BW Training Slides v2.1a

© SAP UCC 2006 SAP BW Course 1

SAP® NetWeaver™ Business Intelligence

Business Information Warehouse

The German University Competence Centers’ BW CourseBased on SAP BW 3.5 and mySAP® ERP® 2004 (ECC 5.0)

Basics - Reporting & Analysis - Modeling & Staging - Data MiningmySAP® ERP® Connectivity

© SAP UCC at Technische Universität MünchenDipl. oec. Matthias Mohr / Prof. Dr. Helmut Krcmar


Copyright 2006 SAP UCC TU München All Rights Reserved

Neither this publication nor any part thereof may be copied or reproduced in any form or by any means, or translated into another language, without the prior consent of SAP UCC TU Munich. The information contained in this publication may be changed without prior notice.

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Course Overview

The purpose of this course is to provide an introduction to the functionalities of SAP Business Information Warehouse. The participants learn the basics of reporting and gain an overview of data modeling and data loading. Not only are the rules for using BW in a UCC environment discussed, but the course also provides suggestions for possible user scenarios of SAP Business Information Warehouse.

Duration: 5 days

Target group: Instructors interested in using SAP BW in their coursesUsers of SAP BW in their courses with little previous knowledge

Dates: June 2006

Prerequisite: Basic Knowledge of Data Warehousing

Release: SAP BW 3.50

Course contents: Rules for using BW in a UCC environmentBasics of data warehousingArchitecture and tools of BWReporting & Web ReportingData ModelingData flow & data extractionAdministrationSuggestions for using SAP BW in the course

This course does not replace participation in follow-up SAP BW courses from SAP.


Europe


Germany


German Soccer Fans


Bavaria


Our SAP UCC at Munich


Selected Chapters

Data Warehousing and SAP BW Basics• 2.1 Introduction to DW• 2.2 System Handling• 2.3 Crash Course Reporting• 2.7 Business ContentBasic Reporting and Data Analysis• 3.1 Query Definition• 3.2 Exception Reporting

Modeling of Data Structures• 4.2 Stars & Galaxies• 4.3 InfoObjects• 4.4 InfoCubesData Staging I: Flat Files• 5.1 Staging Scenarios• 5.2 Master Data Staging• 5.3 Transaction Data Staging• (5.6 Transformations)

Advanced Reporting• 6.1 Geo Maps• 6.2 Web Reporting• 6.3 Data miningData Staging II: mySAP ERP Connections• 7.1 Transaction Data Extraction• 7.2 Delta Extraction

Monday

Tuesday

Wednesday


Course Schedule

Mon. 09:00 a.m. – 04:30 p.m.

Tues. 09:00 a.m. – 04:30 p.m.

Wed. 09:00 a.m. – 04:30 p.m.


• SAPLogonG48

• Client800

• UsersAUSER-3-XXwith XX = 01 to 30

• Initial PasswordINIT

• Please note your new password when you log on for the first time!

Logging On Made Easy


Naming Conventions

Self-defined objects are named according to the pattern AYXX…

• A TU Munich• Y=3 SAP BW course no.• XX Seat no./Team no.

• Example: A303Cube01 or A304Cube01

Guidelines on Using SAP BW


Introduction to Data Warehousing


Google Search Results Over Time

09/2002 03/2003 07/2003 02/2004 07/2004 02/2005 07/2005

“data ware-house”

451,000 574,000 650,000 1,780,000 1,840,000 4,450,000 2,560,000

“data ware-housing”

352,000 443,000 490,000 1,060,000 963,000 2,650,000 4,790,000

“business intelli-gence”

850,000 1,160,000 1,140,000 2,960,000 3,330,000 8,820,000 12,800,000

Source: www.google.de

Number of Search Results


Data Warehouse / Business Intelligence

Wider view of BI

DataProvision

DataAnalysis

Technology Application

Phase

Focus

Analysis oriented view of BI

Closer view of BI

Extraction,Transformation *

DataWarehouse *

Reporting *

Standard *

Ad-hoc *

OLAP *MIS/EIS *Text

Mining

Data Mining * Planning/Consolidation **

Analytical CRM

Performance Measurement Systems/ BSC Systems **

* covered by SAP BW ** covered by SAP SEM Views of Business Intelligence (BI) (Source: Kemper et al. 2004, p. 4)


Definitions for Data Warehouses

1. A data warehouse is a central repositoryfor all or significant parts of the data that an enterprise's various business systems collect.

2. A data warehouse is a copy of transaction data specifically structured for querying and reporting.

3. A collection of data designed to support management decision making. Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.


Inmon Definition: Data Warehouse

“A data warehouse is a subject-oriented, integrated, nonvolatile, time-variant collection of data in support of management’s decision.”

(Bill Inmon)

Constant data collection

Organization-wide integration of data

Period reference as a data component

Subject-oriented to organization issues


Considering Inmon’s Definition

subject-oriented Why be limited to customers, suppliers, products, etc.?

integrated Schema integration (metadata) and data integration are indispensable.

time-variant “Snapshot view” of historical data does not account for:- Current data (e.g. shares)- Constant data (master data)

Not necessary

Keep

Time dependency as one possibility of many

nonvolatile If consistency is guaranteed, the ban on updates can be lifted.

Not necessary

collection of data Of course… Keep

in support of management‘s decision

Data warehouses for managers only? Not necessary

A data warehouse is a physical dataset enabling an integrated view of the underlying DataSources.

Zeh, T. (2003). Data Warehousing als Organisationskonzept des Datenmanagements. Eine kritische Betrachtung der Data-Warehouse-Definition von Inmon. Informatik - Forschung und Entwicklung, 18(1), 32-38.


Data Warehouse: Expanded Definition

Creating at Reports

Creating Graphics

Calculating Tables

Analysis Methods

External Data External

Data

Operative Datan

Operative Data

Data Warehouse

Data Collection 1. Oriented by subject 2. Integrated 3. Constant 4. Time referenced D

W in

bro

ad s

ense

DW

in

narr

ower

sen

se


Three-Level DW Concept

Data Staging

Data Storage

Information Analysis


Structure of SAP BW

© SAP AG


Benefit Potential

Technical Benefits• Improved data integration• Decentral data checks no

longer necessary• Fast query handling• Relieves operative

applications• Flexible access options

Business Benefits• Improved information

staging• Early trend recognition• Prompt reaction to

environmental changes• Improvement in customer

satisfaction• Harmonization of

terminology


BW in SAP NetWeaver™

© SAP AG


SAP BW System Handling


Navigating SAP BW

• SAP Easy Access Menu

• Favorites• Transaction Codes

– Find– Enter– Combination with /o

and /n– Activate technical

names

© SAP AG


Help for SAP BW

• Field help (F1)• Input help (F4)• Help for error messages

• SAP Library• Glossary• http://help.sap.com,

SAP NetWeaver™ area• http://service.sap.com/bw

© SAP AG


UCC Guidelines


Crash Course Reporting


SAP BW Tools

AdministratorWorkbench (AWB)System administration

BEx AnalyzerStage andpresent reports

BEx BrowserManage and execute reports,portal function

Most Important Tools:

BEx Query DesignerDefine reports

Web Application DesignerCreate web applications


Business Explorer (BEx)

© SAP AG


Multidimensional Data Structures


Multidimensionality

Distribution Channel

Tim

e

Division

Other dimensions cannotbe displayed:

• Sales org.• Material• Sold-to party

Sales:2 M

Matrix element withkey figure(s)

Dimension/Characteristic


Characteristic or Dimension?

Dimension Lehrstuhl

DimensionVeranstaltung

Note:2,3

Matrixelement mit Kennzahl(en)

510A 510B 510C 510H

394435

577

Entwurf

Makro

SAP

InfoCube mit Dimensionen (klassisch)

Dimension Zeit mit demMerkmal Semester


mit den MerkmalenVeranstaltung und

Lehrstuhl

Note:2,3


SS 01WS

01/02SS 02

WS02/03

394435

577

Entwurf(510H)

Makro(510B)

SAP(510H)

InfoCube mit Merkmalen in Dimensionen (SAP BW)


What Does Multidimensionality Mean?

