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

667 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
667
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
14
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

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

  1. 1. Chris Testa-O’Neillchris@coeo.com@ctesta_oneill
  2. 2. Introducing Analysis ServicesGetting the dataWorking with Dimensions Hierarchies Attribute RelationshipsCreating the Cube Working with Measure and Measures Group Partitions and aggregation designBrowsing the cube
  3. 3. Full exploration of cube propertiesUse of additional SSAS components Calculations (MDX) Key Performance Indicators Translations PerspectivesAdministration and MaintenanceSecurity
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. Cube wizard Existing Tables Existing Dimension Empty CubeWizard Capabilities differ from SQL Server2005 and 2008/2012
  11. 11. 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
  12. 12. 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
  13. 13. Cube Browser in BIDSMicrosoft ExcelSQL Server Reporting ServicesPerformancePointSharepoint
  14. 14. Chris Testa-O’NeillChris@Coeo.com@ctesta_oneill

×