SlideShare a Scribd company logo
1 of 12
Download to read offline
CUBE DESIGN
BY HANNES MEYER


OnLine Analytical Processing OLAP
Agenda
  What are cubes?
  Multidimensionality

  Storage of multidimensional data.

  Hierarchies

  Operations

  Demo
What are cubes?
    Multi-dimensional representation of data
What are cubes (cont.)?
  syn: Hypercube, multidimensional database (MDB),
   olap cube
  Cubes can have more than three dimensions
Fact Tables
    Contain numerical measurements of a certain
     business process.
       E.g.   $12.000 sales in NY store on 12-01-08
    Additionally foreign keys to different dimension
     tables
       E.g.   further store/sales person information
    Center in star schema
Dimension Tables
  Contain attributes by which data can be grouped
  e.g. city/region of store, product category

  Linked to the fact table via their primary keys

  Slowly changing dimensions: dimensions which

   change over time. Can be dealt with in 3 ways:
       Overwritingold values
       Add new row to table, distinguish records by versioning

       Add new column (attribute) to existing row
Data Storage Models
    relational databases (ROLAP)
       Datain tables
       Summaries stored in precalculated tables

    multi-dimensional databases (MOLAP)
       Data  in multidimensional arrays
       + Less disk space

       + Better Performance (precalculated aggregates)

       - Time to aggregate & calculate

       - Updates require recalculation

    Hybrid (HOLAP)
Hierarchies
    Grouping of dimensions         e.g. country -> sales
    e.g. month -> semester -        region -> state -> city
     > quartal -> year               -> store
    2008                           Germany
       H1   2008                      Southern    germany
         Q1      2008                   BaWue
               Jan 2008                       Stuttgart
                                                    Store A
               Feb 2008
                                                    Store B
               March 2008
         Q2      2008 …                 Bavaria
                                               Munich
       H2   2008 …                                Store A B C
Operations: Slice
    Slicing is the process of retrieving a block of data
     from a cube by filtering on one dimension
Operations: Dice
    Dicingis the process of retrieving a block of data
     from a cube by filtering on all dimensions
Operations: Drill Up/ Down
  Drilling up: Presenting data at a higher level on the
   hierarchy e.g. Store -> Region
  Drilling Down: Presenting data at a lower level on
   the hierarchy Region -> Store
Building the cube in SSAS
    Preconditions
       Connecting  datasources
       Defining views

       Selecting dimensions

  Define fact & dimension tables & time dimension
  Select measures

  Deploy & query the cube

   Demo

More Related Content

What's hot

3 Steps for Breaking Down Data & Analytic Silos
3 Steps for Breaking Down Data & Analytic Silos 3 Steps for Breaking Down Data & Analytic Silos
3 Steps for Breaking Down Data & Analytic Silos Dun & Bradstreet
 
Tips & tricks to drive effective Master Data Management & ERP harmonization
Tips & tricks to drive effective Master Data Management & ERP harmonizationTips & tricks to drive effective Master Data Management & ERP harmonization
Tips & tricks to drive effective Master Data Management & ERP harmonizationVerdantis
 
Big Data Evolution
Big Data EvolutionBig Data Evolution
Big Data Evolutionitnewsafrica
 
Data Governance_Notes.pptx
Data Governance_Notes.pptxData Governance_Notes.pptx
Data Governance_Notes.pptxVivekDubley
 
Oracle Database Overview
Oracle Database OverviewOracle Database Overview
Oracle Database Overviewhonglee71
 
Enabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data VirtualizationEnabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
 
Business Intelligence Data Warehouse System
Business Intelligence Data Warehouse SystemBusiness Intelligence Data Warehouse System
Business Intelligence Data Warehouse SystemKiran kumar
 
Data warehouse
Data warehouseData warehouse
Data warehouseMR Z
 
Optimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxOptimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxIDERA Software
 
