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8/10/2019 BW Backend Fundamentals
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SAP Business WarehouseTraining Program
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Slide 2
Course I Objectives
• Describe the architecture and advantages of using BW
• Get familiar with BW terminology
• Manage Meta Data and define new Info Objects, Data
• Sources and Info Sources
• Create Transfer Rules and Update Rules
• Create Info Cubes and ODS Objects
• Concept of Business Content / Activating the Business Content
• Schedule and Monitor Data Loads
• Create and Execute Queries in BEX Analyzer
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Slide 3
InfoObject AMOUNT##
InfoObject COSTC## 13-FigureTransfer Routine
Texts
Hierarchies
Attributes
Comm. Struct .
Transfer Rules
Transfer Struct .
Master Data InfoSource COSTC##
Texts Hierar -
chies Attribs
Comm. Struct .
Transfer Rules
Transfer Struct .
InfoSource GR##ISCCA
Cost AccountingTransactionData
Transfer Rules
Transfer Struct .
Comm. Struct .
Transfer Struct .
Extract Struct .Extract Struct .
Comm. Struct .
Transfer Rules
Transfer Struct .
CSKS CSKT
BCT InfoSource 0CO_OM_CCA_1
Update Rules Update Rules
GR##CUBE1
SAPSource System
External Data
BW System ODSObject
Update Rules
COSP COSS
Aggregates
Authorizations
Queries
G en er i c
D a t a S o ur c e
B u s i n e s s C o n t e n t
D a t a S o u r c e Transfer Struct .
Scenario
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Slide 4
BW Architecture
Business Information Warehouse Architecture
Client
Business Explorer
InfoCubes
BAPI BAPI
InfoCubes
OLAPProcessor
Administrator Workbench
Master Data Master Data
BAPI BAPI
Metadata Repository
Source S ystems :
Components
B W S
E R V E R
B W S
E R V E R
ODS ODS
RFC RFC
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Slide 5
Multi Dimensional Data Model
Data Modeling for the Data Warehouse
l The aim of a multidimensional data model:
n A data model that provides users with information corresponding to his or her business processes, meaning a data model that allows users to look at his or her performance indicators from the point of view of how these key figures influence the company.
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Slide 6
Basic Modeling Steps (I)
The Entity Relationship Model (ERM)
The following steps are involved in developing an ERM:
l Determine the entities that belong to a process - your basic business terms
l Determine the relationships between these entities
l Determine information about the attributes for each entity
l Determine the degree of normalization
An entity relationship model allows you to better understand the structure of the business processes in your source systems
Entity Relationship Model (ERM)
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Slide 7
Customer Material Sales Person
ERM: Strong Entities
Relationships Key:
1:1
1:n
n:m
ERM - Strong Entities
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Slide 8
Customer
Material SalesPerson
Sales Transaction
Material Group Sales
Organization
Intersection Entity
ERM: Relationships Between Entities
ERM – Relationships between Entities
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Slide 9
Normalization
Customer
City
Region
Material Group
Price
Sales Person
Sales Org.
Material
Material Type Color
ERM: Attributes and Normalization
Transaction
STRONG ENTITIES
Attributes of the Strong Entities
Intersection Entity
l Date
l Sales Order #
l Sales Order Item #
l Sales Document #
l Customer #
l Material #
l Sales Person #
l Amount
l Quantity
l Currency
Sales Org.Location
ERM – Attributes and Normalization
n
Normalization means that information about regions and cities is not stored with the information in the sales transactional table.
This would mean that the information about a customer’s region and city would have to be repeated each time the customer placed
a new order for a product. Instead, the relevant region appears in the city table and the city appears on the customer table. Other examples
of normalization are also shown.
