SlideShare ist ein Scribd-Unternehmen logo
1 von 16
SQL Server Integration ServicesAnd Data Mining
Overview Standard Tasks in SSIS SSIS Packages Data Flow Working with SSIS in Data Mining Data Mining Transformations Text Mining Transformations Summary
Overview of SSIS SQL Server Integration Services (SSIS) is a component of the Microsoft SQL Server database software which can be used to perform a broad range of data migration tasks. SSIS is a platform for data integration and workflow applications. It features a fast and flexible data warehousing tool used for data extraction, transformation, and loading (ETL). The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data.
SQL Server Integration Services
SSIS Designer
SSIS Packages A package is the basic deployment and execution unit of an SSIS project. An SSIS package is the container for SSIS flows.  You can create an SSIS package by right-clicking the SSIS Package folder in the Integration Services project folder and selecting the New SSIS Package menu item. An SSIS project may contain multiple packages. A package contains only one control flow, which may contain one or more data flows. In addition to control flow and data flow, a package contains SSIS connections and package variables.
Task Flow and Containers Tasks are listed in the SSIS Toolbox.  You can add a task to the package by dragging it from the Toolbox and dropping it into the package designer. A package usually contains multiple tasks in a task flow.  Multiple tasks are organized in sequential order with precedence constraints. Containers are SSIS objects that provide structure to a package.  Each package has a container, which stores the flows of a package.
 Data Flow Example
How to Set the Properties of a Task or Container? To set the properties of a task or container by using theProperties window : ,[object Object]
In Solution Explorer, double-click the package to open it.
Click the Control Flow tab.
On the design surface of the Control Flow tab, right-click the task or container, and then click Properties.
In the Properties window, update the property value.
Optionally, create property expressions to dynamically update the properties of the task or container.
To save the updated package, click Save Selected Items on the File menu.,[object Object]
In Solution Explorer, double-click the package to open it.

Weitere ähnliche Inhalte

Was ist angesagt?

warner-DP-203-slides.pptx
warner-DP-203-slides.pptxwarner-DP-203-slides.pptx
warner-DP-203-slides.pptx
HibaB2
 

Was ist angesagt? (20)

Azure Data Factory V2; The Data Flows
Azure Data Factory V2; The Data FlowsAzure Data Factory V2; The Data Flows
Azure Data Factory V2; The Data Flows
 
Architecture of integration services
Architecture of integration servicesArchitecture of integration services
Architecture of integration services
 
SQL Server Integration Services – Enterprise Manageability
SQL Server Integration Services – Enterprise ManageabilitySQL Server Integration Services – Enterprise Manageability
SQL Server Integration Services – Enterprise Manageability
 
Integrating Oracle Data Integrator with Oracle GoldenGate 12c
Integrating Oracle Data Integrator with Oracle GoldenGate 12cIntegrating Oracle Data Integrator with Oracle GoldenGate 12c
Integrating Oracle Data Integrator with Oracle GoldenGate 12c
 
Data Vault and DW2.0
Data Vault and DW2.0Data Vault and DW2.0
Data Vault and DW2.0
 
OSA Con 2022 - Apache Iceberg_ An Architectural Look Under the Covers - Alex ...
OSA Con 2022 - Apache Iceberg_ An Architectural Look Under the Covers - Alex ...OSA Con 2022 - Apache Iceberg_ An Architectural Look Under the Covers - Alex ...
OSA Con 2022 - Apache Iceberg_ An Architectural Look Under the Covers - Alex ...
 