DI&A Slides: Data Lake vs. Data Warehouse
DI&A Slides: Data Lake vs. Data WarehouseDI&A Slides: Data Lake vs. Data Warehouse
DI&A Slides: Data Lake vs. Data WarehouseDATAVERSITY
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingDunn Solutions Group
 
Azure Data Factory Data Flow Performance Tuning 101
Azure Data Factory Data Flow Performance Tuning 101Azure Data Factory Data Flow Performance Tuning 101
Azure Data Factory Data Flow Performance Tuning 101Mark Kromer
 
Database performance tuning and query optimization
Database performance tuning and query optimizationDatabase performance tuning and query optimization
Database performance tuning and query optimizationUsman Tariq
 
Data Modeling is Data Governance
Data Modeling is Data GovernanceData Modeling is Data Governance
Data Modeling is Data GovernanceDATAVERSITY
 
Big Data & Analytics Architecture
Big Data & Analytics ArchitectureBig Data & Analytics Architecture
Big Data & Analytics ArchitectureArvind Sathi
 
Gathering Business Requirements for Data Warehouses
Gathering Business Requirements for Data WarehousesGathering Business Requirements for Data Warehouses
Gathering Business Requirements for Data WarehousesDavid Walker
 
Chapter 7: Data Security Management
Chapter 7: Data Security ManagementChapter 7: Data Security Management
Chapter 7: Data Security ManagementAhmed Alorage
 

What's hot (20)

3 Steps for Breaking Down Data & Analytic Silos
3 Steps for Breaking Down Data & Analytic Silos 3 Steps for Breaking Down Data & Analytic Silos
3 Steps for Breaking Down Data & Analytic Silos
 
Tips & tricks to drive effective Master Data Management & ERP harmonization
Tips & tricks to drive effective Master Data Management & ERP harmonizationTips & tricks to drive effective Master Data Management & ERP harmonization
Tips & tricks to drive effective Master Data Management & ERP harmonization
 
Big Data Evolution
Big Data EvolutionBig Data Evolution
Big Data Evolution
 
Data Governance_Notes.pptx
Data Governance_Notes.pptxData Governance_Notes.pptx
Data Governance_Notes.pptx
 
Oracle Database Overview
Oracle Database OverviewOracle Database Overview
Oracle Database Overview
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
Enabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data VirtualizationEnabling a Data Mesh Architecture with Data Virtualization
Enabling a Data Mesh Architecture with Data Virtualization
 
Business Intelligence Data Warehouse System
Business Intelligence Data Warehouse SystemBusiness Intelligence Data Warehouse System
Business Intelligence Data Warehouse System
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
Optimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptxOptimize the performance, cost, and value of databases.pptx
Optimize the performance, cost, and value of databases.pptx
 
DI&A Slides: Data Lake vs. Data Warehouse
DI&A Slides: Data Lake vs. Data WarehouseDI&A Slides: Data Lake vs. Data Warehouse
DI&A Slides: Data Lake vs. Data Warehouse
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional Modeling
 
Azure Data Factory Data Flow Performance Tuning 101
Azure Data Factory Data Flow Performance Tuning 101Azure Data Factory Data Flow Performance Tuning 101
Azure Data Factory Data Flow Performance Tuning 101
 
Database performance tuning and query optimization
Database performance tuning and query optimizationDatabase performance tuning and query optimization
Database performance tuning and query optimization
 
Data Modeling is Data Governance
Data Modeling is Data GovernanceData Modeling is Data Governance
Data Modeling is Data Governance
 
From Data Warehouse to Lakehouse
From Data Warehouse to LakehouseFrom Data Warehouse to Lakehouse
From Data Warehouse to Lakehouse
 
UML for Data Architects
UML for Data ArchitectsUML for Data Architects
UML for Data Architects
 
Big Data & Analytics Architecture
Big Data & Analytics ArchitectureBig Data & Analytics Architecture
Big Data & Analytics Architecture
 
