Introduction to Microsoft SQL Server 2008 R2 Analysis Service

SQL Server 2008 R2

Understanding SQL Server Analysis
Services

http://techmaster.vn

SQL Server 2008 R2 BI Technologies


Contents
• Understand the Analysis Services 2008 R2
• Understand the OLAP and OLAP database
• Understand the dimensional OLAP
• Understand the multidimensional data
analysis
• Understand dimensional data warehouse

SQL Server 2008 R2 BI Structure
Reporting and Visualization Tools (Dashboard, KPI,
Presentation Layer
Scorecard,…)

Turn data into information (analysis)
Analytical Layer
Multidimensional OLAP Database

Data Storage and Retrieval Layer Data Warehouse in RDBMS

1. Extract the data from the multiple sources
Data Transformation Layer 2. Modify the data to consistent
3. Load the data into Data Storage system

Data Source Layer Text, MS Excel, MS Access, MS SQL, Oracle,…| External Sources


Microsoft Business Intelligence Platform

Analytic Scorecards, Analytics, Planning
Applications (PerformancePoint Service)

Portal
(SharePoint)
Data Delivery Report Builder End-user Analysis
SSRS (Excel)

Integrate Analyze Report
(SQL Integration Services) (SQL Analysis Services) (SQL Reporting Services)
Infrastructure
Platform Data Warehouse, Data Marts,
Operational Data
(SQL Server 2008 R2)

Office SQL

Analysis Challenges
How Do You Deal With:

Data stored in The cost of developing The costs of
multiple data sources analytical solutions learning new tools

Deploy for today’s
problem but scale ‘Real-Time’ data
over time access

Multiple Users, Diverse analytical Inconsistent data
Multiple Tools needs


Analysis Services 2008 R2
Design Scalable Solutions
Productivity enhancing designers
Scalable Infrastructure
Superior Performance

Extend Beyond OLAP
Unified meta data model
Central KPI manageability
Predictive Analysis

Deliver Pervasive Insight
Optimized Office interoperability
Rich partner extensibility
Open, embeddable architecture

Design Scalable Solutions
Productivity Enhancing Designers
Optimized design experience
Best Practice Design Alerts
Project Lifecycle support

Scalable Infrastructure
Heterogeneous data Integration
Robust Scale-Out Configuration
Advanced Resource Monitoring
User-differentiated perspectives

Superior Performance
Market leading MOLAP Engine
Near real-time data access
Subspace computation optimization
MOLAP enabled write-back


Extend Beyond OLAP
Unified Metadata Model
One consolidated business view
Integrated relational & OLAP analysis
Business information modeling
Time- and financial intelligence

Central KPI Manageability
Server based KPI framework
Centrally managed repository
Pervasive end-user accessibility

Predictive Analytics
Complete data mining framework
Embeddable viewers
Predictive capabilities available to
everyone through Microsoft Office


Predictive Analysis
Bring Data Mining to the Masses through Microsoft Office

Enable easy to use predictive
analysis
At every desktop
For every information worker
Through three powerful add-ins
to Microsoft Office
Predictive capabilities readily
available for business users in Excel
Data mining client for building data
mining models in Excel
Data mining templates for project
visualization in Visio

“What Microsoft has done is to make data mining available on the desktop to
everyone” (David Norris, Associate Analyst, Bloor Research).

Deliver Pervasive Insight
Optimized Office Interoperability
Massive data analysis for everyone with
PowerPivot for Excel 2010
Team Collaboration through PowerPivot for
SharePoint 2010
Corporate performance management
through PerformancePoint Services 2010

Rich Partner Ecosystem Extensibility
Vertically specialized solutions
Packaged applications
API support from all major BI vendors

Open, embeddable architecture
Open API’s and XML/A based protocols
Native web service functionality
Close loop analysis


Office 2010 Integration
Excel 2010
Great cross product investments optimizing
Excel 2010 as analytical client for Analysis
Services
Enhancements around local cubes
Significant performance and functionality
investments
Data Mining Add-Ins for predictive analysis
PowerPivot for massive data analysis on
the desktop

PerformancePoint Services 2010
Great cross product investments for thin
analytic client for Analysis Services
Rich web capabilities for data exploration.
Guided and contextual analysis through
integrated dashboards
Predictive analytics by integrating with SQL
Server Data Mining

Understanding SQL Server Analysis Services

UNDERSTANDING OLAP


What is OLAP

Online Analytical • Benefits
Processing
– Consistently fast response
Online Transaction
Processing 1993. – Metadata-based queries
1985. OLAP – Spreadsheet-style formulas
OLTP


