SSAS Introduction

1. Microsoft® SQL
Server 2008
Analysis Services

2. Microsoft® SQL Server 2008 Analysis Services provides
unified, fully integrated views of your business data to
support online analytical processing (OLAP), key
performance indicator (KPI) scorecards, and powerful data
mining capabilities. It provides reliable business decision
support solutions
 SQL Server 2008 Analysis Services (SSAS) provides
o Unified and integrated view of all your business data
o Reporting, online analytical processing (OLAP)
analysis
o Key Performance Indicator (KPI) scorecards
o Data mining

3. Architecture

4. ARCHITECTURE

5. Analysis Manager
 Application for Database Administration
 Snap-In to MMC
 Decision Support Objects
Analysis ServerDSO
Analysis Manager Custom Administration Interface

6. Analysis Server LimitsItems Limits
Databases per server Unlimited
Cubes per database Unlimited
Cubes per virtual cube 64
Dimensions per cube 128
Measures per cube 1,024
Calculated members per cube 65,535
Levels per dimension 64
Partitions per cube Unlimited

7. Analysis Services Objects

8. Databases and Data Sources
 Database contains other Analysis Services
objects
 Data sources define where Analysis Services
gets the data to populate dimensions and cubes
o OLE DB providers
o OLE DB for ODBC
o MSSQLServerOLAPService service account

9. Data Source Views
 Data source views let you define a subset of the
data that populates a large data warehouse.
 They let you define a homogenous schema
based on heterogeneous data sources or
subsets of data sources.
 Because data source views represent an
isolated schema, you can add any required
annotations without affecting the schemas of the
underlying data sources
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10. A data source view contains the following items:
 A name and a description.
o A definition of any subset of the schema
o Table names.
o Column names.
o Data types.
o Nullability.
o Column lengths.
o Primary keys.
o Primary key - foreign key relationships.
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Data Source Views

11.  Annotations to the schema from the underlying
data sources, including the following:
o Friendly names for tables, views, and columns.
Named queries that return columns from one or more
data sources
o Named calculations that return columns from a data
source (that show as columns in tables or views).
o Logical primary keys
o Logical primary key - foreign key relationships
between tables, views, and named queries.
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Data Source Views

12. Cubes
 Multidimensional structure containing
dimensions and measures
 Cells (the intersection between dimensions)
contain the measure values

13. Analysis Services
 CUBE

14. Dimensions
 Organized hierarchies of categories, levels, and
members
 Used to “slice” and query within a cube
 Based on an underlying dimension table

15. Measures
 Contain the data users are interested in
 Created using an aggregation function
 Based on an underlying fact table

16. Roles
 Defines end-user access to objects
 Contains a list of Windows NT/2000 users
and/or groups
 Defines the type and scope of access
o Database
o Cube
o Dimension
o Cell
o Mining model

17. Mining Models
 Groupings and predictive analysis based on
relational or OLAP data
 Interprets data based on statistical information
referred to as cases

18. Repository
 Database containing meta-data about the
objects
o Should be migrated to SQL Server
 Data folder to hold multidimensional structures
o Location defined during installation, but can be
modified
o Should be on an NTFS partition/volume

19. Dimensions

20. Varieties of Dimensions
 Regular
 Virtual
o Based on member properties
o Does not have stored aggregations
 Parent-child
o Based on lineage relationship between dimension
members
o Built using member and parent key values

21. Levels and Members
 (All) level and the All member
 Levels
o Correspond (loosely) to column names
 Members
o Contain the actual dimension data
o Have names and keys

22. Levels and Members
 Properties
o Level
o Member
 Custom rollup operators
o Use unary operators to determine rollups
 Custom rollup and member formulas
o Use MDX expressions to determine rollups and/or
to determine member values
 Member groups
o Automatically group large levels

23. Dimension Characteristics
 Changing
o Handles dimension changes without fully
reprocessing the dimension
o Virtual, parent-child, and ROLAP
 Dependent
o Members depend on another dimension
o Advantageous when cross product of two dimensions
results in large percentage of combinations that
cannot exist

24. Dimension Characteristics
 Balanced vs. unbalanced
o Hierarchy branches descend to the same or different
levels
o Unbalanced supported only by parent-child
 Ragged
o Members have parents not in the level immediately
above them
o Supported in regular and parent-child
 Multiple hierarchies

25. Dimension Characteristics
 Storage mode
o MOLAP
o ROLAP
 Write-enabled
o Supported only by parent-child
o Allows end-users (and administrators)
o Members can be changed, moved, added, deleted;
member properties can be updated
o Changes recorded directly in the underlying
dimension table

26. Measures

27. Measures
 Define the numbers that end users see
 Use aggregation functions
o Sum
o Count
o Min
o Max
o Distinct Count
 Display formats

28. Measures
 Calculated measures (or members)
o Use MDX expressions to provide calculations
o Never stored as aggregation data
o Can include Excel and VBA functions
o Have solve orders for dependencies
o Include display attributes (beyond formats)

29. Cubes

30. Varieties of Cubes
 Regular
 Linked
o Allow for reuse of cubes across servers
o Local caching helps reduce query loads
 Distributed
o Cubes can be broken down into partitions
o Partitions can be spread across servers
o Queries then get distributed (scalability!)

31. Varieties of Cubes
 Virtual
o Like views in a relational database
o Simplify and/or combine cubes together
o Can be used as a security mechanism
 Local
o Used by PivotTable Service to provide off-line access
to parts of a cube
 Real-time
o Combination of Analysis Services and SQL Server
can provide real-time capabilities

32. Cube Characteristics
 Storage mode
o MOLAP
 Data and aggregations compressed and stored
o ROLAP
 Data and aggregations stored in relational source
o HOLAP
 Aggregations stored, data remains relational
 Aggregation level
o Wizard to decide how much to aggregate
o Optimization wizard to redo based on usage

33. Cube Characteristics
 Partitioning
o Allows you to split cubes for scalability,
manageability, etc.
o Partitions defined based on dimensions
 Write-enabled
o Allows users to rewrite cube contents
o Changed data stored in a “write-back” partition as
difference values
o Non-atomic cell updates can be made if client
application can distribute changes

34. Security

35. Security
 Server authentication
o Direct connections (OLE DB for OLAP)
o Http connections via special ASP/DLL
 Roles
o Specify users and groups as members
o Have associated security rights
o Database, cube, and mining model roles
 Dimension security
 Cell-level security

36. Commands

37. Commands
 Actions
o Provide mechanisms to do more than just look at the
data
o Associated with dimensions, levels, members, or cells
 Calculated members
o Most often defined used for new measures
o Can also be used to define new members in any
dimension

38. Commands
 Named sets
o Allow you to create sets of members within a
dimension for analysis purposes
o Use MDX expressions to define membership
 Drill-through
o Give access to underlying relational data
o Can be used to provide access to lower levels of
detail than the cube includes

39. MDX

40. MDX (Multidimensional
Expressions)
 Query language for a cube
 Similar but different from SQL
 Handles DML as well as DDL
 Basic format is:

41. MDX
 Members, tuples, and sets
 Axis dimensions
o Columns, rows, pages, sections, chapters
o Axis(n)
 Slicer dimensions
o Where (<tuple definition>)
 MDX functions

42. Extensible Markup Language
for Analysis - XMLA
 XML/A is a Data Access Protocol Extending BI
to Microsoft .NET Platform
 Supports Exchange of Analytical Data Between
Clients and Servers
o Available on Any Device or Platform
o Using Any Programming Language