• Multidimensionality is a main characteristic of data in DWs

• No presentation of data in tables

• As many criteria (dimensions/characteristics) for analyses as you want

• Data descriptions as accurate and detailed as possible

• Often illustrated as a data cube


Analysis Techniques

• To answer users' detailed questions, the multidimensional data model offers various types of operations for manipulating the data cube.

• Mainly, you can change the dimensions and summary levels and can navigate in the data space.

• These options for analysis can be accessed in BEx Analyzer through the shortcut menu in the result area, for example, and can be forwarded to the OLAP processor. The processor interprets the analyses and applies them to the dataset.


Slicing, Dicing & Co.

• Pivoting means turning the data cube• Slicing means filters are set to create a “slice” of data • Dicing means creating a “smaller” data cube by slicing

an interval• Drilldown generally means adding information to a

report• Roll up = opposite of drilldown• Drill across is when the x- and y-axes are switched• Some data warehouse systems provide the option of

reporting on data that is not in the warehouse but is stored only in the OLTP systems. One example of this might be individual accounting documents. This capability is called Drill Through.

Based on http://miss.wu-wien.ac.at/~info1/stud/dw/main.html


Query Areas

© SAP AG


Data Warehouse Lifecycle


The Business Dimensional Lifecycleas Course Structure

time

by R. Kimball, modified

Bus

ine

ss R

equ

irem

ents

D

efin

ition

Pro

ject

Pla

nnin

g

Technical Architecture

Design

Product Selection & Installation

Dimensional Modeling

Physical Design

Data Staging Design &

Development

End-user Application

Specification

End-user Application

Development

Dep

loym

ent

Man

agem

ent

& G

row

th

Rep

lace

men

t

Project Management

technically oriented lessons

management oriented lessons


Project Planning & Management

• Project definition and scoping

• Development of Project Plan

• Parties involved

Bus

ines

s R

equi

rem

ents

D

efin

ition

Technical Architecture Design


Dimensional Modeling Physical DesignData Staging Design

& Development

End-user Application Specification

End-user Application Development

Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Pro

ject

Pla

nnin

g


Business Requirements Definition

• Gathering requirements

• Define terminology

Pro

ject

Pla

nnin

g




& Development



Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition



• Introducing architecture• Back room technical architecture• Architecture for the front room• Infrastructure and metadata

Pro

ject

Pla

nnin

g



& Development



Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition




• Evaluating products• Choosing a product• Features of SAP BW• Installation procedure

Pro

ject

Pla

nnin

g


& Development



Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition





• Designing dimensional models• Semantic, logical and physical data models• Fact table grain• Special fact types (non/semi-additive)• Specialities in modeling

Pro

ject

Pla

nnin

g



Physical DesignData Staging Design

& Development



Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition



Physical Design

• Physical structures necessary tosupport logical database design

• Naming standards• Physical file locations• Setting up database environment• Indexing• Partitioning

Pro

ject

Pla

nnin

g



Data Staging Design & Development



Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition

Dimensional Modeling Physical Design


Data Staging Design and Development

• Extract, transform and load• Data quality• Initial population load• Regular, incremental loads

Pro

ject

Pla

nnin

g





Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition


& Development


End-User Application Specification & Development

• Standard vs. user-defined reports

• Geovisualization

• Web Reporting

Pro

ject

Pla

nnin

g





Dep

loym

ent

Man

agem

ent &

Gro

wth

Rep

lace

men

t

Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition


& Development


Deployment, Management & Growth, Replacement

• User support structures• Training measures• Performance metrics• Replacement

considerations

Pro

ject

Pla

nnin

g





Project Management

Bus

ines

s R

equi

rem

ents

D

efin

ition


& Development

Dep

loym

ent

Man

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Rep

lace

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Pro

ject

Pla

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Project Management

Bus

ines

s R

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rem

ents

D

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ition


& Development

Dep

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ent

Man

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Rep

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Course Example

Strengths and Weaknesses of Data Warehouse Products

ETL (Flatfiles, R/3), ABAP™

Formulas, Web Reporting, Maps

Extended Star Schema, Snowflaking, Factless Fact Tables, Galaxy

Project Team Role Play

Interviews, Questionnaires

End Users, Work Places, Key Words, Training Concepts

Investment Appraisal,

License Costs

Client Server Architecture,SAP Web AS


Data Warehouse Project Planning


Preparing the Organization

• Find sponsor

• Find technical justification

• Perform a feasibility study

• Relationship between specialized departments and IT

• Understand analytical working methods


Project Scope

• What content should be considered?

• What organizational units are involved?

• Type of data

• Time frame

• Budget


Project Scope

Regions

Fun

ctio

ns

Depar

tmen

ts


Justification

• DW project is not a purpose in itself• Cost-benefit analysis• Measurement of costs?• Measurement of benefits?


Project Team

• Involvement of many organizational units

• Different professional disciplines

• Distribution of roles

• Availability of project team members


Project Plan

• Planning• Controlling• Checking Fans Front Office Coaches Regular Line-Up Special Teams

Project Task Bus

ine

ss E

nd U

sers

Bus

ine

ss S

pons

or

IS S

pons

or

Bus

ine

ss D

river

Bus

ine

ss P

roje

ct L

ead

Pro

ject

Man

ager

Bus

ine

ss S

ys.

Ana

lyst

Dat

a M

odel

er

DW

DB

A

Dat

a S

tagi

ng

De

sig

ner

DW

Ed

ucat

or

E/U

Ap

pl'n

De

velo

per

Tec

h/S

ecur

ity A

rch

itect

Tec

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uppo

rt S

pec

ialis

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Dat

a S

tagi

ng

Pro

gra

mm

er

Dat

a S

tew

ard

DW

QA

An

alys

t

PROJECT MANAGEMENT AND REQUIREMENTS

PROJECT DEFINITION

1 Assess Data Warehousing Readiness 2 Develop Preliminary Project Scope 3 Build Business Justification

PROJECT PLANNING & MANAGEMENT

1 Establish Project Identity 2 Identify Project Resources 3 Prepare Draft Project Plan 4 Conduct Project Team Kick-Off & Planning 5 Revise Project Plan 6 Develop Project Communication Plan 7 Develop Program to Measure Success 8 Develop Process to Manage Scope 9 Ongoing Project Management

USER REQUIREMENT DEFINITION

1 Identify and Prepare Interview Team 2 Select Interviewees 3 Schedule Interviews


OLTP and OLAP Systems


OLTP Systems

Operative Systems

Data Warehouse ...

Reserva-tion

System

OrderProcessing ...

Reserva-tion

System

OrderProcessing

Per-sonnelAdmin.

Per-sonnelAdmin.

Source: based on http://www.educeth.ch/informatik/vortraege/olap/docs/olap.ppt


Differences Between Transaction-Oriented and Analysis-Oriented Systems

Transaction-Oriented SystemsOperative Systems

Analysis-Oriented Systems

Less frequent, complex queries

Large amounts of data per query

Frequent, simple queries

Small amounts of data per query

Quick calculations importantQuick updates important

Executing OLAP queries of operational datasets in parallel could limit the performance of OLTP applications

Operate with current and historical dataOperate mainly with current data

Database system cannot be optimized for OLTP and OLAP applications at the same time

OLTP(Online Transaction Processing)

OLAP(Online Analytical Processing)

Source: based on http://www.educeth.ch/informatik/vortraege/olap/docs/olap.ppt


OLTP vs. OLAP

OLTP OLAP

Target Efficiency through automation Competitive advantages through knowledge generation

Data Content Application-oriented, function-oriented

Subject-oriented

Type of Data Transaction data Aggregated data

Age of Data Current, up-to-date: 30-60 days Historical (often 8-10 years old), current, future

Data Volume Low Comprehensive

Main Functionality Frequent changes Time-dependent reports

Data Integration Minimal integration with other applications

Integrated data from many applications

State-of-the-Art for Database Systems

Relational databases Relational and multidimensional databases

Data Model Normalized (often 3rd normal form) Denormalized data model

Semantic Modeling Method

Entity Relationship Model Multidimensional ERM:

Permitted Operations on the Dataset

Insert, update, delete, read Read


Multidimensionality


Data Warehouse Products


DW Solutions and Providers

http://www.barc.de

Manufacturer Product Version

Ascential DataStage 6.0

Business Objects Data Integrator 6.0

Cognos DecisionStream 7.1

Hummingbird ETL 5

IBM DB2 Warehouse Manager 8.1

Informatica PowerCenter 6

Microsoft SQL Server 2000

MicroStrategy 7i 7i

NCR Teradata V2 R5.0

Oracle 9i 9i R2

Sagent Solution 4.5

Sand Analytic Server 3.0

SAP Business Information Warehouse 3.1 C

SAS System 8

Sybase IQ 12.4.3


OLAP Providers and Products

1. Hyperion Solutions (Essbase, Wired)

2. Oracle (Express)

3. Cognos (PowerPlay)

4. MicroStrategy (MicroStrategy)

5. Microsoft (OLAP server)

6. Business Objects(Business Objects)

* Source: The OLAP Report (www.olapreport.com)

The six OLAP providers with the largest market share in 1999*:

Other OLAP servers:

• IBM (DB2 OLAP Server)

• Applix (iTM1)

• ...