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Slide 10
The Multidimensional Model (MDM)
MDM
Time
Dimension
Material ID
Material Name
Material Type
Material Group
Customer ID
Customer Name
City
Region
Office Name
Time ID
Year
Fiscal Year
Quarter
Month
Day of the Week
Sales Order Data
Material ID
Sales Area ID
Time ID
Customer ID
Sales Quantity
Quantity
Unit Price
Customer D imens ion
Sales Area Dimension
Material Dimension
Facts
Dimension
Keys
Sales Area ID
Sales Person
Sales Organization
Multi Dimensional Model
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Slide 11
From ERM to MDM
Sales Person Sales Org.
Material ID
Material Name Material Type Material Group
Customer ID
Customer Name City Region
Time ID
Year Fiscal Year Quarter Month Day
Material ID
Sales Area ID Time ID Customer ID
Amount Quantity
Price
Time Dimension
Customer D imens ion
Sales Area
Dimension Material Dimension
Fact Table
Customer
City
Region
Material Group
Sales Order
Price
Sales Org.
Material
Material Type Color
Aim of the MDM: To focus on analytical needs without complicating the data model unnecessarily
Sales Area ID Entity Relationship Model
Multidimensional Data Model
Sales Person
Sales Org. Location
ERM to MDM
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Slide 12
*Ralph Kimball
1. The basic business processes and the identity of facts in the fact table (a fact table - an InfoCube) -> Intersection entities
2. The dimensions for each fact table -> Strong entities
3. The dimension attributes with complete descriptions and proper
terminology -> Attributes of the entities 4. The granularity (level of detail) of each fact table
5. The facts, including pre-calculated facts
6. The tracking options for historical data -> Dimensions that change slowly
7. The aggregations, heterogeneous dimensions, mini-dimensions, query modes, and other decisions about the physical storage of
data 8. The life-span of the database (archiving aspects)
9. The time frame in which data is extracted and loaded into the data warehouse
... in a comprehensive database concept for a data warehouse*
The nine crucial points ...
9 points to remember…..always
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Slide 13
Dimension 2 Dimension 2
Facts
Dimension 1 Dimension 1 Dimension 3 Dimension 3
Dimension 4 Dimension 4 Dimension n Dimension n
l One process is modeled at a time
l A star schema optimizes the
storage of data for reporting
purposes
l Characteristics are structured
together in related branches
called dimensions
l The key figures and other calculations form the facts
l This structure is identical for all application areas
Star Schema Properties
Star Schema Properties
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Slide 14
Material Material Dimension Dimension
Quantities Revenues
Costs
Customer Customer Dimension Dimension
Sales Area Sales Area
Dimension Dimension
Competition Competition
Dimension Dimension Time Dimension Time Dimension
Example: Sales - Data Model
l Who have we sold to?
l What have we sold?
l Who sold it?
l How much have we sold?
l Who were our competitors?
l When have we sold?
Data Model Example
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Slide 15
Time Dimension Material Dimension
Customer Dimension
M Material # Material Grp …
2101004 Screens ...
C Customer # Region …
13970522 West ...
T Period Fiscal Year …
10 1999 ...
Dimension Tables
l Dimension tables are groupings of related characteristics.
l A dimension table contains a generated primary key and characteristics.
l The keys of the dimension tables are interpreted as foreign keys in the fact table.
Dimension Tables
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Slide 16
Customer Dimension
Customer number
Customer name
Customer category
Customer subcategory
Industry key
Business partner
Address
Location Postcode
State
Country
Region
Currency
VAT #
Incoterms
Price group
Account assignment group
Delivering plant
Customer classification
Customer market
Customer statistics group
Material Dimension
Material number
Material description
Division
Industry
Material type
Material group
Category
Subcategory
Manufacturer
Manufacturer part number
Market key
Price class
Valuation class
Time Dimension
Calendar day
Calendar year / week
Calendar year / month
Fiscal year
Fiscal year / period
Fiscal year variant
Example: Sales - Dimensions
Example : Sales Dimension
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Slide 17
M C T Quantity Sales Cred .Memos Open Orders
250 $ 500,000 $ 50,000 $ 280,000 50 $ 100,000 $ 7,500 $ 60,000
… … … ...
