Lec 3 - SAP BI Modeling

Lecture 3

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Outline OLAP/OLTP Review of Star Schema

Dimension Tables and Fact Table Real world changes

SAP BI Enhanced Star Schema Fact Table Dimension Tables Master Data Tables

MetaData Objects

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OLTP vs. OLAP systemsOperational (OLTP) Informational (OLAP)Detailed Summarised

Can be updated Snapshot records, no updates allowed

Accurate up to the second Timestamp on each record

Used for clerical purposes Used by management

Built based on requirements Built without knowing requirements

Supports small uniform transactions Supports mixed workload

Data designed for optimal storage Data designed for optimal access

Very current data Mainly historical data

Data is application oriented Data is integrated

Referential Integrity is useful Referential integrity is not useful

High availability is normal High availability is nice to haveProduct

Dimension

Quantities Revenues

Costs Taxes

Customer Dimension Sales Dimension

Competition DimensionTime Dimension

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Review of Star Schema Multidimensional data models are needed for the

creation of data warehouses The classic star schema is a commonly used multi-

dimensional model This database schema classifies two groups of data:

facts (sales or quantity, for example) and dimension attributes (customer, material, time, for example).

Facts are the focus of the analysis of a business' activities. The fact data (values for the facts) are stored in a highly normalized fact table.

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Real World Changes The Star schema reflects changes in the ‘real world’

normally by adding rows to the fact table. More precise ‘real world’ changes like Customer ‘4711’

purchase Material ‘BBB’ at Day ‘19980802’ for $100 creates a new record in the fact table, which is identified by the combination of key attributes in the dimension tables.

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Adding Records

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Star Schema Limitations• No support for multiple

languages • Reduced performance due

to the use of Alphanumeric keys

• No support for time dependent changes

• Duplication of Dimensional data.

Material IDCustomer IDDate ID

Sales Amount (K)Unit Price (K)Quantity (K)

Date ID

Year Fiscal Year

QuarterMonth

Material ID

Material Name Material Type Material Group

Customer ID

Customer NameCity RegionType

Fact Table

CustomerDimension

TimeDimension

MaterialDimension

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SAP BI SchemaThe multi-dimensional model in SAP BI is based on

the SAP BI schema, which was developed as an enhanced star schema as a response to problems experienced with the classic star schema.

The enhancement comes from the fact that the dimension tables do not contain master datainformation!

Master data is stored in separate tables, called master data tables (outside the dimension tables).

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Snowflake model SAP BI uses a snowflake model instead of the star schema Further normalization and expansion of the dimension

tables in a star schema result in the implementation of a snowflake design.

A dimension is said to be snowflaked when the low-cardinality columns in the dimension have been removed to separate normalized tables that then link back into the original dimension table.

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Dimensional Models

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Snowflake

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When do you snowflake?Snowflaking a dimension table can typically be

performed under the following two conditions: The dimension table consists of two or more sets of

attributes which define information at different grains (detail)

The sets of attributes of the same dimension table are being populated by different source systems (very common in data warehouses)

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Details of the SAP BI Schema1. The center of an InfoCube forms the fact table

containing the key figures (e.g. sales amount).

2. The fact table is surrounded by several dimensions.

3. A dimension consist of different table types: Dimension Table

Attributes of the dimension tables are called characteristics (e.g. material). The meta data object in BI that describes characteristics and also key figures (facts) is called InfoObject.

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SAP BI Extended Star Schema Master Data

Represents Dimensional data Independent of any Fact table Reusable in multiple Fact tables Designed to support multi lingual systems Designed to support time dependent data. Designed to improve query performance.

Fact table Consists of Dimensional Keys and Key Figures (facts).

Dimension table Acts as a link between Master data and Fact table records

InfoCube Consists of Fact and Dimensional tables.

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InfoCubes

InfoCubes are the central objects of the multi-dimensional model in SAP BI

Reports and analyses are based on these InfoCubes It is a self-enclosed dataset for a business area from a

reporting viewpoint Queries can be defined and executed on the basis of

an InfoCube

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InfoCubes

InfoCube consists of a number of relational tables arranged together- Fact Table – One table in which key figures are stored. Max 233 key

figures! Dimension Tables – Linked by SID tables to the master data tables.

(Remember master data is not part of the InfoCube. Those are stored and maintained outside the InfoCube.)

Dimension – Grouping of logically related characteristics. SAP BI allows a total of 16 dimensions. Max 248 characteristics per dimension.