Other OLAP frontends:

• Temtec (Executive Viewer)

• ...

Other OLAP providers:

• Brio Technology

• Pilot Software

• SAS Institute

• ...


What's Being Said About SAP BW

• Especially suited for analyzing and presenting data stored in a DW

• Good tools for creating individual applications (BEx Analyzer, Web Reporting)

• Predefined information models (Business Content)

• Theoretically not dependent on R/3

• The structures are based in part on R/3 business processes

• Business Content is oriented toward R/3 structures

• Optimized performance in coordination with R/3 (special extractors, and so on)


Business Content


Why Have Preconfigured Information Models?

• Modeling data models to specific requirements is a tedious and sometimes highly complex task.

• The time and effort needed are even greater the more individual the requirements are and the less the developers can access and refer to existing templates.

• Organizations often model the same subjects.


Contents of Business Content

© SAP AG

© SAP AG, Marianne Kollmann, Product Management BI


Contents of Business Content

© SAP AG

© SAP AG, Marianne Kollmann, Product Management BI


Definition of Business Content

• Business Content is a comprehensively prefabricated information model for analyzing business processes.

• Components of these models include:– Extractors in SAP R/3– Elements of the data model (such as key figures,

characteristics, InfoCubes, and ODS objects)– Components for the data loading process (such as

InfoSources and update rules)– Reporting components (such as queries, web

templates, and workbooks)– Basic components (such as roles and currency

conversion types)


Number of SAP BW Business Content Objects

BI Content 3.2 Add-On

InfoObjects 11,772 ODS objects 349 InfoCubes 605 MultiCubes 121 Roles 861 Queries 3,299 Workbooks 1,979


Working with Business Content

BusinessContent

Usewithout

adjustments

Refiningor

Coarsening

Template foryour own

Business Content


Business Content Versions

• In BW, there are three different object versions of Business Content:– D version: SAP delivery version– A version: active version– M version: modified version

• To work with Business Content objects, you first have to convert them into active versions (A versions).


Searching for the Right Business Content

1. Business Content can be searched in the Metadata Repository.

2. The Metadata Repository is integrated into AWB.

Questions:

• What is metadata?

• What is AWB?

• What is the Metadata Repository?


SAP BW Tools

AdministratorWorkbench (AWB)Systemadministration

BEx AnalyzerStage and present reports

BEx BrowserManage andexecute reports,portal function

Most Important Tools:

BEx Query DesignerDefine reports

Web Application DesignerCreate web applications


Administrator Workbench

© SAP AG


What is Metadata?

• Metadata is information on the data structures and their relationships, “data about data”


Technical and Specialist Metadata

• Technical Metadata contains information about the data warehouse required by DW administrators and designers to develop and operate the data warehouse. This includes data such as database fields, columns, tables, and the memory needs of the database, data models, and mappings.

• Specialist Metadata contains the information that gives a specialized user a business overview of the data warehouse. This includes data such as mappings, report details, specialist terms, and so on. Specialist metadata assigns data from the DW to the multidimensional business model and to the frontend tool of the end user and usually contains descriptions and hierarchies that are internal to the organization.


Meta Database System

• Help system for users

• BW Metadata Repository: central management of all metadata

• BW Metadata Repository Browser: convenient access to all metadata


Common Warehouse Metamodel (CWM™)

“The CWM™ is a specification that describes metadata interchange among data warehousing, business intelligence, knowledge management and portal technologies.”

From: www.omg.org/cwm

• Object Management Group (OMG)http://www.omg.org

• Common Warehouse Metamodelhttp://www.omg.org/cwm– Specification– Articles and links


Query Definition


InfoProviderQuerydefinition liefertDaten 0,n1,1

Arbeitsmappe(xls-Datei)

einge-betteteQuery

0,n0,m

alsView

speich-ern

View

MetaObjects: Query


InfoProvider as Basis for a Report I

InfoCube ODS Characteristic with Master Data InfoSet Virtual Cube MultiProvider

InfoCube ODS Characteristic with Master Data

InfoProvider DataTargets

Reporting

Loading Data


InfoProvider as Basis for a Report II

© SAP AG

No

Dat

a

Wit

h D

ata

Master Data

Basic InfoCube

MultiProvider

InfoSet

Info

Pro

vid

er Interface

ODS Object

OLAP Engine

Business

Explorer

Virtual InfoCube


BEx Toolbar

© SAP AG


Query Designer Toolbar

© SAP AG


Defining Queries Using Drag & Drop

© SAP AG


Defining a Formula

© SAP AG


Some Important Elements of a Query Definition

• Insert characteristics• Insert key figures• Free characteristics• Filter characteristics• Properties of

characteristics• Formula key figures

© SAP AG


Summary: Query Definition Procedure

To define queries, you:

1. Select an InfoProvider that the query is being defined for2. Select reusable structures that already contain combinations of characteristics or key figures (such as

a contribution margin scheme)3. Select characteristics from the InfoProvider4. Limit the selected characteristics to characteristic values, characteristic intervals, or hierarchy nodes5. Use variables for characteristic values, hierarchies, hierarchy nodes, formulas, and texts, and define

new variables if necessary6. Select key figures from the InfoProvider7. Formulate calculated key figures8. Limit the key figures by combining characteristics9. Define exception cells10. Arrange the characteristics and key figures in rows or columns to determine a starting view

for the query analysis

The steps not in bold print are optional.

You can save the query in your Favorites or in your role. You can then analyze the query data inBusiness Explorer. You can• Display the query with a click in the web in a standard view• Use the query as a data provider for web items and analyze the query data in a separately designed

web application OR• Place the query in a workbook and analyze it in BEx Analyzer (MS Excel-based)

Source: SAP BW Functions in Detail, Version 1.0


Exception Reporting


Exception Reporting: Process

© SAP AG


Step by Step

1. Define the exception

2. Output: highlighted in color in the query worksheet

3. Define Reporting Agent settings

4. Schedule

5. Output: alert Monitor and messages


Semantic Data Modeling


ARIS Model

Source: Scheer, Wirtschaftsinformatik [Business Information Management]


OLTP vs. OLAP

Data Modeling Methods for Transaction-Oriented Databases (OLTP)

• Semantic level: ERM• Logical level: relations model• Physical level: description of

relational database systems

Data Modeling Methods for Data Warehouses (OLAP)

?


Selected Design Methods

Entwurfsebene Entwurfsmethoden Konzeptueller (semantischer) Entwurf

Semantisches Data Warehouse Modell Multidimensionales ERM Dimensional Fact Modeling Application Design for Analytical Processing Technologies

Logischer Entwurf Starschema Erweitertes SAP-Starschema Fact/Constellation Schema Galaxy Schema Snowflake Schema Partial Snowflake Schema

Physischer Entwurf Speicherungsstrukturen Zugriffsmechanismen Datenbanktuning usw.