Fact Tables
Fact Tables
l Central intersection entities, meaning N:M relationships between strong entities, are converted into fact tables
l The individual data records in the fact table are identified uniquely by the keys of the dimension tables
l The fact table is maintained when transaction data is loaded
l Fact tables have a relatively small number of columns (key figures) and a large number of rows
Fact Tables
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Slide 18
Quantity of incoming orders
Value of incoming orders
Sales quantity
Sales
Quantity of open orders
Value of open orders
Quantity of returns
Returns receipt value
Credit memo quantity
Credit memo
List price
Discount
Invoice price
Example: Sales - Facts
Facts
Example – Sales facts
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Slide 19
C Customer # Region …
13970522 West ...
M C T Quantity Sales CreditMemos Open Orders
250 $ 500,000 $ 50,000 $ 280,000
50 $ 100,000 $ 7,500 $ 60,000
… … … ...
Time Dimension Material D imension
T Period Fiscal Year ...
10 1999 ...
Fact Table
Customer Dimension
Star Schema
l The combination of fact tables and dimension tables is
called a star schema.
M Material # Material Grp …
2101004 Screens ...
Star Schema
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Slide 20
Facts
Quantity of incoming orders
Value of incoming orders
Sales quantity
Sales
Quantity of open orders
Value of open orders
Quantity of returns
Returns receipt value
Credit memo quantity
Credit memo
List price
Discount
Invoice price
Customer
Material Time
Example: Sales - Star Schema
Customer Dimension
Customer number
Customer name
Customer category
Customer subcategory
Industry key
Business partner
Address
Location
Postcode
State
Country
Region
Currency
VAT #
Incoterms
Price group
Account assignment group
Delivering plant
Customer classification
Customer market
Customer statistics group
Time Dimension
Calendar day
Calendar year / week
Calendar year / month
Fiscal year
Fiscal year / period
Fiscal year variant
Material Dimension
Material number
Material description
Division
Industry
Material type
Material group
Material type
Category
Subcategory
Manufacturer
Manufacturer part number
Market key
Price class
Valuation class
Example – Sales Star Schema
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Slide 21
Differences in Terminology
Basic Star Schema BW Star Schema Basic Star Schema BW Star Schema
Fact Key Figure
Fact Table Fact Table
(Dimension) Attribute Characteristic Navigation Attribute Display Attribute (External) Hierarchy
Dimension (Table) Dimension Table Attribute Table Text Table
Hierarchy Table (SID Table)
The Terminology - differences
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Slide 22
Different Types of Data in BW
InfoCubes InfoCubes
PSA
Transaction Transaction
Data Data
ODS
Amount
Quantity
Number ...
InfoObjects: Key Figures:
Characteristics:
Cost Center
Controlling- Area
Cost Element
...
Texts
Attributes
Hierarchies
Master Master Data Data
Meta Data – Types of Data in BW
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Slide 23
What is an InfoObject?
InfoObject 0COSTCENTER
InfoObjects are unique across different application components
CO Controlling
CO Controlling
R/3
OLTP
HR Human
Resources
HR Human
Resources
COST ...
Table of cost centers
Table of employees
EMPLO COST_CENTER ...
BW Extractor
DataSource for
Cost Center
l The various OLTP data models are unified in BW
l Business objects / data elements become InfoObjects
What is an Info Object ?
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Slide 24
l Characteristics: Evaluation groups such as cost center product group , and material
n Discrete values are stored in each master data table (for example, the characteristic r egion has the values North , South , and so on )
n Special types of characteristic:
w Time-characteristics like f iscal year and calendar year
w Unit-characteristics including currencies and units of measure such as l ocal currenc y (DEM) or Kg .
l Key figures: Continually updated numerical fields such as amounts and quantities (for example, revenue and s ales quanti ty )
Types of Info Objects
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Slide 25
Creating a Characteristic
BEx MasterData / Txt Hierarchy Attributes Compound
Data Type
Length
Conversion
Routine
CHAR-sequence
13
ALPHA
Transfer Routine
X Transfer routine
available
Characteristic
LongDescrptn
ShortDescrptn
Version
Object Status
COSTC##
Cost Center (13-Figure)
CostCntr (13-Fig.)