Operational Data Vault
Operational Data VaultOperational Data Vault
Operational Data Vault
 
Azure Data Lake Intro (SQLBits 2016)
Azure Data Lake Intro (SQLBits 2016)Azure Data Lake Intro (SQLBits 2016)
Azure Data Lake Intro (SQLBits 2016)
 
SQL Server Integration Services
SQL Server Integration ServicesSQL Server Integration Services
SQL Server Integration Services
 
Top Three Big Data Governance Issues and How Apache ATLAS resolves it for the...
Top Three Big Data Governance Issues and How Apache ATLAS resolves it for the...Top Three Big Data Governance Issues and How Apache ATLAS resolves it for the...
Top Three Big Data Governance Issues and How Apache ATLAS resolves it for the...
 
warner-DP-203-slides.pptx
warner-DP-203-slides.pptxwarner-DP-203-slides.pptx
warner-DP-203-slides.pptx
 
Azure Data Factory v2
Azure Data Factory v2Azure Data Factory v2
Azure Data Factory v2
 
Azure Data Factory ETL Patterns in the Cloud
Azure Data Factory ETL Patterns in the CloudAzure Data Factory ETL Patterns in the Cloud
Azure Data Factory ETL Patterns in the Cloud
 
Introduction to NoSQL
Introduction to NoSQLIntroduction to NoSQL
Introduction to NoSQL
 
Sql server basics
Sql server basicsSql server basics
Sql server basics
 
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)
 
MSBI-SSRS PPT
MSBI-SSRS PPTMSBI-SSRS PPT
MSBI-SSRS PPT
 
MS-SQL SERVER ARCHITECTURE
MS-SQL SERVER ARCHITECTUREMS-SQL SERVER ARCHITECTURE
MS-SQL SERVER ARCHITECTURE
 
Azure data bricks by Eugene Polonichko
Azure data bricks by Eugene PolonichkoAzure data bricks by Eugene Polonichko
Azure data bricks by Eugene Polonichko
 
Building Lakehouses on Delta Lake with SQL Analytics Primer
Building Lakehouses on Delta Lake with SQL Analytics PrimerBuilding Lakehouses on Delta Lake with SQL Analytics Primer
Building Lakehouses on Delta Lake with SQL Analytics Primer
 

Andere mochten auch

05 SSIS Control Flow
05 SSIS Control Flow05 SSIS Control Flow
05 SSIS Control Flow
Slava Kokaev
 

Andere mochten auch (20)

SQLDay2013_ChrisWebb_CubeDesign&PerformanceTuning
SQLDay2013_ChrisWebb_CubeDesign&PerformanceTuningSQLDay2013_ChrisWebb_CubeDesign&PerformanceTuning
SQLDay2013_ChrisWebb_CubeDesign&PerformanceTuning
 
Professional Recycling - SSIS Custom Control Flow Components With Visual Stud...
Professional Recycling - SSIS Custom Control Flow Components With Visual Stud...Professional Recycling - SSIS Custom Control Flow Components With Visual Stud...
Professional Recycling - SSIS Custom Control Flow Components With Visual Stud...
 
Advanced integration services on microsoft ssis 1
Advanced integration services on microsoft ssis 1Advanced integration services on microsoft ssis 1
Advanced integration services on microsoft ssis 1
 
9\9 SSIS 2008R2_Training - Package Reliability and Package Execution
9\9 SSIS 2008R2_Training - Package Reliability and Package Execution9\9 SSIS 2008R2_Training - Package Reliability and Package Execution
9\9 SSIS 2008R2_Training - Package Reliability and Package Execution
 
Step by Step design cube using SSAS
Step by Step design cube using SSASStep by Step design cube using SSAS
Step by Step design cube using SSAS
 
05 SSIS Control Flow
05 SSIS Control Flow05 SSIS Control Flow
05 SSIS Control Flow
 
06 SSIS Data Flow
06 SSIS Data Flow06 SSIS Data Flow
06 SSIS Data Flow
 
SSIS Basic Data Flow
SSIS Basic Data FlowSSIS Basic Data Flow
SSIS Basic Data Flow
 
SSIS Data Flow Tasks
SSIS Data Flow Tasks SSIS Data Flow Tasks
SSIS Data Flow Tasks
 
SSIS 2008 R2 data flow
SSIS 2008 R2 data flowSSIS 2008 R2 data flow
SSIS 2008 R2 data flow
 