Multidimensional ERM (MERM)

• Derived from ERM

• New: fact relationship, dimension field, hierarchical relationship

• Principle of minimalism

• So there are only five Meta Objects:

Name

Central Fact Relationship

Name

Dimension Field

Name

Variable or Attribute

Hierarchical Relationship Relationship


From ERM to MDM

Mapping transaction structuresas analytical structures


Three-Step Method

Step Activity Description 1 Identify business processes Split ERM into one or more business processes

2 Generate fact relationship n-m-relationships between strong entities

provide the fact relationship, numerical attributes are candidates for key figures

3 Form dimensions Remaining entities are summarized intogroups that are strongly dominated by other entities


Overlapping Entity: Example

Customer

Material Sales Person

Material group Sales Department

Customer noCustomer nameCityRegion

Material noMaterial nameMaterial type color price

Material group noMaterial group name....

Sales TransactionDateCustomer noMaterial noSales pers noAmountQuantityCurrency

Sales pers. noSales pers. name.......

Sales dep. noSales dep. location.......

© SAP AG


Forming Dimensions

Sales Rep ID

LastNameSalesDep

Material ID

Material NameMaterial TypeMaterial Group

Customer ID

Customer NameCityRegionOffice Name

Time Code ID

YearFiscal YearQuaterMounthDay of the Week

Material IDSales Rep IDTime Code IDCustomer IDSales AmountQuantityUnit Price

Time DimensionCustomer Dimension

Sales Org DimensionMaterial Dimension

FACT??

Customer

City

Region

Material Group

Sales order

Price

Sales Person

Sales Dept.

Sales Dept. Loc.

Material

Material TypeColor

© SAP AG


Granularity

• = Details of a data structure

• High granularity: data is described by many characteristics

• Low granularity: data is described by few characteristics

• Positive effect on options in the query

• Negative effects on performance of requests and load time


Granulartity


Logical Data Modeling


Selected Design Methods

Entwurfsebene Entwurfsmethoden Konzeptueller (semantischer) Entwurf

Semantisches Data Warehouse Modell Multidimensionales ERM Dimensional Fact Modeling Application Design for Analytical Processing Technologies

Logischer Entwurf Starschema Erweitertes SAP-Starschema Fact/Constellation Schema Galaxy Schema Snowflake Schema Partial Snowflake Schema

Physischer Entwurf Speicherungsstrukturen Zugriffsmechanismen Datenbanktuning usw.


Physical Conversion in the Data Warehouse System

Physical Multidimensional Data Warehouse Systems

• Database and storage structures are multidimensional

• There is no recognized standard yet

• Large datasets are problematic• Examples: Express (Oracle),

Holos (Seagate), Essbase (Applix)

Physical Relational Data Warehouse Systems

• Data classified in fact tables and dimension tables

• Connected by keys• Example: SAP BW


Classic Star Schema

• Very effective requests can be made in the Star Schema

• It is very easy to understand

• Flexibility?


Star Schema

Kennzahlen

Faktentabelle

Dimensions-attribute

Dimension 2


Dimension 1


Dimension 3


Dimension 4


Problems with the Classic Star Schema

• No support for multiple languages

• Alphanumeric foreign keys

• No support for time-dependent master data

• Hierarchy relationships have to be modeled as attributes of a dimension table


SAP's Expanded Star Schema

• Fact table is unchanged • Dimension characteristics are separated into segments– Attributes– Texts– Hierarchies

• Attributes and texts can be defined time-dependently

• Segments are optional and need not be created

• Introduction of SID


• Solution-Dependent Data:Fact Tables and Dimension Tables

Solution-Dependent and -Independent Data

• Solution-Independent Data:Characteristics

G e b ie t 1 G e b ie t 2 G e b ie t 3

B e z irk 1

G e b ie t 3 a

B e z irk 2

R e g io n 1

G e b ie t 4 G e b ie t 5

B e z irk 3

R e g io n 2

G e b ie t 6

B e z irk 4

G e b ie t 7 G e b ie t 8

B e z irk 5

R e g io n 3

V e rtr ie b s o rg a n is a t io n

M a t e r i a l G r o u p

M a t e r i a l H i e r a r c h y T a b l e

M a t e r i a l N u m b e rL a n g u a g e C o d e

M a t e r i a l N u m b e rL a n g u a g e C o d e

M a t e r i a l N a m e

M a t e r i a l T e x t T a b l eM a t e r i a l _ D i m e n s i o n _ I D

M a t e r i a l N u m b e r

M a t e r i a l D i m e n s i o n T a b l e

M a t e r i a l M a s t e r T a b l e

M a t e r i a l N u m b e rM a t e r i a l N u m b e r

M a t e r i a l T y p e

M a t e r i a lM a t e r i a l D i m e n s i o n D i m e n s i o n

© SAP AG


Surrogate ID (SID)

• Artificial primary key

• 4 byte whole number

• Technical link between InfoCube and characteristic

• Technical link between characteristic and corresponding attribute, text, and hierarchy tables

• Technical key instead of production key


SID Tables

Text

SID Tables

Master

Hierarchies

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Hierarchies

Master

SID Tables

Text

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

Text

SID Tables

Master

Hierarchies

DimensionTable

Text

SID Tables

Master

Hierarchies

DimensionTable

DimensionTable

DimensionTable

DimensionTable

Hierarchies

Master

SID Tables

Text

FACT

© SAP AG


SID Tables

SID Tables andInfoCube Access

(1) Fact Table(1) Fact Table

(2) Dimension Tables(2) Dimension Tables

(3) time-independent-SID(3) time-independent-SID(4)(4) time-dependent-SIDtime-dependent-SID(5) ‘traditional‘ SID (5) ‘traditional‘ SID

11

22

22

22

22 3 3

55

4 4

3 3

5555

55

55

55

55

55

55

3 3 3 3

55

55

5555

4 4

3 3

55

55

55

55

© SAP AG


Working with InfoObjects


InfoObjects

• e.g. customer, product

InfoObject: Key Figure• e.g. sales, costs• numerical and

additive if possible

InfoObject: Characteristic


Kennzahlen

Faktentabelle


Dimension 2


Dimension 1


Dimension 3


Dimension 4

From Star Schema to InfoObject

TextsAttributes

Hierarchies

• Every field of a dimension becomes a characteristic

– Exception because of expanded Star Schema: texts, attributes, hierarchies are placed in their own segments

• Each key figure of the fact table becomes a key figure


Important Properties of Characteristics

• Name• Data type• Length• Master data

– Texts– Attributes– Hierarchies

© SAP AG


Key Figures: Data Types

From: http://www.dpunkt.de/leseproben/3-89864-179-1/Kapitel_6.pdf


Characteristics: Data Types

From: http://www.dpunkt.de/leseproben/3-89864-179-1/Kapitel_6.pdf


Texts and Attributes: Fields

Texts• Short: 0TXTSH• Medium: 0TXTMD• Long: 0TXTLN

Attributes• Each attribute of

a characteristic InfoObject is an InfoObject itself (characteristic or key figure)


Process Flow for InfoObject Creation

1. Create InfoObject

2. Check: syntax formula of the InfoObject checked

3. Save: definition saved

4. Activate: database tables generated


Working with InfoCubes


InfoCubes

• Central data store in SAP BW

• Compiled from characteristics and key figures

• 233 key figures max.

• Approx. 3,224 characteristics possible


MetaObjects: InfoProvider

InfoProviderQuerydefinition liefertDaten 0,n1,1

Arbeitsmappe(xls-Datei)

einge-betteteQuery

0,n0,m

alsView

speich-ern

View


InfoCube Structure

Dimension Lehrstuhl


Dim

ensio

n St

uden

t

Note:2,3


510A 510B 510C 510H

394435

577

Entwurf

Makro

SAP

InfoCube mit Dimensionen (klassisch)

Dimension Zeit mit demMerkmal Semester


mit den MerkmalenVeranstaltung und

Lehrstuhl

Dim

ensio

n St

uden

t

mit

dem

Mer

kmal

Stu

dent

Note:2,3


SS 01WS

01/02SS 02

WS02/03

394435

577

Entwurf(510H)

Makro(510B)

SAP(510H)

InfoCube mit Merkmalen in Dimensionen (SAP BW)


InfoCube Step-by-Step

1. Create InfoCube

2. Add key figures

3. Add characteristics

4. Create dimensions

5. Sort characteristics into dimensions

6. Check, save, activate


Line Item and High Cardinality

Line Item:Very few characteristic values of the InfoObject e.g. order numberin an order (detail) cube No dimension table, InfoObject integrated directly into the InfoCube

High Cardinality:Many entries in this dimension (min. 20% of the number of data records of the fact table) Different indexing

© SAP AG


What Can Be Documented?

