table primarily intended extraction transformation loading(etl) in datawarehouSing environment. The data warehouse flat file • Create table table as • Select …..from<external table> • Where……
  • 12. Database administration tool • The decision use subquering for paticular dimensional cost effective decisions query better original based optimizer cost estimates
  • 13. Access path selection • Each operation optimizer cost function generatiom combinatio operation lower overall cost. • Cpu speed disk i/o performed optimizier statistics • Nontrivialnumber of joinquery optimizer • Smaller number of order in better response time