SlideShare a Scribd company logo
1 of 29
Parikshit Savjani is a Premier Field Engineer with Microsoft with
specialization on SQL Server and Business Intelligence (SSAS,SSIS
and SSRS).His role involves consulting,performance
tuning,delivering workshops,chalk talks to Premier Customers of
Microsoft environment. He has 4.5 years of experience with
Microsoft & SQL Server.He contributes to the community by
Blogging his learnings on this site, www.sqlserverfaq.net & MSDN
Blogs
Provides context to slice the data                    Measures of interest.
Maps to the Master Table of the OLTP system maps      Maps to Transactional table of OLTP system.
Should be Denormalized & should be 1st NF             Are in 3 NF
Are wide in nature.                                   Narrow in Nature
Comparatively shallow as compared to Fact Tables.     Very Deep contains rows for every transaction
Include as many columns as you can think of           Aggregated in the context of the Dimensions
Are related to only Fact table and otherwise should   Consists of Key Columns and Measure Columns
be unrelated
The shape looks like a Star

A Star Schema contains a fact
   table and one or more
   dimension tables.
  1. A Fact Table: The central fact
     table store the numeric fact
     (measures) such as Sales
     dollars, Costs, Unit Sales etc.
  2. Dimension Tables: They
     surround the central fact
     table, and they store
     descriptive information about
     the measures
If there are m dimensions and if each dimension has n
rows, the theoretical size of the Cube is m*n.
Addition of one redundant Dimension can increase the
size of the Cube by large amount.
pariks@microsoft.com
http://www.sqlserverfaq.net

More Related Content

What's hot

Graphexamples how-to-do
Graphexamples how-to-doGraphexamples how-to-do
Graphexamples how-to-do
Melody Teoxon
 
Drawings, Tables, Graphs
Drawings, Tables, GraphsDrawings, Tables, Graphs
Drawings, Tables, Graphs
Dwayne Squires
 
Interpret data for use in charts and graphs
Interpret data for use in charts and graphsInterpret data for use in charts and graphs
Interpret data for use in charts and graphs
Charles Flynt
 
Stem & leaf, Bar graphs, and Histograms
Stem & leaf, Bar graphs, and HistogramsStem & leaf, Bar graphs, and Histograms
Stem & leaf, Bar graphs, and Histograms
bujols
 
Unit 8 presenting data in charts, graphs and tables
Unit 8 presenting data in charts, graphs and tablesUnit 8 presenting data in charts, graphs and tables
Unit 8 presenting data in charts, graphs and tables
igids
 

What's hot (20)

Charts in Microsoft Excel
Charts in Microsoft ExcelCharts in Microsoft Excel
Charts in Microsoft Excel
 
Graphexamples how-to-do
Graphexamples how-to-doGraphexamples how-to-do
Graphexamples how-to-do
 
MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra MSBI and Data WareHouse techniques by Quontra
MSBI and Data WareHouse techniques by Quontra
 
Drawings, Tables, Graphs
Drawings, Tables, GraphsDrawings, Tables, Graphs
Drawings, Tables, Graphs
 
Practical Considerations for Displaying Quantitative Data
Practical Considerations for Displaying Quantitative DataPractical Considerations for Displaying Quantitative Data
Practical Considerations for Displaying Quantitative Data
 
Types of charts in Excel and How to use them
Types of charts in Excel and How to use themTypes of charts in Excel and How to use them
Types of charts in Excel and How to use them
 
Charts And Graphs
Charts And GraphsCharts And Graphs
Charts And Graphs
 
Graphs and chars
Graphs and charsGraphs and chars
Graphs and chars
 
Tables, Graphs, and Charts Social Studies
Tables, Graphs, and Charts Social StudiesTables, Graphs, and Charts Social Studies
Tables, Graphs, and Charts Social Studies
 
Interpret data for use in charts and graphs
Interpret data for use in charts and graphsInterpret data for use in charts and graphs
Interpret data for use in charts and graphs
 
Assesment in education
Assesment in educationAssesment in education
Assesment in education
 