Consistently Fast Response
• Calculating and storing aggregate values and
the results of formulas when a cube is loaded
(calculation in advance)
• Aggregate tables can be created to provide
fast query results


Metadata-Based Queries
SQL Query
• SQL is suitable for SELECT
transaction system [Store].[Store Country].[Canada].[Vancouver]
ON COLUMNS,
not for reporting [Product].[All Products].[Clothing].[Mittens]
applications ON ROWS
FROM [Sales]

• Query language for WHERE ([Measures].[Unit Sales],
[Date].[2010].[February])
OLAP data source MDX Query

– Multidimensional SELECT SUM(Sales.[Unit Sales])

expression
FROM (Sales INNER JOIN Stores
ON Sales.StoreID = Stores.StoreID)
INNER JOIN Products
– MDX ON Sales.ProductID = Products.ProductID
WHERE Stores.StoreCity = 'Vancouver'
AND Products.ProductName = 'Mittens'
AND Sales.SaleDate BETWEEN '01-02-2010' AND
'28-02-2010'

Spreadsheet-Style Formulas
• MDX formulas use named references
– C14/D14 (Spreadsheet) | [Actual]/[Budget] (MDX)
• MDX formulas are easy to manage
• MDX formulas are multidimensional
– Spreadsheet is two dimensional
• MDX formulas take advantage of metadata (its
relationship)
– There is no relationship in cells on the sheet.



MULTIDIMENSIONAL DATA ANALYSIS


Measure and Metadata
• Measure: A summarizable numerical value
– Sales Dollars, Shipment Units,...
• Metadata: Data about data
– Label, Order by,...
Metadata

Units Sold
70 70
Measure
Adventure Works Sales Adventure Works Sales


Unit sold by Product and Month report

Product Jan 2011 Feb 2011 Mar 2011 Apr 2011
Mountain-500 Black, 40 1 3 1 2
Mountain-500 Black, 44 2 1
Mountain-500 Black, 48 1 2 1
Mountain-500 Silver, 40 1 2 1
Mountain-500 Silver, 48 2
Road-750 Black, 44 10 7
Road-750 Black, 48 5 9
Hitch Rack 1 6 6 3


Grouping/Aggregating/Attribute/Member
• Grouping – Aggregating: is the
Product Model Color Size way humans deal with too much
Mountain-500 Black, 40 Mountain- Black 40 detail
500
Mountain-500 Black, 44 Mountain- Black 44 – Ex: group Products by model,
500 subcategory, and category groups

Attribute: Product (Key), Model,
Mountain-500 Black, 48 Mountain- Black 48
500 •
Mountain-500 Silver, 40 Mountain- Silver 40
Color, Size
500
• Member
500
– Model, Mountain-500, Road-
750…
500
Road-750 Black, 44 Road-750 Black 44 – Color: Black, Silver
Road-750 Black, 48 Road-750 Black 48
Hitch Rack Hitch Rack – Size: 40, 44, 48
Product Attributes

Hierarchy: Model  Product

Jan 2011 Feb 2011 Mar 2011 Apr 2011
Mountain-500 3 8 6 6
Mountain-500 Black, 40 1 3 1 2
Mountain-500 Black, 44 2 1
Mountain-500 Black, 48 1 2 1
Mountain-500 Silver, 48 2
Road-750 15 16
Road-750 Black, 44 10 7
Road-750 Black, 48 5 9
Hitch Rack 1 6 6 3
Hitch Rack 1 6 6 3
Units Sold by Model, Product and Month


Hierarchy
• Hierarchy is created by
arranging related
attributes into levels

• Hierarchy level: 2, 3,…n

• Hierarchy type:
– Balance (Date)

– Unbalance
(Organization)


Dimensions
Jan Feb Mar Apr
2011 2011 2011 2011
Mountain- 3 8 6 6
500
Road-750 15 16
Hitch Rack 1 6 6 3
Units Sold by Model and Month

• Attribute:
– Model (3)
– Month (4)
• Potential number of values: 12 = 3x4


Dimensions
Jan 2011 Feb 2011 Mar 2011 Apr 2011
Units $ Units $ Units $ Units $
WA Hitch Rack 4 $480 3 $360 2 $240
Mountain- 2 $1.105 6 $3.256 5 $2.775 5 $2.750
500
Road-750 9 $4.860 10 $5.400
OR Hitch Rack 2 $240 3 $360 1 $120
Mountain- 1 $120 2 $1.105 1 $540 1 $540
500
Road-750 1 $565 6 $3.240 6 $3.240