Control Flow Using SSIS
Control Flow Using SSISControl Flow Using SSIS
Control Flow Using SSIS
 
Business Intelligence with SQL Server
Business Intelligence with SQL ServerBusiness Intelligence with SQL Server
Business Intelligence with SQL Server
 
Developing ssas cube
Developing ssas cubeDeveloping ssas cube
Developing ssas cube
 
Informatica power center 9 Online Training
Informatica power center 9 Online TrainingInformatica power center 9 Online Training
Informatica power center 9 Online Training
 
SSIS control flow
SSIS control flowSSIS control flow
SSIS control flow
 
Designing and implementing_an_etl_framework
Designing and implementing_an_etl_frameworkDesigning and implementing_an_etl_framework
Designing and implementing_an_etl_framework
 
SSAS - Other Cube Browsers
SSAS - Other Cube BrowsersSSAS - Other Cube Browsers
SSAS - Other Cube Browsers
 
Informatica power center 9.x developer & admin Basics | Demo | Introduction
Informatica power center 9.x developer & admin Basics | Demo | Introduction Informatica power center 9.x developer & admin Basics | Demo | Introduction
Informatica power center 9.x developer & admin Basics | Demo | Introduction
 
Informatica student meterial
Informatica student meterialInformatica student meterial
Informatica student meterial
 
Informatica ppt
Informatica pptInformatica ppt
Informatica ppt
 

Ähnlich wie MS SQL SERVER: SSIS and data mining

Ssis2008 120710214348-phpapp02
Ssis2008 120710214348-phpapp02Ssis2008 120710214348-phpapp02
Ssis2008 120710214348-phpapp02
sumitkumar3201
 
CaseStudy-MohammedImranAlam-Xcelsius
CaseStudy-MohammedImranAlam-XcelsiusCaseStudy-MohammedImranAlam-Xcelsius
CaseStudy-MohammedImranAlam-Xcelsius
Mohammed Imran Alam
 
Presentation kaushal
Presentation kaushalPresentation kaushal
Presentation kaushal
Ajay Yadav
 
Architecture Specification - Visual Modeling Tool
Architecture Specification - Visual Modeling ToolArchitecture Specification - Visual Modeling Tool
Architecture Specification - Visual Modeling Tool
Adriaan Venter
 
B Woodward Portfolio
B Woodward PortfolioB Woodward Portfolio
B Woodward Portfolio
bwoodward
 
MMYERS Portfolio
MMYERS PortfolioMMYERS Portfolio
MMYERS Portfolio
Mike Myers
 
5 tsssisu sql_server_2012
5 tsssisu sql_server_20125 tsssisu sql_server_2012
5 tsssisu sql_server_2012
Steve Xu
 
Adopting AnswerModules ModuleSuite
Adopting AnswerModules ModuleSuiteAdopting AnswerModules ModuleSuite
Adopting AnswerModules ModuleSuite
AnswerModules
 

Ähnlich wie MS SQL SERVER: SSIS and data mining (20)

Ssis2008 120710214348-phpapp02
Ssis2008 120710214348-phpapp02Ssis2008 120710214348-phpapp02
Ssis2008 120710214348-phpapp02
 
CaseStudy-MohammedImranAlam-Xcelsius
CaseStudy-MohammedImranAlam-XcelsiusCaseStudy-MohammedImranAlam-Xcelsius
CaseStudy-MohammedImranAlam-Xcelsius
 
Microsoft-business-intelligence-training-in-mumbai
Microsoft-business-intelligence-training-in-mumbaiMicrosoft-business-intelligence-training-in-mumbai
Microsoft-business-intelligence-training-in-mumbai
 
Presentation kaushal
Presentation kaushalPresentation kaushal
Presentation kaushal
 
Business Intelligence Technology Presentation
Business Intelligence Technology PresentationBusiness Intelligence Technology Presentation
Business Intelligence Technology Presentation
 