New
Inactive, non-executable
Display parameters BEx Map
With master data? With text? Text length?
Language-dependent? Time-dependent?
With hierarchies? Version-dependent? Time-dependent? Intervals allowed?
List of associated InfoObjects - display or navigation?
Higher-Level InfoObjects
General
Creating Info Objects - Characteristic
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Slide 26
Texts
Cost Center
Short Text (20) Medium Text (40)
Long Text (60)
Language-dependent
Time-dependent
Cost Center Texts
Master Data / Texts
Texts for characteristic: COSTC##
EXT0000001200
Cafe
Cafeteria
Cafeteria
E
from 01.01.1994 to 31.12.9999
l You can choose short, medium, and long texts for each characteristic.
l Texts can be language-dependent.
l Texts can be time-dependent.
Texts
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Slide 27
Navigation Attributes and Display Attributes
Business area
Company code
Person responsible Profit center
Strategic business unit
. . .
Cost Center Attributes Navigation Display Time-Dependent
l You use the navigation attributes of a characteristic in the analysis (execution of queries) for navigation purposes.
n Usability for navigation purposes is similar to the other characteristics in the
queries n Can be used to build aggregates (time-independent attributes only)
l You use display attributes in the analysis only as descriptive information.
Attributes
Attributes for characteristic: COSTC##
Navigation and Display Attributes
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Slide 28
Hierarchies
l Hierarchies are used in the analysis to describe alternative views of the data.
l Hierarchies consist of a series of nodes that are joined hierarchically to one
another.
l Usually, the leaves of a hierarchy are represented by values from characteristics.
Hierarchy
Hierarchy for characteristic: COSTC##
Cost Center Hierarchy
Version 01
Company 1000
Corporate Admin. & Finance
Executive
Board
Internal Services
Corporate
Services Cafeteria Telephone
Finance Admin Human Resources
Hierarchies
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Slide 29
InfoObjects: Characteristic
l BW term for a business-evaluation object
l A unique name containing technical information and
business logic
l Components of characteristics:
n Technical definition (length, format, check routines, and so
on)
n BEx usage
n Master data, texts
n Attributes
n Hierarchies
n Information on compounding
Summary - Characteristics
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Slide 30
Creating a Key Figure
Type/Unit Aggregation Extra Properties
Type / Data Type
Data Type
Currency/Unit
Fixed Currency
Fixed Quantity
Unit / Currency
CURR- currency field, stored as DEC
Amount Number Date
Amount Integer Time
Key Figure
LongDescrptn
ShortDescrptn
Version
Object Status
AMOUNT##
Amount ##
Amount ##
New
Inactive, non-executable
Aggregation Exception Aggregation
BEx display parameters Misc . (data element) Last changed by / on
0CURRENCY
Creating Key Figures
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Slide 31
Info Object: Key Figure
l BW term for a business-evaluation object
l A unique name containing technical information and
business logic
l Components of key figures: n Technical definition (data type)
n Key figure type
n Currency/unit
n Aggregation behavior
n BEx usage
Summary - Key Figures
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Slide 32
Aggregation Options for Key Figures
l Time period and aggregation structures may cause key figures to behave differently
n No aggregation (price)
n MAX,MIN,SUM (normal situation)
l One characteristic may require a different type of aggregation (for example, non- cumulative , non-
cumulative values, and so on). This characteristic is the
reference for the exception aggregation.
l Exception aggregation provides many types of aggregation: SUM, MAX, MIN, different average values and counters, FIRST, LAST, deviation, and so on.