Gathering Business Requirements for Data Warehouses
Gathering Business Requirements for Data WarehousesGathering Business Requirements for Data Warehouses
Gathering Business Requirements for Data Warehouses
 
Chapter 7: Data Security Management
Chapter 7: Data Security ManagementChapter 7: Data Security Management
Chapter 7: Data Security Management
 

Similar to Olap Cube Design

Using Continuous Etl With Real Time Queries To Eliminate My Sql Bottlenecks
Using Continuous Etl With Real Time Queries To Eliminate My Sql BottlenecksUsing Continuous Etl With Real Time Queries To Eliminate My Sql Bottlenecks
Using Continuous Etl With Real Time Queries To Eliminate My Sql BottlenecksMySQLConference
 
The Yahoo Open Stack
The Yahoo Open StackThe Yahoo Open Stack
The Yahoo Open StackMegan Eskey
 
Gmr Highload Presentation Revised
Gmr Highload Presentation RevisedGmr Highload Presentation Revised
Gmr Highload Presentation RevisedOntico
 
Gmr Highload Presentation
Gmr Highload PresentationGmr Highload Presentation
Gmr Highload PresentationOntico
 
Enterprise PHP Development (Dutch PHP Conference 2008)
Enterprise PHP Development (Dutch PHP Conference 2008)Enterprise PHP Development (Dutch PHP Conference 2008)
Enterprise PHP Development (Dutch PHP Conference 2008)Ivo Jansch
 
Architecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyArchitecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyMark Ginnebaugh
 
Bcm Best Practise & Local Challenges
Bcm Best Practise & Local ChallengesBcm Best Practise & Local Challenges
Bcm Best Practise & Local Challengesbudzeg
 
Internationalisierung Barcampbodensee Share
Internationalisierung Barcampbodensee ShareInternationalisierung Barcampbodensee Share
Internationalisierung Barcampbodensee Sharekindo
 

Similar to Olap Cube Design (10)

Using Continuous Etl With Real Time Queries To Eliminate My Sql Bottlenecks
Using Continuous Etl With Real Time Queries To Eliminate My Sql BottlenecksUsing Continuous Etl With Real Time Queries To Eliminate My Sql Bottlenecks
Using Continuous Etl With Real Time Queries To Eliminate My Sql Bottlenecks
 
The Yahoo Open Stack
The Yahoo Open StackThe Yahoo Open Stack
The Yahoo Open Stack
 
Gmr Highload Presentation Revised
Gmr Highload Presentation RevisedGmr Highload Presentation Revised
Gmr Highload Presentation Revised
 
Gmr Highload Presentation
Gmr Highload PresentationGmr Highload Presentation
Gmr Highload Presentation
 
Enterprise PHP Development (Dutch PHP Conference 2008)
Enterprise PHP Development (Dutch PHP Conference 2008)Enterprise PHP Development (Dutch PHP Conference 2008)
Enterprise PHP Development (Dutch PHP Conference 2008)
 
Architecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case StudyArchitecting a Data Warehouse: A Case Study
Architecting a Data Warehouse: A Case Study
 
Bcm Best Practise & Local Challenges
Bcm Best Practise & Local ChallengesBcm Best Practise & Local Challenges
Bcm Best Practise & Local Challenges
 
Groovy Finance
Groovy FinanceGroovy Finance
Groovy Finance
 
Internationalisierung Barcampbodensee Share
Internationalisierung Barcampbodensee ShareInternationalisierung Barcampbodensee Share
Internationalisierung Barcampbodensee Share
 
From Work To Word
From Work To WordFrom Work To Word
From Work To Word
 

Recently uploaded

RAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIRAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIUdaiappa Ramachandran
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfDaniel Santiago Silva Capera
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024SkyPlanner
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024D Cloud Solutions
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6DianaGray10
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
GenAI and AI GCC State of AI_Object Automation Inc
GenAI and AI GCC State of AI_Object Automation IncGenAI and AI GCC State of AI_Object Automation Inc
GenAI and AI GCC State of AI_Object Automation IncObject Automation
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfAijun Zhang
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintMahmoud Rabie
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...Aggregage
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfAnna Loughnan Colquhoun
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Websitedgelyza
 