Architecture Specification - Visual Modeling Tool
Architecture Specification - Visual Modeling ToolArchitecture Specification - Visual Modeling Tool
Architecture Specification - Visual Modeling Tool
 
MS SQL SERVER: Using the data mining tools
MS SQL SERVER: Using the data mining toolsMS SQL SERVER: Using the data mining tools
MS SQL SERVER: Using the data mining tools
 
MS SQL SERVER: Using the data mining tools
MS SQL SERVER: Using the data mining toolsMS SQL SERVER: Using the data mining tools
MS SQL SERVER: Using the data mining tools
 
B Woodward Portfolio
B Woodward PortfolioB Woodward Portfolio
B Woodward Portfolio
 
1\9.SSIS 2008R2_Training - Introduction to SSIS
1\9.SSIS 2008R2_Training - Introduction to SSIS1\9.SSIS 2008R2_Training - Introduction to SSIS
1\9.SSIS 2008R2_Training - Introduction to SSIS
 
Olap
OlapOlap
Olap
 
Test Strategy Utilising Mc Useful Tools
Test Strategy Utilising Mc Useful ToolsTest Strategy Utilising Mc Useful Tools
Test Strategy Utilising Mc Useful Tools
 
Trunk and branches for database configuration management
Trunk and branches for database configuration managementTrunk and branches for database configuration management
Trunk and branches for database configuration management
 
MMYERS Portfolio
MMYERS PortfolioMMYERS Portfolio
MMYERS Portfolio
 
UI Testing Pattern
UI Testing PatternUI Testing Pattern
UI Testing Pattern
 
5 tsssisu sql_server_2012
5 tsssisu sql_server_20125 tsssisu sql_server_2012
5 tsssisu sql_server_2012
 
Sas training in hyderabad
Sas training in hyderabadSas training in hyderabad
Sas training in hyderabad
 
Adopting AnswerModules ModuleSuite
Adopting AnswerModules ModuleSuiteAdopting AnswerModules ModuleSuite
Adopting AnswerModules ModuleSuite
 
Dbi h315
Dbi h315Dbi h315
Dbi h315
 
Agile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS ReportingAgile Methodology Approach to SSRS Reporting
Agile Methodology Approach to SSRS Reporting
 

Mehr von DataminingTools Inc

Mehr von DataminingTools Inc (20)

Terminology Machine Learning
Terminology Machine LearningTerminology Machine Learning
Terminology Machine Learning
 
Techniques Machine Learning
Techniques Machine LearningTechniques Machine Learning
Techniques Machine Learning
 
Machine learning Introduction
Machine learning IntroductionMachine learning Introduction
Machine learning Introduction
 
Areas of machine leanring
Areas of machine leanringAreas of machine leanring
Areas of machine leanring
 
AI: Planning and AI
AI: Planning and AIAI: Planning and AI
AI: Planning and AI
 
AI: Logic in AI 2
AI: Logic in AI 2AI: Logic in AI 2
AI: Logic in AI 2
 
AI: Logic in AI
AI: Logic in AIAI: Logic in AI
AI: Logic in AI
 
AI: Learning in AI 2
AI: Learning in AI 2AI: Learning in AI 2
AI: Learning in AI 2
 
AI: Learning in AI
AI: Learning in AI AI: Learning in AI
AI: Learning in AI
 
AI: Introduction to artificial intelligence
AI: Introduction to artificial intelligenceAI: Introduction to artificial intelligence
AI: Introduction to artificial intelligence
 
AI: Belief Networks
AI: Belief NetworksAI: Belief Networks
AI: Belief Networks
 
AI: AI & Searching
AI: AI & SearchingAI: AI & Searching
AI: AI & Searching
 
AI: AI & Problem Solving
AI: AI & Problem SolvingAI: AI & Problem Solving
AI: AI & Problem Solving
 
Data Mining: Text and web mining
Data Mining: Text and web miningData Mining: Text and web mining
Data Mining: Text and web mining
 