Key Figures – Aggregation Options
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Slide 33
BW InfoCube Components
l
Central data stores for reports and evaluations l Contain two types of data:
n Key figures
n Characteristics
l 1 fact table and up to 16 dimension tables
n 3 of these dimensions are defined by SAP
n Time
n Unit
n InfoPackage
BW InfoCube Components
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Slide 34
Customer G roup
Division
R e g i o n
Dept.Store
Wholesale
Retail
Glass Ceramics Plastics Clay Copper Tin
N o r t h
S o u t h
E a s t
InfoCube: Example
InfoCube in BW
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Slide 35
R e g i o n
N o
r t h
S o u t h
E a s t
Glass Ceramics
Customer Group
Division
Retail TradeWholesale
DeptStores
Analysis of the Ceramics Division
Analysis of the Plastics Division
Analysis of the Plastics Division and the South Region
R e g i o n
N o r t h
S o u t h
E a s t
Glass Ceramics Plastics
Customers
Group
Division
Retail TradeWholesale
DeptStores R e g i o n
N o r t h
S o u t h
E a s t
Glass Ceramics Plastics
CustomerGroup
Division
Retail TradeWholesale
DeptStores
2 2 2
R e g i o n
N o r t h
S o u t h
E a s t
Glass Ceramics Plastics
CustomerGroup
Division
Retail TradeWholesale
DeptStores
3 3 3
Product group
Customer group
Division
Area
Company code
Region Period
Profit center
Business area
Plastics
Characteristics: Query Cache InfoCube
1 1 1
InfoCube: Multdimensional Analysis
InfoCube in BW – Multi Dimensional Analysis
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Slide 36
Division
1 100
RT-0001
North Plastics
Retail Trade
Sales Hoursworked
4.000.000 1.300.000 Key Figs.
Chars
Customer Group
R e g i o n
InfoCube: Characteristics and Key Figures
l Key figures are stored for a unique combination of characteristic
values
l Number of dimensions is the degree of granularity / summarization
level of the dataset
InfoCube – Data Slice
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Slide 37
From Data Model to Database
Star Schema
InfoCube
Customer Dimension
M a t e r i a l D i m e n s i o n
Material Material
Dimension Dimension
Quantities Revenues
Costs Rev./Group
Customer Customer
Dimension Dimension
Sales Sales
Dimension Dimension
Competition Competition
Dimension Dimension
Time Time
Dimension Dimension
Real interconnected database tables on a BW database server.
Terminology used to describe the multidimensional modeling of a business process.
From Data Model to Database
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Slide 38
1. Create New InfoCube Name in Selected InfoArea
2. Choose Characteristics Specified in the Data Model
4. Assign Characteristics to Dimensions
3. Create User-Defined Dimensions
5. Choose Time-Characteristics
6. Choose Key Figures
7. Activate
Steps to Create an InfoCube
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Slide 39
C Customer # Region …
13970522 West ...
M C T Qty Sales Cred . Mems Open Orders
250 $ 500,000 $ 50,000 $ 280,000
50 $ 100,000 $ 7,500 $ 60,000
… … … ...
Time Dimension Material D imension
T Period Fiscal Year ...
10 1999 ...
Fact Table
Customer Dimension
Star Schema
M Material # Material Group …
2101004 Screens ...
l The combination of fact tables and dimension tables is called a star schema
Star Schema
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Slide 40
Problems with the Basic Star Schema
l Multilingual descriptions for attributes in the dimension
tables are not supported.
l Secondary indexes for the data are stored as alphanumeric
fields in comprehensive tables. This makes it more difficult to access the data.
l If attributes of the dimensions change over time, there is no
way of maintaining the old and new values for the attribute.
l Even if the majority of a company ’s master data is used
across the different business processes, each star schema
must duplicate all of the data that is required for all of the
possible user-reports that might be generated.
l All hierarchy relationships for the data must be modeled as attributes (characteristics) of a dimension table. It is not
possible to generate user-defined hierarchy types.
Problems with Basic Star Schema
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Slide 41
l Only characteristics from the dimension tables can be used to
access the facts.
l No structured drilldowns can be generated.
l Supporting a large number of languages is difficult.
