Prem ShankerSr. Software EngineerCredit Suisse

Introduction to Introduction to Business IntelligenceBusiness Intelligence

Goals

• Learn about the concept of Data Warehousing and what BIDS offer.

• Learn about how to design and implement a Data Warehouse Dimensional database.

• Learn about what is a cube.• Learn about the SQL Server Analysis Services

Architecture• Learn what is new in Analysis Services 2008• Learn about what is a MDX Language.

What BIDS can do?

SQL ServerSQL ServerDataData

WarehouseWarehouse

SourceSourceSystems/OLTPSystems/OLTP

ClientsClients

Design theDesign the Populate Populate CreateCreate QueryQuery Data Warehouse Data Warehouse Data Warehouse Data Warehouse OLAP CubesOLAP Cubes DataData

11 33 44

Query ToolsQuery ToolsReportingReportingAnalysisAnalysis

22

Cubes Cubes

Analysis Analysis ServicesServices

Data Warehouse

• Table and Cube• Star Schema and Snowflake Schema• Fact Table and Dimension Table

Table vs Cube

A simplified example:

Product Region Sales $

Donut East 1

Donut West 2

Milk East 3

Milk West 4

A typical relational A typical relational tabletable

Make it into a cubeMake it into a cube

Data are organized by rows Data are organized by intersections

East West Total

Donut 1 2 3

Milk 3 4 7

Total 4 6 10

Product dimProduct dim

Region dimRegion dimSales tableSales table

The basic ingredients to make a cube

• Two kinds of table in a data warehouse DB1. fact table2. dimension tables.

• Question:1. Which one is a fact table and which one is a

dimension table?

Star Schema

• A Star Schema contains a fact table and one or more dimension tables. 1. A Fact Table: The central fact table store the

numeric fact (measures) such as Sales dollars, Costs, Unit Sales etc.

2. Dimension Tables: They surround the central fact table, and they store descriptive information about the measures

• The shape looks like a Star

Star schema

Snowflake Schema

Review: Data Warehouse Schemas

– The Data Warehouse is either a Star Schema or a Snowflake Schema:

• Fact tables that contain foreign keys and numeric measures• Dimension table contains the data describes the measures.

• The schema is ready for Analysis Services to build a cube.

Analysis Analysis ServerServer

OLEDBOLEDB

ADOMD ADOMD .NET.NET

AMOAMOIISIIS

TCPTCP

HTTPHTTP

XMLAXMLA

ADOMDADOMD

Client Client AppsApps

BIDSBIDS

SSMSSSMS

SSRSSSRS

ExcelExcel

Client Server Architecture

MOSSMOSS

A Logical Cube - Example

ProductProduct

RegionRegion

WestWest

EastEast

SouthSouth NorthNorth

1999 2000 2001 20021999 2000 2001 2002

MilkMilk

DonutDonut

SandwichSandwich

SodaSoda

BeerBeer

NorthNorth

SouthSouth

EastEast

WestWest

Time

The The Sales$ by Sales$ by Soda by Soda by West in Yr West in Yr of 2001of 2001

Tools to connect to Cubes

• SQL Server Management Studio (SSMS)• Business Intelligence Development Studio (BIDS)• Query Analyzer (SSMS) – To write MDX• Excel 2007 – Uses MDX

Physical Cube- BIDS• Analysis Services Database• Unified Dimensional Model• Data Source connection• Data Source View• Dimensions• Cube Creation Wizard

Analysis Services Database

• An Analysis Services database is the top level container for other dependent objects:

• A database includes– Data Source– Data Source View– Cube– Dimension– Security Role

Creating an Analysis Services Database

• You can use one of the following to create a new empty database on an instance of SQL Server 2005 Analysis Services.– SQL Server Management Studio – Business Intelligence Development Studio.

Unified Dimensional Modeling

• Common Name: UDM• New feature Since AS 2005• Combine all Relational Sources in one

single environment. • A single data model, called Unified

Dimensional Model (UDM) over one or more physical data sources

Unified Dimensional Model - Concept

• The user needs to understand the particulars of each technology (e.g. the dialect of SQL used) to generate reports.

• Within one single Analysis Services, you can have more than one data sources to pull the data from.

Data Source Connection

• The data sources of your AS database is your Data Warehouse databases (SQL).