Dot Plot Presentation
Dot Plot PresentationDot Plot Presentation
Dot Plot Presentation
 
Understanding visual information:Figure, Graph, Table, and Diagram
Understanding visual information:Figure, Graph, Table, and DiagramUnderstanding visual information:Figure, Graph, Table, and Diagram
Understanding visual information:Figure, Graph, Table, and Diagram
 
Graphs
Graphs  Graphs
Graphs
 
TYPES ON CHARTS
TYPES ON CHARTS TYPES ON CHARTS
TYPES ON CHARTS
 
Trabajo Excel
Trabajo ExcelTrabajo Excel
Trabajo Excel
 
Stem & leaf, Bar graphs, and Histograms
Stem & leaf, Bar graphs, and HistogramsStem & leaf, Bar graphs, and Histograms
Stem & leaf, Bar graphs, and Histograms
 
2nd Test - Scatterplots
2nd Test - Scatterplots2nd Test - Scatterplots
2nd Test - Scatterplots
 
Unit 8 presenting data in charts, graphs and tables
Unit 8 presenting data in charts, graphs and tablesUnit 8 presenting data in charts, graphs and tables
Unit 8 presenting data in charts, graphs and tables
 
Types of Chart
Types of ChartTypes of Chart
Types of Chart
 

Similar to Bi dimension modelling basics

dataminingpres-150821063129-lva1-app6891 (3).pdf
dataminingpres-150821063129-lva1-app6891 (3).pdfdataminingpres-150821063129-lva1-app6891 (3).pdf
dataminingpres-150821063129-lva1-app6891 (3).pdf
AnilGupta681764
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modeling
vivekjv
 

Similar to Bi dimension modelling basics (20)

Data warehouse logical design
Data warehouse logical designData warehouse logical design
Data warehouse logical design
 
Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modeling
 
Data Warehouse_Architecture.pptx
Data Warehouse_Architecture.pptxData Warehouse_Architecture.pptx
Data Warehouse_Architecture.pptx
 
Dbms schemas for decision support
Dbms schemas for decision supportDbms schemas for decision support
Dbms schemas for decision support
 
dataminingpres-150821063129-lva1-app6891 (3).pdf
dataminingpres-150821063129-lva1-app6891 (3).pdfdataminingpres-150821063129-lva1-app6891 (3).pdf
dataminingpres-150821063129-lva1-app6891 (3).pdf
 
multi dimensional data model
multi dimensional data modelmulti dimensional data model
multi dimensional data model
 
22_presentation.ppt
22_presentation.ppt22_presentation.ppt
22_presentation.ppt
 
Data Analytics with R and SQL Server
Data Analytics with R and SQL ServerData Analytics with R and SQL Server
Data Analytics with R and SQL Server
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modeling
 
Office 365 Saturday Europe - Self-Service Business Intelligence with Power BI
Office 365 Saturday Europe - Self-Service Business Intelligence with Power BIOffice 365 Saturday Europe - Self-Service Business Intelligence with Power BI
Office 365 Saturday Europe - Self-Service Business Intelligence with Power BI
 
Database aggregation using metadata
Database aggregation using metadataDatabase aggregation using metadata
Database aggregation using metadata
 
Data Visualization - Presentation at Microsoft IT Pro Mumbai July 2010
Data Visualization - Presentation at Microsoft IT Pro Mumbai July 2010Data Visualization - Presentation at Microsoft IT Pro Mumbai July 2010
Data Visualization - Presentation at Microsoft IT Pro Mumbai July 2010
 
Become BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAPBecome BI Architect with 1KEY Agile BI Suite - OLAP
Become BI Architect with 1KEY Agile BI Suite - OLAP
 
Reporting services 2016 with labs
Reporting services 2016 with labsReporting services 2016 with labs
Reporting services 2016 with labs
 
Data modelling interview question
Data modelling interview questionData modelling interview question
Data modelling interview question
 
Ms access 2007 pptx
Ms access 2007 pptxMs access 2007 pptx
Ms access 2007 pptx
 
Data Visualization.pptx
Data Visualization.pptxData Visualization.pptx
Data Visualization.pptx
 