• Attribute:
– State (2), Model (3), Month (4), Measure (2: Units sold, Sales dollars)
• Potential number of values: 2x3x4x2 = 48

Dimensions
• Examples:
– State attribute belongs to the Geography
dimension
– Model attribute belongs to the Product
dimension
– Month attribute belongs to the Date dimension
– Units sold and Sale Dollars belongs to the
Measure dimension

Dimensions
• The independent attributes and hierarchies are the
dimension
• A dimension may contain more than one attributes
– Ex: Product dimension contain Color and Size attribute
• Dimension also contain hierarchies
– Ex: Product by Model hierarchy is composed of attributes
contained in the Product dimension, so the hierarchy also
belongs in the Product dimension
• Measure dimension are displayed on columns


DIMENSIONAL DATA WAREHOUSE


Dimension Data Warehouse
• Dimension Data Warehouse is the data
storage and retrieval layer of BI system
• In dimension data warehouse:
– Dimension are stored in dimension tables
– Measure are called facts and are stored in fact
tables


Fact Table
• Fact table: table that stores the detailed values for measures
• Key Column: State, Product, Month
• Fact Column: UnitsSold, SalesDollars

State Product Month UnitsSol SalesDollar
d s
OR Hitch Rack Jan 2011 1 $120.00
OR Mountain-500 Silver, 40 Jan 2011 1 $565.00
OR Mountain-500 Silver, 48 Jan 2011 1 $552.50
WA Mountain-500 Silver, 48 Jan 2011 1 $552.50
OR Hitch Rack Feb 2011 2 $240.00
WA Hitch Rack Feb 2011 4 $480.00
FactSales table

Fact Table
• The value in the key columns relate the facts
in the fact table row to a row in each
dimension table
• Fact table may have other type of column for
reference purposes
• Fact table might contain one or more
measure columns

Fact Table
• The level of detail stored in a fact table is
called granularity
• The dimensions that a fact table is related to
is called dimensionality of the fact table
• Facts that have different granularity of
different dimensionality must be stored in
separate fact tables

Fact table: Dimension key
• Actually a fact table almost
always uses an integer, called
a dimension key, for each State Product Month UnitsSold SalesDollars
dimension member 1 483 201101 1 120.00
1 591 201101 1 565.00
• There must be a dimension 1 594 201101 1 552.50
table for each dimension key 2 594 201101 1 552.50
in a fact table 1 483 201102 2 240.00
2 483 201102 4 480.00

FactSales table using Dimension key


Dimension Table
• A dimension table contain one row
for each member of the key
attribute of the dimension ProductKey Product
596 Mountain-500 Black, 40
• The key attribute has two column: 598 Mountain-500 Black, 44
599 Mountain-500 Black, 48
– Integer dimension key (PK)
591 Mountain-500 Silver, 40
– Attribute label 593 Mountain-500 Silver, 44
594 Mountain-500 Silver, 48
• A dimension table may contain 604 Road-750 Black, 44
other columns for other attributes 605 Road-750 Black, 48
of the dimension 483 Hitch Rack

DimProduct Dimension Table


Dimension table
ProductKey Product SubCategory Category Color Size
596 Mountain-500 Black, 40 Mountain Bikes Bikes Black 40
591 Mountain-500 Silver, 40 Mountain Bikes Bikes Silver 40
604 Road-750 Black, 44 Road Bikes Bikes Black 44
605 Road-750 Black, 48 Road Bikes Bikes Black 48
483 Hitch Rack Bike Racks Accessories
DimProduct Dimension Table

Aggregatable and Aggregate
• Aggregatable: Attributes that can be used to create groups
• Non aggregatable attributes are referred to as member
properties
– Ex: List Price, Telephone Number, Street Address…
• Aggregate: Summary value in the group of aggregatable
• Example:
– Aggregatable: Category, Color…
– Aggregate: Number of Units Sold for each Category



MULTIDIMENSIONAL OLAP


Multidimensional OLAP
• Multidimensional OLAP database resides
between the data storage and retrieval layer
and the presentation layer
• It converts the relation data warehouse data
into a fully implemented dimensional model
for creating analytical reports and data
visualizations

Measure Group and Cube
• Measure group corresponds to a single fact table

• Measure group may contains data for single level of detail and
aggregated data for all higher levels of detail

• Cube: Combination of several related measure groups and a
set of dimensions
State Product Date Units Sold Sales Amount
All All All 70 31.305
WA All All 46 21.235
WA Bikes All 37 20.115
WA Road Bikes All 19 10.260


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Introduction to Microsoft SQL Server 2008 R2 Analysis Service

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