A basic star schema has the following restrictions:
l Master data tables and their associated fields (attributes)
l Text tables with extensive multilingual descriptions
l External hierarchy tables for structured access to the data
In BW the extended star schema enables you to
access:
Extending the Star Schema
Extending the Star Schema
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Slide 42
Customer Dimension
Customer number
Incoterms
Price group
Account assignment group
Delivering plant
Customer classification
Customer market
Customer statistics group
Customer number
Customer name
Customer category
Customer subcategory
Industry key
Business partner Address
Location
Postcode
State
Country
Region
Currency
VAT #
Customer Number: Attribute
Attributes within the dimension Attributes separate from the dimension
Master Data Outside the Dimension
Customer Dimension Customer number
Customer name
Customer category
Customer subcategory
Industry key
Business partner
Address
LocationPostcode
State
Country
Region
Currency
VAT #
Incoterms
Price group
Account assignment group
Delivering plant
Customer classification
Customer market
Customer statistics group
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Slide 43
C Customer # Area Chain Store HeadOffice C Customer # Area Chain Store HeadOffice
C M T Sales Qty Sales Disc. Sales Overheads Stock Value C M T Sales Qty Sales Disc. Sales Overheads Stock Value
T Period Fiscal Year … T Period Fiscal Year …
M Material # Mat . Grp Brand Catgry M Material # Mat . Grp Brand Catgry
Mat . Nbr Language Material Description Mat . Nbr Language Material Description
Time Dimension
Material D imension
Customer Dimension
Material Master Data Texts
Fact Table
Territory 1 Territory 2 Territory 3
District 1
Territory 4
District 2
Area 1
Territory 5 Territory 6
District 3
Area 2
Territory 7
District 4
Territory 8 Territory 9
Area 3
Sales hierarchy
Sales InfoCube
Customer # Name Location Industry Key Customer # Name Location Industry Key
Customer Master Data : Attributes Sales Hierarchy
Extended Star Schema
District 5
Extended Star Schema
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Slide 44
Modeling Attribute s : Decisions
Settings for attributes:
l InfoObject
n Navigation or display
n Time-independent or time-dependent
n Navigational name/description for the user to see on
queries
l InfoCube
n Activate navigation attribute
Modeling Attributes - Decisions
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Slide 45
Modeling Attribute s : Results
Results:
l Display attributes:
n Can be used only as descriptive information
l Navigation attributes:
n Can be used for navigation purposes similar to the other characteristics in the queries (dimension characteristics)
n Can be used to build aggregates (time-independent attributes only)
Modeling Attributes - Results
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Slide 46
SAP BW: Extended Star Schema
Fact Table
Dimension
Table
Fact Table
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Text
SID Table
Master
Hierarchy
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
Dimension
Table
BW Star Schema
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Slide 47
Transfer Rules
Update Rules
InfoCubes
Communication StructureCommunication Structure
BW Server
Transfer StructureTransfer StructureTransfer StructureTransfer Structure
InfoSource
Transfer Rules
Transfer StructureTransfer Structure
Extraction Source StructureExtraction Source Structure
DataSource
mySAP System
Flat File Source System
Extraction Source StructureExtraction Source Structure
Transfer StructureTransfer Structure
Transfer StructureTransfer Structure
Transfer Rules DataSource (Replica)
Source System, Data Sources and Info Sources
– ETL Overview
Source System - Any system which sends data to the BW system.
Transfer Structure - consists of all the fields of the Extract Structure in Source System – Transfer Rules will Apply
Communication Structure – Link between Transfer Structure and Info Cube/ODS – Update Rules will Apply
8/10/2019 BW Backend Fundamentals
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Slide 48
Replicating DataSources for R/3 Source Systems
R/3 Source System
Extraction
Source-Structure
BW Server
Transfer Structure
Transfer Structure
DataSource
The structure of the
source of the extraction is
used as a template for the
transfer structure in BW
Function:
Repl icate DataSources
1
2 1
2
Replicating the transfer structure (BW -> Source
System) by activating the
transfer structure in BW
Function:
Activate Transfer Rules
Replicating Data Sources for R3 Systems
8/10/2019 BW Backend Fundamentals
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Slide 49
Info Sources
• Master Data Info Source
• Attributes
• Texts
• Hierarchies
• Transaction Data Info Source
Info Source Types
Info Source Components
• Transfer Structure
• Communication Structure
• Transfer Rules
……and is assigned to a Source System
8/10/2019 BW Backend Fundamentals
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Slide 50
Transfer Rules & Update Rules
• Transfer Rules are defined for a Info Source and a Data Source
• They are Link between a Transfer Structure and a Communication Structure.