• It defines the connection string and authentication information for a database on an OLE DB data provider.

• You can use the Data Source Wizard to specify one or more data sources (SQLDB) for Analysis Services databases.

The Functions of the Data Sources

• Integrate your Analysis Services databases with the data warehouses

• They are used for the following:– Processing the Cubes and dimensions– Data Retrieval if ROLAP or HOLAP is used as

the storage.– Write Back

Different Storage types of Cube

Data Sources connection to SQL Server

• For SQL Server, you can pick from the following providers:– OLE DB provider for SQL Server– SQL Native Client– .NET Provider/SqlClient Data Provider

– (Avoid using .NET data sources – OLEDB is faster for processing in practices)

Data Source Views• New feature Since AS 2005• A single unified view of the metadata (UDM) from specified

tables and views that the data source defines in the project.

• It hides the physical implementation of the underlying data sources from the reporting users.

• Basic Data Layout for Cubes• Define Data Relationships• Can Leverage Multiple Data Sources• The key to effective cube design• Named Query As Objects – Not only Tables or Views

Demo

Dimension

• All dimensions are based on tables or views in a data source view. • All dimensions are shared since AS 2005• The structure of a dimension is largely driven by the structure of

the underlying dimension table or tables. • The simplest structure is called a star schema, which is where each

dimension is based on a single dimension table that is directly linked to the fact table by a primary key - foreign key relationship.

Dimension Consists of

• A dimension consists of:

–Attributes that describe the entity–User-Defined Hierarchies that

organize dimension members in meaningful ways

• such as Store Name Store City Store State Store Country

Attributes

• New feature since AS 2005• Containers of dimension members• Typically have one-many relationships between

attributes in the same dimension:– City State, – State Country, etc.– All attributes implicitly related to the key

User Defined Hierarchies

• User Defined Hierarchies are created from Attributes

• Tree-like structure

City State Country All• Provide navigation paths in a

cube

Typical Example – Calendar Hierarchy• The Year, Quarter, and Month attributes are

used to construct a hierarchy, named Calendar, in the time dim.

• The relationship between the levels and members of the Calendar dimension (a regular dimension) is shown in the following diagram.

Measure Group

• In a cube, a measure is the set of values, usually numeric, that are based on a column in the fact table in the cube.

• A measure group contains one or more or all the measures from a single fact table. It can’t contain measures from different fact table.

Measure Group Advantages

• Measure groups provide the following advantages:– They can be partitioned and processed separately– They allows to include measures from diff fact

tables.– They are grouped by granularity: Same measure

group same granularity.– Security can be applied to specific measure groups

Cube

• A cube is defined by its measures and dimensions.

Inside a Cube

• Measures and Measure Groups• Dimensions Relationships• Calculations• Actions• Partitions• Perspectives

Demo

Dimension Design

• Different Dimension Relationships– Regular Dimension Relationship– Reference Dimension Relationship– Fact Dimension Relationship– Role Playing Dimension– Parent-Child Hierarchy

Regular Dimension Relationships • A traditional star schema design• The Primary Key in the dimension table joins

directly to Foreign Key in the fact table.

Reference Dimension Relationships

• Snowflake schema• A Reference dimension using columns from

multiple tables, or the dimension table links a dimension that is directly linked to the fact table.

Role Playing DimensionIt is used in a cube more than one time, each

time for a different purpose.• Each role-playing dimension is joined to a fact

table on a different foreign key. • Example, you might add a Time dimension to a cube three times

to track the times that

– products are ordered, – products are shipped,

– Orders are due..

Parent-Child Hierarchy

• A parent-child hierarchy is a hierarchy in a standard dimension that contains a parent attribute. A parent attribute describes a self-join, within the same dimension table.

• Example: Employee Hierarchy An employee is an employee who reports to his/her manager. His manager is an employee as well Employee Key self joins to ParentEmployeeKey

Slowly Changing Dimension

• Some attribute values may change over time.• Two basic techniques:

– Type 1 change– Type 2 change

Slowly Changing Dimension – Type 1• A Type 1 change, is to simply overwrite the old value

with the new one.

Slowly Changing Dimension – Type 2• You create a new dimension row with the new

value and a new surrogate key, and mark the old row or timestamp as no longer in effect The fact table will use the new surrogate key to link new fact measurements

Calculated Member• A Calculated Members is a member of a

dimension or a measure group that is defined based on a MDX expression.