© SAP AG


Documentation

• Possible Formats– Text (.TXT) – HTML – MS Word (.DOC) – MS Power Point (.PPT) – MS Excel (.XLS) – GIF – JPG


Staging Scenarios


Staging Scenarios

• Staging scenarios with transient data store

• Data is always collected anew and only kept in the BW system for the duration of a given transaction.

• Staging scenarios with persistent data store

• Data loaded from the source system into the SAP BW system is stored even after a transaction has been concluded.


Staging Scenarios: Overview

Transient Data Store

Persistent Data Store

InfoCube/ODS RemoteCube

Source System RemoteCube

Source System PSA InfoCube ODS InfoCube

Source System PSA ODS

InfoCube InfoCube

InfoCube Source System PSA ODS

Staging Scenarios


Transient Staging Scenarios with RemoteCubes

© SAP AG


Persistent Staging Scenario

SourceSystem

PSA

InfoCube

InfoObjects(Characteristics)


Flatfiles as Source System

• Source systems are all systems that stage data for SAP Business Information Warehouse (BW). These include:– SAP Systems (Release 3.0D and higher)– SAP Business Information Warehouse systems– Flatfiles, in which the metadata is maintained

manually and the data is transferred using a data interface to the SAP BW system

– A database system, in which data is loaded from an SAP-supported database without the assistance of an external extraction program

– External systems in which data and metadata is transferred using staging BAPIs

From: BW online documentation


Flexible Master Data Staging

Flexible Master Data Staging


Data in SAP BW

Data in BW

Metadata Application Data

Specialist Metadata

Technical Metadata

Transaction- Data Master Data

Attributes Texts Hierarchies


Hints on Loading from Flatfiles

• Do not use titles if possible.Headers may be ignored during the loading process.

• The order of the fields in the file must match the order of the InfoObjects in the transfer structure.

• Date: YYYYMMDDTime: hhmmss


Structure of Attribute Flatfiles

/BIC/<ZYYYYY> Key of the compounded characteristic (if characteristic is available)

/BIC/<ZXXXXX> Characteristic key

DATETO CHAR 8 Valid-to date (only with time-dependent master data)

DATEFROM CHAR 8 Valid-from date (only with time-dependent master data)

Attribute 1

Attribute …


Structure of Attribute Flatfiles

Key Compound-ing

Datefrom

Dateto

Attribute 1

Attribute 2

...

optional optional optional

Fields relevant for case study


Structure of Text Flatfiles

LANGU CHAR 1 Language key (D for German, E for English)

/BIC/<ZYYYYY> Key of the compounded characteristic (if characteristic is available)

/BIC/<ZXXXXX> Characteristic key

DATETO CHAR 8 Valid-to date (only with time-dependent master data)

DATEFROM CHAR 8 Valid-from date (only with time-dependent master data)

TXTSH CHAR 20 Short text

TXTMD CHAR 40 Medium text

TXTLG CHAR 60 Long text


Structure of Text Flatfiles

Key Comp-ounding

Datefrom

Dateto

Shorttext

optional optional optional

Med. text

Longtext

Lang-uage

Fields relevant for case study


Update Types

Flexible Updating• Transaction data• Master data

= with update rules

(= transaction data-InfoSources in BW-Release 2.X)

Direct Updating• Master data only

= without update rules

(= master data-InfoSources in BW-Release 2.X)

Easier, therefore preferable if update rules require no transformations


Data Flow for Flexible Updating

© SAP


Update Rules

• Update rules specify how data (key figures, time characteristics, characteristics) are updated from an InfoSource communication structure to the data targets.

• They combine an InfoSource with an InfoCube, characteristic or ODS object.

• For InfoCubes, there are two ways of defining the update rule for a key figure: no update or addition, minimum or maximum. Characteristics can also be looked up in external tables such as a master data table.


Loading Master Data Step by Step

1. Enter characteristic as data target

2. Define InfoSource for master data

3. Assign source system and DataSource(s)

4. Maintain transfer structure and transfer rules

5. Create update rule

6. Create and schedule InfoPackage


Define InfoSource

• An InfoSource describes the amount of all available data for a business process or a type of business process. An InfoSource is a unit of logically associated information, that is of InfoObjects and, when transfer rules are applied, can refer to data from one or more DataSources. The structure of an InfoSource is called the commu-nication structure. In contrast to the DataSource transfer structure, it is independent of the source system.


Assign DataSource(s)

• Logically associated data are found in the source system in the form of DataSources. DataSources are also source system-referenced. They comprise many fields, which are offered for the data transfer to BW in a flat structure (extract structure). Data is transferred from the source system to BW in the form of a selection of fields of the extract, transfer structure.


Transfer Rules

• Transfer rules determine how and which fields in the source system-dependent transfer structure are transferred to which fields in the source system-independent communication structure. Detailed transformation rules can be generated for this purpose.


Transfer Rules

TransferRules

Write field in field Assign constantvalue

ABAP routine

Formula

© SAP AG


Data Flow

1

2

3 4 5

© SAP AG

6 7


Create and Schedule InfoPackage

• Data request

• Includes diverse parameters for the upload

• Can be planned and scheduled by job administration


Monitor

• Monitor is the monitoring tool in Administrator Workbench.

• Use Monitor to supervise data requests and data processing in Administrator Workbench. The status of the data processing is

displayed on the different levels of the detailed display.

© SAP AG


PSA

• The Persistent Staging Area (PSA) is the initial storage area of SAP BW for requested data from different source systems. The requested data is stored unchanged in the form of the transfer structure in transparent, relational database tables, and so may have errors if it had errors on the source system. Logical data packages (requests) can now be checked for quality and meaningfulness, order and completeness.

© SAP AG


Loading Transaction Data


Data in SAP BW

Data in BW

Metadata Application Data

Specialist Metadata

Technical Metadata

Transaction- Data Master Data

Attributes Texts Hierarchies


Structure of Transaction Data Flatfiles

Char. 1

Char. 2

Char. n...

Key fig. 1

Key fig. 2

Key fig. n...

Characteristics Key Figures

• Maintain a consistent order• Do not use titles if possible• Give dates in the YYYYMMDD format


Update Rules

• Update rules specify how data (key figures, time characteristics, characteristics) are updated from an InfoSource communication structure to the InfoCubes.

• They combine an InfoSource with an InfoCube, characteristic or ODS object.

• For InfoCubes, there are two ways of defining the update rule for a key figure: no update or addition, minimum or maximum. Characteristics can also be looked up in external tables such as a master data table.


Loading Transaction Data Step by Step

1. Define InfoSource for the transaction data (flexible update)

2. Assign DataSource(s)

3. Maintain transfer structure and transfer rules

4. Maintain update rules

5. Create and schedule InfoPackage


Copying InfoCubes


Copying Cube Structure

Copying Data

Cube Copy: Concept

Cube A

Export DataSource

InfoSource Update Rule

Cube B


Export DataSources

Export DataSources are needed to transfer data from a source BW to a target BW.

The selected InfoProvider is available for you to use as an InfoSource for another system. The corresponding export DataSource is not displayed in the InfoSource tree of the source BW.

The metadata of an export DataSource is generated as it exists in the source BW. This also includes the procedure for non-SAP systems.