Technical name of SAP provided InfoCubes starts with 0. Your own Cubes begin with A-Z, 3-9 characters long

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SAP BI Schema

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Types of InfoCubes

Standard Cube Physically contains data in the database They are data targets ie. data can be loaded into it A type of InfoProvider BI objects are called InfoProviders when queries can be

executed based on them Standard InfoCubes are technically optimized for read access.

Virtual Cubes (Virtual Provider) Only represent logical views of a dataset Data resides in data source

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Viewing InfoCube Schema

Go to the Data Warehousing Workbench Metadata Repository

Choose InfoCube Find the InfoCube you want to view Eg. SAP Demo Sales and Distribution overview -

0D_SD_C03 Choose InfoCube - schematic display as star

schema

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Viewing/Downloading Data in an InfoCube1. Start Data Warehousing Workbench: Modeling (RSA1)

Choose Modeling InfoProvider Navigate to the InfoCube using the hierarchy of InfoAreas (SAP Demo

Sales and Distribution overview - 0D_SD_C03)

2. Right click and Display data3. Click on Fld Selection for output.4. Select the fields (characterisitcs and key figures that you

want)5. Execute6. Choose value ranges (if desired)7. Execute8. Request export to a local spreadsheet file9. View the data in the spreadsheet. Note that this is a flat file

derived from a relational database (star schema) 21

Terms used

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InfoCube and Master Data

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Master Data Tables Additional information about characteristics is

referred to as master data. Master data types:

Attributes Texts (External) Hierarchies

Eg. the attribute 'material group' is stored in the attribute table, the text description for 'material name' is stored in the text table and the material hierarchy is stored in the hierarchy table for the characteristic 'MATERIAL'.

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SAP BI MetaData Objects –Master Data Tables3 possible components Text

Textual description of Master Data element (AU = Australia).

Can be language and time dependent. Attributes

Characteristics which describe another Characteristic. (Address, Telephone.)

Can be language and time dependent. Hierarchies

Tree like structures to group master data. Can be language and time dependent.

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Note: Master Tables = Attribute Tables in this slide

BI IconsKey Figure

Characteristic

InfoCube

Dimension

Hierarchies

Maintain Master Data

Text

InfoObject

InfoSource

Source System

InfoArea

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SAP BI MetaData Objects

InfoObjects Represent the structure that allows data to be stored in a BI

systems. Used to describe business processes and information

requirements (fields): Customers Sales revenue

Contain technical and specialist information for master data and transaction data in the Meta Data Repository.

Used in BI to create structures and tables. Either Characteristics or Key Figures.

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SAP BI MetaData Objects – Key Figures

Date Customer Material Sales Quantity Amount

1/1/08 Jones Racer 26 2 $2000

1/1/08 Rosemann Mountain B 5 $4000

Key Figures Any kind of numeric information used to measure a

business process.

Six different types Amount * Quantity * Number Integer Date Time* Requires Unit (currency, size) for further clarification

Amount Currency1000 EUR

1500 USD

30,000 ZWD

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Year Month SalesRepNO

ProductNO

Sales

2007 10 S1 P1 5002007 10 S1 P2 5002007 10 S2 P1 2002007 10 S2 P2 6502007 10 S3 P1 300

Aggregation Key Figures are stored

in Fact Table Aggregation defines

how they are stored Sum Maximum Minimum

OrdDate SalesRepNO

ProductNO

Sales

23.10.2007 S1 P1 30024.10.2007 S1 P1 20025.10.2007 S1 P2 10026.10.2007 S2 P1 20026.10.2007 S2 P2 30027.10.2007 S1 P2 40027.10.2007 S3 P1 10030.10.2007 S2 P2 20030.10.2007 S3 P1 20030.10.2007 S2 P2 150

Fact Table

Transaction Table

Sum

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SAP BI MetaData Objects –Characteristics

Date Customer Material Sales Quantity Amount

1/1/08 Jones Racer 26 2 $2000

1/1/08 Rosemann Mountain B 5 $4000

Characteristics Designed to describe objects used with in the

business process customer, product, colour, postcode, date, unit etc.