• The value for the member is calculated at runtime. The result values are not stored in the disk.

Calculated Member Properties

Named Set

• A named set is a MDX expression that returns a set of dimension members.

• You can define named sets and save them as part of the cube definition.

• It allows you to reuse the same named set throughout the cube.

• Typical example:– Create a list Top 10 customers based on

Sales– You can reuse same Top 10 customers in diff

queries.

Best practices for Cube Design

• Use integer or numeric for key columns. • Avoid ROLAP storage mode, particular with

custom rollup or unary operators. MOLAP is the fastest storage structure in SSAS.

• Use parent-child dimensions prudently, especially those containing custom rollup and unary operators. No aggregation support in PC dimension.

Best practices for Cube Design (Contd..)

• Use role playing dimensions (e.g. OrderDate, BillDate, ShipDate) - avoids multiple physical copies. If the dimensions are base from the same physical table(s), use role playing dimensions.

What's New (Analysis Services - Multidimensional Database)

• New Attribute Relationship designer. The dimension editor has a new Attribute Relationship designer that makes it easier to browse and modify attribute relationships.

• New AMO Warnings. These new warning messages alert users when they depart from design best practices or make logical errors in database design.

What's New (Analysis Services - Multidimensional Database) • Backup and Restore Improvements • The backup and restore functionality in Analysis Services

has a new storage structure and enhanced performance in all backup and restore scenarios.

• Improved Storage Structure• The new storage structure provides a more robust

repository for the archived database. By using the new storage structure, there is no practical limit to the size of the database file, nor is there a limit to the number of files that a database can have.

• Improved Performance• The new backup and restore functionality achieves

increased performance. Tests on different sized databases and with various numbers of files have shown significant performance improvements.

What's New (Analysis Services - Multidimensional Database) • Dynamic Management Views• Monitoring Connections, Sessions, and

CommandsDiscover_Connections, Discover_Sessions, and Discover_Commands.

• select * from $system.discover_connections

Fetching Data from Cube

• What Is MDX• Testing MDX with the Query Tool in SQL Server

Management Studio• The Basic Elements of an MDX Query

What Is MDX

• An Extension of SQL Syntax That:– Queries and manipulates multidimensional

data in OLAP cubes

– Defines calculations based on information in the cube

– Defines and populates local cubes

• Not a True Extension – – Syntax Deviates Significantly from SQL

Testing MDX with Management Studio

Background

Select

on axis (x),

on axis (y),

on axis (z)

From [cubeName]

ComponentsComponents

BikesBikesClothingClothing

SalesSales CostCostUnitsUnits

Every cell has a Every cell has a name...name...

19991999

20002000

20012001

19981998

19971997

MeasuresMeasures

Time

Time

Products

Products

SalesSales CostCostUnitsUnits

((Products.Bikes, Measures.Units, Time.[2000])Products.Bikes, Measures.Units, Time.[2000])

Every cell has a Every cell has a name...name...

19991999

20002000

20012001

19981998

19971997

MeasuresMeasures

Time

Time

ComponentsComponents

BikesBikesClothingClothing

Products

Products

SalesSales CostCostUnitsUnits

(Products.Bikes(Products.Bikes, , Measures.UnitsMeasures.Units, , Time.[2000])Time.[2000])

(Products.Bikes, Measures.Sales, Time.[1999])(Products.Bikes, Measures.Sales, Time.[1999])

Every cell has a Every cell has a name...name...

19991999

20002000

20012001

19981998

19971997

MeasuresMeasures

Time

Time

ComponentsComponents

BikesBikesClothingClothing

Products

Products

SalesSales CostCostUnitsUnits

What if I only specify this?What if I only specify this?(Products.Bikes, Measures.Units)(Products.Bikes, Measures.Units)

A Cell is referenced by all the A Cell is referenced by all the dimensionsdimensions

19991999

20002000

20012001

19981998

19971997

MeasuresMeasures

Time

Time

ComponentsComponents

BikesBikesClothingClothing

Products

Products

ComputerComputer

PrinterPrinterMonitorMonitor

SalesSales CostCostUnitsUnits

What if I only specify this?What if I only specify this?(Products.Bikes, Measures.Units)(Products.Bikes, Measures.Units)If Time’s default member is [1997]If Time’s default member is [1997]Ans: (Products.Bikes, Measures.Units, Time.[1997])Ans: (Products.Bikes, Measures.Units, Time.[1997])