Procedure:See document BW_Richtlinie03_Kopieren.doc

(BW_Guidelines03_Copying.doc)

The technical name of the export DataSource consists of the number 8 and the name of the data target. Example:

InfoCube: AYXX_EKFExport InfoSource: 8AYXX_EKF

Source: http://help.sap.com/saphelp_bw31/helpdata/de/ad/6b023b6069d22ee10000000a11402f/frameset.htm


InfoSpokes and Open Hub Service


Open Hub Service

Open Hub Service makes it possible to distribute data from an SAP BW system to non-SAP data marts, analytical applications, and other applications. This guarantees a controlled distribution across several systems. The central object for exporting data is the InfoSpoke. The InfoSpoke is used to define what data should be taken from which object, and to which target that data should be forwarded.

Source: http://help.sap.com/saphelp_bw31/helpdata/DE/a8/6b023b6069d22ee10000000a11402f/frameset.htm


Transformations During Data Load


Transformations in Data Flow

Communication Structure

DataSource DataSource

InfoSource Communication Structure

InfoSource

DataSource DataSource DataSource DataSource

Application - Spec.

Extractor: FI Application -

Spec. Extractor: CO

Generic Extractor

From Table

Update Rules

T ransfer Rules Homogenization: transforms

data data into structured and einheitliches uniform format

Integration of data in application-specific models


Transformations in Transfer Rules

field 1:1 (no transformation) formula

constant ABAP routine


Formula


ABAP Routine


Geovisualization


Pictures Say More......


... Than a Thousand Words


Geo-Characteristics

• Many of the BW characteristics, such as customer, sales region, state or country, also have geographical significance.

• You can evaluate geographic information together with the business-relevant key figures in BEx Map.

• BEx Map is the geographical information system (GIS) of BW that is integrated in Business Explorer (BEx).


Creating Maps Step by Step

1. Identify characteristic as geographically relevant

2. Load geographic data into BW

3. Insert BEx Map into the query


Step 1: Identifying the Geo-Characteristic

• First, you need to identify the geo-relevant characteristics (such as region) as geo-characteristics in InfoObject maintenance.


Static and Dynamic Geo-Characteristics

• Static Geo-CharacteristicA static geo-characteristic is a characteristic that describes a surface (polygon) and whose geographic coordinates do not change often. Countries or regions are examples of static geo-characteristics.

• Dynamic Geo-CharacteristicA dynamic geo-characteristic is a characteristic that describes a place (point-like information) whose geographic coordinates could change frequently. Customers or plants are examples of dynamic geo-characteristics, since they are located at a geographic “point” that can be described by an address, and the address data of these characteristics may change frequently.


Step 2: Loading Geographic Data into BW

• This will load the maps available as shape files into the BW system and assign them to the respective characteristic.

• A shape file is a standard file that is commonly used to describe geographic data and which is used in many geographical information systems. Detailed shape files also may contain demographic information about social structure, age structure, and so on, but may be very expensive. However, simple shape files are available on the internet and can often be downloaded free of charge. A simple shape file containing the structure of the German states will be used in this course.


Step 2: Loading Geographic Data into BW

1. Download “geo-data”: Downloads the master data of the characteristic. Important: SAPBWKey

2. Open the DBF file of geo-data and enter the SAPBWKey

3. Upload modified shape files


Shape File Structure

• The map on which you can display static geo-characteristics is provided as a shape file.

• The shape file consists of three files in different formats that belong together: *.shp contains the actual geo-data that forms the map *.shx contains an index, which improves access time to the map *.dbf contains the attributes for individual geographic items such as

countries or regions • You copy the SAPBWKEY from the geo-data file for your InfoObject into

the dbf file within the shape file

Master Data OREGION

010203...

BavariaBremenHamburg...

Region: *.dbf

...

...

...

...

BerlinHamburgBavaria...

050301...


From Shape File to Map


Step 3: BEx Map

• Finally, define a query with geo-characteristics and insert it into a workbook. After you attach a map, it displays query data of geographical relevance. You can navigate through the map to further evaluate geo-relevant data.


Web Reporting


Advantages of Web Reporting

• Constant availability and access

• Access to information on intranet and internet

• Dispenses with complex software installations

• Intuitive operation

• Many users have experience with web browsers

• Robust navigation in web browsers


Approaches for Web Reporting

1. Offline Approach– Querying the report data periodically– Storing the data on the web server as static HTML documents SAP BW: Reporting Agent

2. Dynamic Generation– Webpages are generated at the request of users SAP BW: Embeds items in web applications

3. Applets– Java or ActiveX Applets allow you to program and generate more

sophisticated interfaces SAP BW: JavaScript


Web Application Server Architecture

Web Browser

HTTP

ITS Web Serv.

ITS

• Mainly used for web-enabling of existing SAP applications

• Dynpro-based

• SAP BW used ITS only as a gateway (WEBRFC)

• ITS Flow Logic was used only in special cases

mySAP WAS

• Enhanced scalability, performance, and robustness

• Generation of charts and maps on Internet Graphic Server (IGS)

• Supports background processing

• BEx Mobile Intelligence

• Easy administration

SAP BW 3.0 with mySAP Web

AS technology

IGS

SAP BW 2.0

© SAP AG


Web Application Designer: Overall Architecture

SAP BW Server (using mySAP Web

App Server Technology)

HTMLTemplates

Data-base SAP BW Charting EngineSAP BW Charting Engine

Any Web Design Tool

OLAP ProcessorOLAP Processor

Portal/Web Browser

http

• Save HTML templates in Web Content Management

• Generate URL automatically

BEx Web Application

Designer

SAP BW Web Service

SAP BW Web Service

BEx Query Designer

(Excel-based, Windows-based,

Web-based)

createQueries/

Views

© SAP AG


Web Application DesignerAvailable web

itemsMultiple documents Web item

propertiesDrag&Drop

© SAP AG


Dependencies

The range of functions of analysis in Web Applications is dependent on which Web Browser you use.

Requirements for Unrestricted Range of FunctionsYou can have the complete range of functions with shortcut menus, snippet operations, and an expanded function toolbar for maps only if the current web browser supports DOM Level 2(with dynamic generation of DOM objects), ECMA-262 Script, HTML 4.0, and CSS 1.0.The reference web browsers are Microsoft Internet Explorer (MS IE) andNetscape Navigator (NS) in the current versions of Windows (MS IE 6.x and NS 6.x).Versions of these web browsers on other systems, such as Apple Macintosh or Linux may deviate in performance.

Minimum RequirementsYou can use Web Applications on web browsers that satisfy the HTML 3.2 standard and supportelementary functions of CSS 1.0.

Web Browser and Range of FunctionsIf you use Internet Explorer 6.x and 5.x as well as Netscape Navigator 6.x, you can utilize the complete range of functions of the shortcut menu and the ad-hoc query designer as well as navigate smoothly.For Internet Explorer 4.x and Netscape Navigator 4.x, the Hierarchical shortcut menu web item islimited: reloading hierarchy branches is not possible.Web browsers such as Internet Explorer 3.0 or Netscape 3 do not enable a shortcut menu in in BEx Web Applications. Instead, you can use symbols for limited navigating.For more information on Web Browser dependencies, go to SAP Service Marketplace, alias SAP BW,and look under Services & Implementation Frequently Asked Questions SAP BW & WebApplication Server.

Source: SAP BW Functions in Detail, Version 1.0 SAP BW 3.0B


Objects Used in the Design Process

ExcelWorkbookExcelWorkbook

HTMLTemplates

ExcelWorkbook

ExcelWorkbook

Items(Charts, Tables, News Tickers ..)

ExcelWorkbook

ExcelWorkbook

SAP BW Queries

ExcelWorkbook

ExcelWorkbookQuery Views

derived from

can be stored with

used in

supply data to

ExcelWorkbook

ExcelWorkbookLibraries

ExcelWorkbook

ExcelWorkbook

SAP BW Workbooks

embedded in

supply data to

= stored in roles

© SAP AG


View

• = Data basis for the items

• Define a set of data

• Specify workbook filters, outlines, exceptions, and so on

• Derived from a query but contain workbook filters and navigation


Items

For example:

• Table (results area)

• Navigation block

• Chart

• Filter

• Alert Monitor

• Exceptions

• Conditions


New Web Items

– Ad-hoc Query Designer– News Ticker– Checkboxes for filter values– Hierarchical dropdown boxes– Single documents and

document list– Menu

New Items

© SAP AG


Structuring the Layout

You can change the layout of your web template - an HTML page with SAP BW content - the sameway you would in an HTML editor.

