BW Backend Fundamentals

8/10/2019 BW Backend Fundamentals

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SAP Business WarehouseTraining Program



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



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



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



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.



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)



Slide 7

Customer Material Sales Person

ERM: Strong Entities

Relationships Key:

1:1

1:n

n:m

ERM - Strong Entities



Slide 8

Customer

Material SalesPerson

Sales Transaction

Material Group Sales

Organization

Intersection Entity

ERM: Relationships Between Entities

ERM – Relationships between Entities



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.



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



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



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



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



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



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



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



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



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



Slide 19


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



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


Industry key

Business partner

Address

Location

Postcode

State

Country

Region

Currency

VAT #

Incoterms

Price group


Delivering plant


Customer market


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



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



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



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 ?



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



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



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



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



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



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



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



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



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



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



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



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


DeptStores R e g i o n

N o r t h

S o u t h

E a s t


CustomerGroup

Division


DeptStores

2 2 2

R e g i o n

N o r t h

S o u t h

E a s t


CustomerGroup

Division


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



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



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



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



Slide 39


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



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



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



Slide 42

Customer Dimension

Customer number

Incoterms

Price group


Delivering plant


Customer market


Customer number

Customer name

Customer category


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


Industry key

Business partner

Address

LocationPostcode

State

Country

Region

Currency

VAT #

Incoterms

Price group


Delivering plant


Customer market




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



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



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



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



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 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



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



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



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



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.



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



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



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



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



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

BW Backend Fundamentals

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