Recently uploaded (20)

RAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AIRAG Patterns and Vector Search in Generative AI
RAG Patterns and Vector Search in Generative AI
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024Salesforce Miami User Group Event - 1st Quarter 2024
Salesforce Miami User Group Event - 1st Quarter 2024
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024Artificial Intelligence & SEO Trends for 2024
Artificial Intelligence & SEO Trends for 2024
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6UiPath Studio Web workshop series - Day 6
UiPath Studio Web workshop series - Day 6
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
GenAI and AI GCC State of AI_Object Automation Inc
GenAI and AI GCC State of AI_Object Automation IncGenAI and AI GCC State of AI_Object Automation Inc
GenAI and AI GCC State of AI_Object Automation Inc
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
Machine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdfMachine Learning Model Validation (Aijun Zhang 2024).pdf
Machine Learning Model Validation (Aijun Zhang 2024).pdf
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership Blueprint
 
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
The Data Metaverse: Unpacking the Roles, Use Cases, and Tech Trends in Data a...
 
Spring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdfSpring24-Release Overview - Wellingtion User Group-1.pdf
Spring24-Release Overview - Wellingtion User Group-1.pdf
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
COMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a WebsiteCOMPUTER 10 Lesson 8 - Building a Website
COMPUTER 10 Lesson 8 - Building a Website
 

Olap Cube Design

  • 1. CUBE DESIGN BY HANNES MEYER OnLine Analytical Processing OLAP
  • 2. Agenda   What are cubes?   Multidimensionality   Storage of multidimensional data.   Hierarchies   Operations   Demo
  • 3. What are cubes?   Multi-dimensional representation of data
  • 4. What are cubes (cont.)?   syn: Hypercube, multidimensional database (MDB), olap cube   Cubes can have more than three dimensions
  • 5. Fact Tables   Contain numerical measurements of a certain business process.   E.g. $12.000 sales in NY store on 12-01-08   Additionally foreign keys to different dimension tables   E.g. further store/sales person information   Center in star schema
  • 6. Dimension Tables   Contain attributes by which data can be grouped   e.g. city/region of store, product category   Linked to the fact table via their primary keys   Slowly changing dimensions: dimensions which change over time. Can be dealt with in 3 ways:   Overwritingold values   Add new row to table, distinguish records by versioning   Add new column (attribute) to existing row
  • 7. Data Storage Models   relational databases (ROLAP)   Datain tables   Summaries stored in precalculated tables   multi-dimensional databases (MOLAP)   Data in multidimensional arrays   + Less disk space   + Better Performance (precalculated aggregates)   - Time to aggregate & calculate   - Updates require recalculation   Hybrid (HOLAP)
  • 8. Hierarchies   Grouping of dimensions   e.g. country -> sales   e.g. month -> semester - region -> state -> city > quartal -> year -> store   2008   Germany   H1 2008   Southern germany   Q1 2008   BaWue   Jan 2008   Stuttgart   Store A   Feb 2008   Store B   March 2008   Q2 2008 …   Bavaria   Munich   H2 2008 …   Store A B C
  • 9. Operations: Slice   Slicing is the process of retrieving a block of data from a cube by filtering on one dimension
  • 10. Operations: Dice   Dicingis the process of retrieving a block of data from a cube by filtering on all dimensions
  • 11. Operations: Drill Up/ Down   Drilling up: Presenting data at a higher level on the hierarchy e.g. Store -> Region   Drilling Down: Presenting data at a lower level on the hierarchy Region -> Store
  • 12. Building the cube in SSAS   Preconditions   Connecting datasources   Defining views   Selecting dimensions   Define fact & dimension tables & time dimension   Select measures   Deploy & query the cube    Demo