Arranging Web Items on the Page• You can change the size of the placeholders• You can arrange the web items horizontally• You can drag and drop the web items to the positions you want them and regroup them within the web

template

Arranging Web Items Using an HTML TableYou can use an HTML table to arrange web items.You can lay out this grid for your own needs and place various web items in each table cellvertically or horizontally, according to how you want to arrange them.

Enhancing Web Templates with TextAs well as adding and arranging Web items, you can enhance the Web template with text and formatthis text.

Enhancing Web Templates with ImagesYou can also insert images such as your organization logo into your web template. The images are storedIn the MIME repository of the SAP BW server. The system supports image formats GIF, JPG, and BMP.



URL

• In General:http://server/sap/bw/BEx?sap-language=Language&cmd=ldoc&TEMPLATE_ID=Template (and other parameters)

• Example:http://hcc2b12.informatik.tu-muenchen.de:8001/sap/bw/BEx?sap-language=DE&cmd=ldoc&TEMPLATE_ID=A200_APPL1


Web Reporting: User and Password

Enter User and Password in URL

• &sap-user=xxx&sap-password=yyy

Anonymous Logon• See SAP Note

498936


Integration in HTML Code

<html>

<body>

<object>

SAP BW Object

</object>

</body>

</html>

SAP BW Objects• Data Provider (View)• Item


Additional Editing of the HTML Code

You have the following options for editing the HTML source of a web template:

1. You can edit the web template directly in the HTML view of Web Application Designer. In the lower part of the Template window of Web Application Designer, select the HTML tab page.

2. You can also use an external HTML editor to edit the web template.



Data Mining: ABC Classification


What is Data Mining?

• Data mining helps you analyze and understand customer behavior:

– Data mining is an analytical approach that looks for hidden data patterns and relationships in large databases

– Data mining not only provides insights by analyzing past data, but it is also capable of predicting future trends and behaviors

– Data mining allows organizations to make the critical jump from retrospective analysis to prospective decision-making

© SAP AG


ABC Classification - Definition

• ABC Classification is used to classify objects (such as customers, employees, or products) based on a particular measure (such as revenue or profit)

– Two different approaches for classification:• Define intervals for classification criteria• Define intervals for classified object

– Absolute values or cumulated percentages can be used to create these intervals

© SAP AG


Data Mining – Process Overview

Step 1. Create Query

Step 2. Define Model

BW

Step 4. Transfer Results

CRM

Step 3. Run Model

© SAP AG


Revision: Data Flow in BW


Contents

1. Data Flow in SAP BW

2. Source Systems

3. Technical Prerequisites


Exercise

Unit 1• Assignment 1:

Data Warehouse• Assignment 2:

Change the color


Exercise

Additional Assignment• Describe the data

flow in BW.


Data Flow: Overview

SourceSystem

UpdateRule Role

Work-books

Query

Characteristicwith Master Data

InfoSource(Comm. Structure)

TransferRules

PSADataSource(TransferStructure)

Source System

BW

InfoCube(InfoProvider)

xls

Data

Transformation

InfoPackage

View

xls

Web Template

HTMLStructures/Definitions

1

2

3

DS Replication

45+6

7

8

Reporting


Source System Types and Their Interfaces

BW Service APIBW Service API

Web Service

Web Service

RDBMSRDBMSFlat file

Flat file

ExtractorExternal

DB

RF

CS

erve

r

RF

CC

lient

ExtractorNon-SAPSystems

SAP Source System(R/3, CRM, SEM, BW, APO)

FileInter-face

FileInter-face

XMLInter-face

XMLInter-face

DBConnect

DBConnect

StagingBAPI

StagingBAPI

InfoSourceInfoSource

Data Targets

Transfer Rules

Update Rules

BW

1

RFC Connection (sm59)

with Background Users


DataSource

• Metadata of a business process or business unit

• Types: Transaction data, master data (attributes, texts, hierarchies)

• Reference to source system• Each DataSource (DS) has

exactly one extract structure (ES)

• ES is filled by an extractor• Metadata table: ROOSOURCE• DS is replicated from the

source system to the target system

2 0CO_OM_CCA_9: Cost center actual costs line items (Delta)

Function Module View Query


Extractors

• Extracting data from SAP R/3 systems is done by extractors.

• Plug-ins provide the technical solution that makes extraction possible. They also provide prefabricated extraction scenarios for the various modules.

HRHRFIFICOCO

ExtractorExtractor ExtractorExtractor ExtractorExtractor

ExtractorExtractor ExtractorExtractor

DB ViewSAP QueryFunction Module

R/3 System

…


DataSource Replication

3

© SAP AG


InfoSource

• Contains metadata for a business process

• Functions– Metadata comparison

with DataSources– Supplying the data

targets

• Types– Direct updating– Flexible updating

4

© SAP AG


DS-IS Assignment and Transfer Rules

5+6

Fields in the transfer structure are assigned to InfoObjects.

Transfer Rules:- 1:1- Constants- ABAP routine- Formula

© SAP AG


Data Targets

1. Basic InfoCube

2. ODS object

3. Master data-bearing characteristic

Data Target = contains physical data

InfoProvider = reporting basis

7


Update Rules

• Connects flexibly updated InfoSources with data targets

• Various updating methods

8

© SAP AG


Data Flow Modeling in BW


DataSourceDataSource

InfoSource


InfoSource

DataSourceDataSource DataSourceDataSource

Application-specific

extractor: FI

Application-specific

extractor: CO

Genericextractor

from table

Update Rules

Transfer Rules Homogenization: transforming

data into a structured and singularformat

Data integration into user-specific models


Exercise

Unit 1• Assignment 3: Data

Flow

Instructor

Unit 1• Assignment 4: Test

the source system


Loading Data from mySAP® ERP®


R/3® Extraction: Tips for Use in Courses

1. Cross-system activities 2 systems must be mastered

2. BW cannot handle clients:multiple customers on a BW system Rules and considerations

3. Large amounts of data may be transferred Transaction time

4. No singular procedure available because extraction is heavily dependent on the application in use large amount of time and effort required for initial use

5. Work with central objects of the Data Dictionary high requirements for designing case studies many activities must be performed by the instructor first


Prerequisites for Data Extraction from R/3

• Necessary plug-ins and patches installed• R/3 system set up as the source system in BW (performed by UCC

upon request)• Unique ID of the systems: logical name• Settings for RFC and ALE• ALE provides monitoring and error handling for data transfer• Requirements and acknowledgement sent through IDocs

R/3 BW


Data Extraction from SAP R/3 Systems

• Extracting data from mySAP ERP systems is done by extractors as plug-ins.

• Plug-ins provide the technical solution that makes extraction possible. They provide prefabricated extraction scenarios for the various modules.

HRFICO

Extractor Extractor Extractor

Extractor Extractor

DB ViewSAP QueryFunction Module

R/3 System

…


Data Flow

Extract structure

Transfer Structure

DataSource

DataSource

Update Rules

SAP BW

SAP R/3

Selection of Fields

Selection of Fields

Replication


Transfer Rules

Extract structure

ExtractorExtractor

Extract Structure

Extractor

• Data from a DataSource in the source system are provided in the extract structure.

• The extract structure contains the number of fields that an extractor in the source system provides for the process of loading data.

• The extract structures of DataSources are processed in the source system.

• In the transfer structure, data is transferred from the source system to BW.

• The transfer structure represents a selection of fields from a DataSource of the source system.

• A transfer structure is always related to a DataSource from a source system and an InfoSource in BW.

• A DataSource consists of a number of fields that are provided for data transfer to BW.

• Technically, the DataSource is based on the fields of the extract structure.

• You can expand or filter the fields.


Process of an R/3 Upload

• When metadata is uploaded, the corresponding DataSource is copied into BW. In BW, the DataSource can be assigned to an InfoSource.

• The fields of the DataSource can be assigned to InfoObjects in BW.

• An InfoPackage can be scheduled after the data flow has been set by maintaining the transfer rules.