• Type of Transfer Rules:
• Field to Field mapping• Fixed Value
• Create and assign Transfer Routines (ABAP Code)
• Update Rules are defined for an Info Source and Info Provider which can
be a ODS or an Info Cube.
• Types of Update Rules:* Simple Move
* Start Routine (ABAP)
* Routine
* Formula
* Constant
8/10/2019 BW Backend Fundamentals
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Slide 51
PSA
PSA stands for Persistent Staging Area.
Flat tables which hold raw data coming from source systems before
The data is transformed. Meaning before transfer rules are applied.
PSA remains in the system till the administrator deletes it on a periodic
Basis.
Major advantage of PSA Re-Extraction of data is not necessary if there
is a data loss.
Therefore the Data Flow from Source System to BW is…..
Data Source in Source >> Transfer Structure in BW >> Transfer Rules are applied >>Communication Structure >> Update Rules >> InfoCube/ODS.
8/10/2019 BW Backend Fundamentals
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Slide 52
Business
Information
Warehouse
Server
InfoCube
Market Info
Market Info
Data Provider
InfoSource InfoSource
Data Extraction
Sales Europe
Data Extraction
Sales Europe
R/3 System
InfoCube
InfoSource InfoSource
Transfer Rules
File interface
ASCII Data
ASCII Data
Non-SAP System
Data Extraction
Sales America
Data Extraction
Sales America
Non-SAP Extraction Tool
Non-SAP
Extraction Tool Source
Systems
Extraction from Multiple Sources
Transfer Rules
Update Rules Update Rules Update Rules
Extracting data from multiple sources
8/10/2019 BW Backend Fundamentals
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Slide 53
Scheduler
l InfoPackage
n Parameterized extraction job with
w InfoSource
w Source system
w
Selection criteria including variables
n User-friendly
n Consistency achieved through synchronized extraction
l One-off or repeated extraction jobs
n InfoPackage groups
n R/3 job scheduling
n Triggered by events or time
Scheduler
8/10/2019 BW Backend Fundamentals
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Slide 54
Monitor
l InfoI Docs sent between the BW and the source system to monitor the data requests and the processing of data (traffic light status in the monitor)
l All the data requests that you want to analyze are listed in a tree structure
l Header data for the request is displayed n Technical names of all the objects involved in the data
flow
n Option of branching to the maintenance screens of the individual objects
n Runtime analysis
l Detailed display of the individual extraction and
processing steps enables targeted control
Monitor
8/10/2019 BW Backend Fundamentals
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Slide 55
Green
Yellow
Red
Data Loading: Status Indicators in the Monitor
l Status Successful
n No errors occurred
n Data has been updated successfully
l Status Not yet com pleted/Warning
n Data update process is still running
n Invalid number of data packages (updated IDocs )
n Invalid data request (scheduler)
n No data in the source system
l Status Incorrect (Errors)
n Errors in the source system
n Data loaded with errors
n Indeterminate status after time-out
Data Loading – Status Indicators in the Monitor
8/10/2019 BW Backend Fundamentals
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Slide 56
Step-by-Step Extraction
1. Create source system in BW
2. Replicate DataSource from the source system (Template for the InfoSource in BW)
3. Prepare the InfoSource in BW (transfer rules, extraction methods)
4. Create update rules for the data target (InfoCube)
5. Create InfoPackage and schedule it for update
6. Use the control options in the monitor
Step by Step Extraction of Data from Source System
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