Default MemberDefault Member

19991999

20002000

20012001

19981998

19971997

MeasuresMeasures

Time

Time

Products

Products

The Basic Elements of The Basic Elements of an MDX Queryan MDX Query

Select{[Ship Date].[Calendar]} on columns,{[Product].[Product Categories]} on rows

from [Adventure Works]

Using Braces { }Using Braces { }

• Braces Denote a Set• Braces Can be Omitted when the Set is

Unambiguous.• In SSAS 2005 / 2008:• SELECT

[Ship Date].[Calendar] ON COLUMNS, [Product].[Product Categories] ON ROWSFROM [Adventure Works]

In AS 2000:SELECT

{[Ship Date].[Calendar]} ON COLUMNS, {[Product].[Product Categories]} ON ROWS

FROM [Adventure Works]

Using Brackets [ ]Using Brackets [ ]

• Brackets Enclose a String Value• Necessary for:

– Field names with spaces: [New York], [Mary Lo]– Numbers as field names: [2007], [2008]

• Otherwise, the SSAS will treat them as numerous constants

Default MembersDefault Members

• Every Dimension has a Default Member– Usually the “All” member is the default

member.• Default Measures

– The measures dimension also has a default measure

– In our sample cube [Adventure Works], the default member for the cube is [Reseller Sales Amount]

MembersMembers

You want to query more than a single cell.Use Members functionMembers function returns the set of members in a dimension, level, or

hierarchy.select

[Ship Date].[Calendar] on columns,

[Product].[Product Categories].members on rows

from [Adventure Works]

Test Yourself: Number 1Test Yourself: Number 1

[Ship Date].[Calendar] also has a membership; that is, it is made up of more granular information. Modify the query to return the membership of the [Ship Date].[Calendar]dimension.

select

[Ship Date].[Calendar] on columns,

[Product].[Product Categories].members on rows

from [Adventure Works]

Desired result:

Naming Additional Naming Additional DimensionsDimensions

Number Name

AXIS(0) COLUMNS

AXIS(1) ROWS

AXIS(2) PAGES

AXIS(3) SECTIONS

AXIS(4) CHAPTERS

2004

[Ship Date].[Calendar]

2001 2002 2003

[Promotion].[Promotions]

No DiscuntReseller

Retrieving Data from a Retrieving Data from a CubeCube

select

[Ship Date].[Calendar].[Calendar Year].[CY 2004] on axis(0),

[Promotion].[Promotions].[reseller] on axis(1)

from [Adventure Works]

Test Yourself: Number 2Test Yourself: Number 2

• Modify the query to return the sales of Bikes with No Discount

select[Ship Date].[Calendar].[Calendar Year].[CY 2004] on axis(0),[Promotion].[Promotions].[reseller] on axis(1)from [Adventure Works]

Expect Result

Fully Qualified NamesFully Qualified Names

• [CY 2001] below could be – [Delivery Date].[Calendar].[CY 2001] or– [Ship Date].[Calendar].[CY 2001]

select

[CY 2001] on axis(0)

from [Adventure Works]

• [Product].[Product Categories].[bikes] is the same as[Product].[Product Categories].[All Products].[bikes]

2004[Ship Date].[Calendar]

2001 2002 2003Bikes

ComponentsClothing

[Product].

[Product Categories]

Two Dimensions with Two Dimensions with Where ClauseWhere Clause

select

[Ship Date].[Calendar].[Calendar Year].members on axis(0),

[Promotion].[Promotions].[reseller] on axis(1)

from [Adventure Works]

where [Product].[Product Categories].[bikes]

[Promotion].[Promotions]

No Discount

Reseller

DemoDemo

• Lab MDX Query

Few Useful ReferencesFew Useful References

• www.microsoft.com/sqlserver/2008/en/us/analysis-services.aspx

• All BI WebCasts -http://www.microsoft.com/events/series/bi.aspx?tab=webcasts&id=all

• MDX References – msdn.microsoft.com/en-us/library/ms145506.aspx

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