• The process of loading the data is triggered by a request IDoc to the source system.


Extraction Scenarios

Business ContentDataSources

Customer-DefinedDataSources

GenericDataSources

Application-SpecificExtractors

GenericExtractors

App

licat

ion-

Spe

cific

(CO

, F

I, H

R,

etc

.)

Cus

tom

er-

defin

ed(T

ab

les,

Vie

ws,

Q

ue

ries)

Extractors

Dat

a S

tora

ge

in R

/3

-

Extraction Process

Applications

Extractors

Possible focus points in the

course

Legend:


Exercise Scenario

ZYXX_KUVZYCO_OM_CCA_IK0CO_OM_CCA_90CO_OM_CCA_9

R/3 BW

DataSource DataSource InfoSource

InfoCube

0CCA_C11

Template

replicate

0CO_OM_CCA_9

Template

FR

InfoPackage 3

1

2

assign

45

6

Monitoring

7


Delta Data Extractionfrom mySAP® ERP®


Full vs. Delta Upload

There are two kinds of extraction:

– Full Upload: extracting the entire dataset– Delta Upload: only data that has changed since the

last extraction is loaded into BW.

Significant improvement in performance compared to extracting the entire dataset


Exercise Scenario: Postings in OLTP

Material consumptionfor cost center

Vendor invoice

Internal activity allocation

MM

FI

CO

Posting costs in application table COVP

Extractor:Function module BWOMD_GET_CTRCSTA1

DataSource0CO_OM_CCA_9

BW

Postings in the OLTP system after a full update is performed


Exercise

InstructorUnit 3• Assignment 1:

Delta initialization

Unit 3• Assignments 2-4

Perform postings in R/3

Unit 3• Assignments 5-7

Add to delta loading process


Delta Transfer to BW

The BW scheduler offers the following updating modes:– Full Update

Requires all data that matches the selection criteria in the scheduler

– Delta-Update:Requires data that has occurred since the last loading in the source system

– Initializing the Delta Process:Prerequisite for delta processes. Selections for the initialization are used to load the delta records.


Delta Transfer to BW

Update Mode to BW

Update Mode to BW

© SAP AG


How are Deltas Identified?

Delta Queue

• Key values from modified or new records created in one table.

• SAP stores before and after images of each modified dataset in the delta queue.

• Similar approach to DBMS logs.

Time Stamp

• Time stamps posted in an external table.

• Discrepancy between time stamp and posting time.

• So, default safety time should be set.

• Changes cannot be historicized.


DB

Dynpro 1 Dynpro 2 Dynpro 3

... COMMIT WORK.

SAVE

Posting Section

DB COMMIT

Log Tables Application Tables

DB COMMIT DB COMMIT DB COMMIT

DB-LUW 1

Time

Dialog Section

DB COMMIT

SAP-LUW

DB-LUW 2 DB-LUW 3 DB-LUW 4 DB-LUW 4

SAP-LUW

SAP LUW vs. DB LUW

© SAP AG


Safety Time

Because the SAP R/3 system needs a certain amount of posting time to post line items and because it sets the time stamp at the beginning of the posting, there may be a deviation between the posting time and the time stamp. Line items located within this deviation have not yet been posted to the database. Therefore, they cannot be selected when creating a delta dataset and are not loaded into BW.By setting a safety time (a time period in which line items will certainly be posted) you ensure that line items are extracted and loaded into BW despite the deviation between the time stamp and the posting time.

© SAP AG


Delta Procedure of the DataSources

• The delta modes used in a DataSource define a certain delta procedure.

• The delta procedure is a property of the extractor.

• As an attribute of the DataSource, it indicates how the data will be transferred to the data target.

• This enables you to determine the data targets for which a DataSource is suitable, how to perform updates, and how to perform serialization.


DataSource Delta Capability

Delta update possibleDelta update possible

© SAP AG


Delta Extraction – Example: Cost Centers

0CO_OM_CCA_9Cost Centers:

Actual costs line items(Delta)

0CO_OM_CCA_9Cost Centers:

Actual costs line items(Delta)

DataSource

Data origintables R/3

Data Records:-Before Img.-After Img.

Time StampTable

BW

Defining the Delta Process

ADD

Delta ProcessADD: Additive Extraction via Extractor• The extractor allows fields to be added only.• Updating possible in InfoCube and ODS.• Request Serialization.

Because of Posting of Line Items:• Deviation between time stamp and posting time• Set a safety time

Updating modes supported:• Delta-Init (determining the initial set)• Delta Update (determining and uploading the delta dataset)• Full Update (determining and uploading the entire dataset)

R/3

DataSource 0CO_OM_CCA_9 returns information on actual costs that have been posted to cost centers.


Conclusion: Uses of “BW Extraction”

1. Simply, “Filling InfoCubes”

2. Database-oriented subject

3. Delta Management as challenging SAP subject

4. System-wide case studies

1. System-wide activities 2 systems must be mastered

2. BW cannot support multiple clients: several customers in one BW system Rules and consideration

3. Large datasets may be moved Length of transactions

4. No single procedure, as extraction is largely dependent on application considerable time and effort required for initial use

5. Work with central objects of the Data Dictionary high requirements for designing case studies many activities must be carried out by the instructor

Opportunities Challenges


Extraction from mySAP ERP Using Generic Data Sources


mySAP ERP Users

• G51, Client 902(mySAP ERP ECC 5)

• Users: DEVELOP-XX, with XX = 01 to 20

• Password: init

• There are developer keys for these users (see table)


Extraction Scenarios

Business ContentDataSources

Customer-DefinedDataSources

GenericDataSources

ApplicationSpec.Extractors

GenericExtractors

App

licat

ion-

Spe

cific

(CO

, F

I, H

R,

etc

.)

Cus

tom

er-

Def

ined

(Ta

ble

s, V

iew

s,

Qu

erie

s)

Extractors

Dat

a S

tora

ge

in R

/3

-

Extraction Process

Applications

Extractors

Possible focus points in the

course


mySAP ERP Source system

Scenario: Generic Extraction

USR01

USREFUS

R/3 Application tables(User administration)

Z_YXX_Userdata

Extractor (View)

Z_YXX_Userdata_DS

GenericDataSource

BW System

Z_YXX_Userdata_DS

GenericDataSource(copy)

AYXX_US_IS

InfoSource

Z_YXX_Userdata

Z_YXX_Userdata_DS

Z_YXX_Userdata_DS

AYXX_US_IS

AYXX_US

Characteristic (with master data)

AYXX_US


Project Completion


Distribution of a Data Warehouse

• Making the product known to users• Marketing: newsletter, webpage• Community• Technical setup at the workplace• User training• Creating a support structure

The users are the most sensitivefactor in a data warehouse project!

NewDataWare-

house !!!


Data Warehouse Maintenance

• User-Related “Maintenance”– Constant contact with users– Continuous Support– Providing training continuously and repeatedly

• Technical Maintenance– Avoiding system downtime– Maintaining the infrastructure– Guaranteeing and improving performance

• Managing Growth– Equipping the system for growth– Growth is a sign of DW acceptance


Terminating/Replacing a DW

• Determining the time for complete termination or replacement

• Conversion costs

• Residual license costs

Investment calculation Life cycle view Follow-up project!


SourceSystem

UpdateRule Role

Work-books

Query

Characteristicwith Master Data

InfoSource(Comm. Structure)

TransferRules

PSADataSource(TransferStructure)

Source BW

InfoCube(InfoProvider)

xls

Source: BW Course, TUM March 31, 2004

Data

Transformation

InfoPackage

View

xls

Web Template

HTML

Structures/Definitions


Current BW Courses from SAP AG


Courses on SAP BW: Overview

Source: www.sap.de(online course catalog),accessed April 2006


Courses on SAP BW: Reporting Emphasis

• Focus on analyses and evaluations• Primarily BEx Analyzer• InfoProvider and up



Courses on SAP BW: Data Warehousing Emphasis

• Technically oriented• Primarily AWB• Up to InfoProvider



Courses on SAP BW: Administration Emphasis

• Not necessary for UCC customers• Task of UCC


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