ADVANCED EXCEL COURSE IN LAXMI NAGAR
ADVANCED EXCEL COURSE IN LAXMI NAGARADVANCED EXCEL COURSE IN LAXMI NAGAR
ADVANCED EXCEL COURSE IN LAXMI NAGAR
 
Star schema
Star schemaStar schema
Star schema
 
Export Data Model | SQL Database Modeler
Export Data Model | SQL Database ModelerExport Data Model | SQL Database Modeler
Export Data Model | SQL Database Modeler
 

More from PARIKSHIT SAVJANI

SQL 2012: Indirect checkpointing
SQL 2012: Indirect checkpointingSQL 2012: Indirect checkpointing
SQL 2012: Indirect checkpointing
PARIKSHIT SAVJANI
 

More from PARIKSHIT SAVJANI (9)

Migrating on premises workload to azure sql database
Migrating on premises workload to azure sql databaseMigrating on premises workload to azure sql database
Migrating on premises workload to azure sql database
 
How SQL Server 2016 SP1 Changes the Game
How SQL Server 2016 SP1 Changes the GameHow SQL Server 2016 SP1 Changes the Game
How SQL Server 2016 SP1 Changes the Game
 
PASS VC: SQL Server Performance Monitoring and Baselining
PASS VC: SQL Server Performance Monitoring and BaseliningPASS VC: SQL Server Performance Monitoring and Baselining
PASS VC: SQL Server Performance Monitoring and Baselining
 
SQL ON Azure (decision-matrix)
SQL  ON  Azure (decision-matrix)SQL  ON  Azure (decision-matrix)
SQL ON Azure (decision-matrix)
 
Sql 2012 Upgrade Readiness Guide
Sql 2012 Upgrade Readiness GuideSql 2012 Upgrade Readiness Guide
Sql 2012 Upgrade Readiness Guide
 
Oracle on Azure at Windows Azure Conference 2014
Oracle on Azure at Windows Azure Conference 2014Oracle on Azure at Windows Azure Conference 2014
Oracle on Azure at Windows Azure Conference 2014
 
All about Kerberos In Microsoft BI
All about Kerberos In Microsoft BIAll about Kerberos In Microsoft BI
All about Kerberos In Microsoft BI
 
Indirect checkpointing
Indirect checkpointingIndirect checkpointing
Indirect checkpointing
 
SQL 2012: Indirect checkpointing
SQL 2012: Indirect checkpointingSQL 2012: Indirect checkpointing
SQL 2012: Indirect checkpointing
 

Bi dimension modelling basics

  • 1.
  • 2. Parikshit Savjani is a Premier Field Engineer with Microsoft with specialization on SQL Server and Business Intelligence (SSAS,SSIS and SSRS).His role involves consulting,performance tuning,delivering workshops,chalk talks to Premier Customers of Microsoft environment. He has 4.5 years of experience with Microsoft & SQL Server.He contributes to the community by Blogging his learnings on this site, www.sqlserverfaq.net & MSDN Blogs
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. Provides context to slice the data Measures of interest. Maps to the Master Table of the OLTP system maps Maps to Transactional table of OLTP system. Should be Denormalized & should be 1st NF Are in 3 NF Are wide in nature. Narrow in Nature Comparatively shallow as compared to Fact Tables. Very Deep contains rows for every transaction Include as many columns as you can think of Aggregated in the context of the Dimensions Are related to only Fact table and otherwise should Consists of Key Columns and Measure Columns be unrelated
  • 15. The shape looks like a Star A Star Schema contains a fact table and one or more dimension tables. 1. A Fact Table: The central fact table store the numeric fact (measures) such as Sales dollars, Costs, Unit Sales etc. 2. Dimension Tables: They surround the central fact table, and they store descriptive information about the measures
  • 16. If there are m dimensions and if each dimension has n rows, the theoretical size of the Cube is m*n. Addition of one redundant Dimension can increase the size of the Cube by large amount.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.