Data Mining: Outlier analysis
Data Mining: Outlier analysisData Mining: Outlier analysis
Data Mining: Outlier analysis
 
Data Mining: Mining stream time series and sequence data
Data Mining: Mining stream time series and sequence dataData Mining: Mining stream time series and sequence data
Data Mining: Mining stream time series and sequence data
 
Data Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlationsData Mining: Mining ,associations, and correlations
Data Mining: Mining ,associations, and correlations
 
Data Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysisData Mining: Graph mining and social network analysis
Data Mining: Graph mining and social network analysis
 
Data warehouse and olap technology
Data warehouse and olap technologyData warehouse and olap technology
Data warehouse and olap technology
 
Data Mining: Data processing
Data Mining: Data processingData Mining: Data processing
Data Mining: Data processing
 

Kürzlich hochgeladen

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Kürzlich hochgeladen (20)

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
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
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...
 
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
 
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live StreamsTop 5 Benefits OF Using Muvi Live Paywall For Live Streams
Top 5 Benefits OF Using Muvi Live Paywall For Live Streams
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024Manulife - Insurer Innovation Award 2024
Manulife - Insurer Innovation Award 2024
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...Apidays New York 2024 - The value of a flexible API Management solution for O...
Apidays New York 2024 - The value of a flexible API Management solution for O...
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
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
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 

MS SQL SERVER: SSIS and data mining

  • 1. SQL Server Integration ServicesAnd Data Mining
  • 2. Overview Standard Tasks in SSIS SSIS Packages Data Flow Working with SSIS in Data Mining Data Mining Transformations Text Mining Transformations Summary
  • 3. Overview of SSIS SQL Server Integration Services (SSIS) is a component of the Microsoft SQL Server database software which can be used to perform a broad range of data migration tasks. SSIS is a platform for data integration and workflow applications. It features a fast and flexible data warehousing tool used for data extraction, transformation, and loading (ETL). The tool may also be used to automate maintenance of SQL Server databases and updates to multidimensional cube data.
  • 6. SSIS Packages A package is the basic deployment and execution unit of an SSIS project. An SSIS package is the container for SSIS flows. You can create an SSIS package by right-clicking the SSIS Package folder in the Integration Services project folder and selecting the New SSIS Package menu item. An SSIS project may contain multiple packages. A package contains only one control flow, which may contain one or more data flows. In addition to control flow and data flow, a package contains SSIS connections and package variables.
  • 7. Task Flow and Containers Tasks are listed in the SSIS Toolbox. You can add a task to the package by dragging it from the Toolbox and dropping it into the package designer. A package usually contains multiple tasks in a task flow. Multiple tasks are organized in sequential order with precedence constraints. Containers are SSIS objects that provide structure to a package. Each package has a container, which stores the flows of a package.
  • 8. Data Flow Example
  • 9.
  • 10. In Solution Explorer, double-click the package to open it.
  • 12. On the design surface of the Control Flow tab, right-click the task or container, and then click Properties.
  • 14. Optionally, create property expressions to dynamically update the properties of the task or container.
  • 15.
  • 16. In Solution Explorer, double-click the package to open it.
  • 18. On the design surface of the Control Flow tab, right-click the task or container, and then click Edit to open the corresponding task or container editor.
  • 19. If the task or container editor has multiple nodes, click the node that contains the property that you want to set.
  • 20. Optionally, click Expressions and, on the Expressions page, create property expressions to dynamically update the properties of the task or container.
  • 22.
  • 23. Data Mining Transformations The data flow components can be categorized in three large groups, depending on their position in the data flow:
  • 24. Text Mining Transformations you must first bring the text to some form that can be consumed by the algorithms, to perform text mining with SQL Server Data Mining. The solution included in the product is to represent each piece of text as a collection of words and phrases.
  • 25.
  • 26. Clustering models that find similar documents based on common occurrences
  • 27.
  • 28. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net