SQL DAY 2012 | DEV Track | Session 8 - Getting Dimension with Data by C.Tecta-O'Neill
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SQL DAY 2012 | DEV Track | Session 8 - Getting Dimension with Data by C.Tecta-O'Neill

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SQL DAY 2012 | DEV Track | Session 8 - Getting Dimension with Data by C.Tecta-O'Neill SQL DAY 2012 | DEV Track | Session 8 - Getting Dimension with Data by C.Tecta-O'Neill Presentation Transcript

  • Chris Testa-O’Neillchris@coeo.com@ctesta_oneill
  • Introducing Analysis ServicesGetting the dataWorking with Dimensions Hierarchies Attribute RelationshipsCreating the Cube Working with Measure and Measures Group Partitions and aggregation designBrowsing the cube
  • Full exploration of cube propertiesUse of additional SSAS components Calculations (MDX) Key Performance Indicators Translations PerspectivesAdministration and MaintenanceSecurity View slide
  • Components of SQL Server used for querying andanalysing dataMulti-Dimensional is very much alive. Tabular providesnew opportunitiesTypically uses a data warehouse as it source data Dimension tables Fact tablesCore object is a cube storing detailed and preaggregated dataNumber of clients can be used to retrieve cube data View slide
  • Relational Reporting (OLTP) Use of Normalised tables to query data Can be slow as number of tables used increases or a requirement for aggregate data – Tabular addresses this.Online Analytical Processing (OLAP) Database type that stores one or more cubes that stores data in a central repository for reporting purposesData Mining Uses OLAP database to explore trands and patterns in the data
  • Data Sources provides the connectioninformation. Server Name Authentication DatabaseData Source Views allows you to define a subsetof data from the data source Data Source Wizard Data Source Designer
  • Provide contextual information for data in a cubeTypically maps to the data in a dimension table of adata warehouseDimensions form the cube axisCan selectively add attributes to meet businessrequirementsKey properties include Key Columns Name Colums Order byTime Dimensions
  • Improves the readability of large dimensiondataAdds levels to dimension data so users candrill down into the dataTypes of Hierarchies include Balanced (Natural) Hierarchies Parent Child Ragged Hierarchies
  • Defines relationships that exists between attributes ina dimensionBy default, all attributes have a relationship to the keyattribute in a star schemaModifying the default behaviour can Result in more effective aggregation designs Increases query performance Reduce memory requirements for processing dimensionsUse Attribute relationships tab in SQL Server 2008Use the Dimension tab in SQL Server 2005
  • Cube wizard Existing Tables Existing Dimension Empty CubeWizard Capabilities differ from SQL Server2005 and 2008/2012
  • Measures are the business metrics stored within thecubeTypically map to measures in a Fact table in a datawarehouseCan create derived measure using MDX expressionsAggregate property in Measures has additivity issuesStorage Mode property: MOLAP, ROLAP and HOLAPMeasures Group typically map to fact tablesMeasures Groups group measures togetherMeasures Group maps the measure to dimensions
  • Enterprise EditionSpread the data across multiple physical disks Improved query performance Reduced cube processing timeDetermine the storage mode on a per partition basisDesign aggregation Enables you to set aggregations based on disk and performance limit Usage Based Optimisation a better method
  • Cube Browser in BIDSMicrosoft ExcelSQL Server Reporting ServicesPerformancePointSharepoint
  • Chris Testa-O’NeillChris@Coeo.com@ctesta_oneill