They are the attributes of the dimension table

Could be made up of more than one field Used to analyze Key Figures

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Characteristic(Single)

Region City Customer Name Customer Phone

Characteristic(Grouped)

Customer State City Customer Name Customer Phone

(Attributes of Customer)

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SAP BI MetaData Objects –Characteristics Characteristic

definition contains: Technical field descriptions (data type, length, etc) Display properties Transfer routines (executed when data is uploaded) Master data descriptions (time dependency, navigational properties, text

properties, language dependency, Hierarchies) Unit Characteristic

currencies and units of measure are required for Amount and Quantity data types

Time Characteristic compulsory assignment to time dimension in InfoCube, (Calendar

year) Technical Characteristic

only technical use in BI (Request ID)

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Types of Master Data Tables

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Characteristic - Compounding

Department

DepartNO (PK)

Cost Center

CostCenterNO (PK)

Dimension

DepartNO (PK) CostCenterNO (PK)

Entity Relationship Model

• Compounding refers to the when more than one characteristic is required to uniquely identify a master data record.

• The Characteristics combine to form a concatenated primary key within a Dimension table.

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SAP BI MetaData Objects –InfoCube Contains two types of data

Key Figures (transactional data) Characteristics

1 Fact Table and 16 Dimension Tables 3 Dimensions are predefined by SAP

Time Unit Data Package

Dim1 Dim2 Dim3 Dim4 Dim5 Dim6 KF1 KF2

3 Pre-defined Up to 13 User-defined

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Surrogate Keys• In order to improve query execution the system assigns a numeric

(surrogate) key to represent the structured alpha numeric key assigned in Master data.

• A SID table is used to store the reference between the two keys.

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Dimension Table

KFSales

DIM IDTime

DIM IDProduct

DIM IDSalesRep

Fact Table

2233

11SIDDIM ID

Master DataSID Table

KFSales

DIM IDTime

DIM IDProduct

DIM IDSalesRep

Fact Table

2233

11SIDDIM ID

Master DataSID Table

• A Dimension table does not store any master data records. It contains a series of surrogate keys that link the Fact table and Master data

together.

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SAP BI Meta Data Objects –InfoArea/InfoCatalog An InfoArea is a directory that stores all related

InfoObjects within the same business context. An InfoObject Catalog is a folder within an

InfoArea that stores specific InfoObjects. Separate InfoObject Catalogs are used for Key Figures

and Characteristics

Displayed via a tree structure or directory

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InfoObjects SummaryInfoObjects

Key Figures Characteristics

Characteristic

Time

Unit

Package

Attribute

Text

Hierarchy

Amount

Quantity

Number

Integer

Date

Time

Unit

Unit

Key Figure InfoObject CatalogCharacteristic InfoObject Catalog

InfoArea 43

Aggregation

The system has to aggregate using multiple characteristics.

The system can aggregate with another rule for each key figure (exception aggregation).

The key figure Number of Employees would, for example, be totaled using the characteristic Cost Center, and not a time characteristic. Here you would determine a time characteristic as an exception characteristic with, for example, the aggregation Last Value.

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Hierarchies

Hierarchies allow data to be presented in alternative ways.

Hierarchies can be either Version, Time or Interval dependent.

Version Dependent

Region

North South

S1 S3S2 S4 S5

Version 1

Region

North South

S1 S5 S2 S4

Version 2

S3

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Hierarchies• Time Dependent

• Interval DependentInstead of each node representing a single value, node may now represent a range of values.

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InfoCube: Quick build1

• Create InfoArea

2• Create InfoObject

Catalogues

3• Create Characteristics

and Key Figures

4• Create InfoCube and

Define Dimensions

5• Assign Characteristics to

Dimensions

6• Choose Key Figures

7• Save and Activate

InfoCube

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1. Start with the Data Warehousing Workbench– Modeling

2. Since InfoCubes are InfoProviders, go to InfoProvider

3. Create InfoArea if needed4. Create InfoCube in the context menu of your

InfoArea5. Specify name and description (you can copy an

existing InfoCube too)

6. Choose a Standard Cube. Save

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InfoCube: Detailed build

7. Time dimension- Add the InfoObjects that you want to be part of the Time dimension

8. Then add and modify dimensions as modeled in your star schema

9. Add InfoObjects (characteristics) to the dimensions. These InfoObjects were created previously

10. Add Key figures InfoObjects11. Save and Activate the InfoCube12. You can view the Data Model in the context menu of

the cube.

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Summary SAP BI uses an extended (snowflake) star schema Dimension tables, fact table and master data tables are

used to create the snowflake Master data tables reside outside the InfoCube Characteristics and key figure InfoObjects are used to

model attributes of dimension tables and fact tables respectively

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Documents

Lec 3 - SAP BI Modeling