on 04-13-2007 11:43 AM
i want to learn sap-bw can anyone suggest me any material for bw.plz help me
Part
I
Guided Tours
In Part I, we will tour basic SAP BW (Business
Information Warehouse) functionalities using a
simplified business scenariosales analysis.
After introducing the basic concept of data warehousing and giving an
overview of BW, we create a data warehouse using BW and load data into it.
We then check data quality before creating queries and reports (or workbooks,
as they are called in BW). Next, we demonstrate how to use an SAP tool called
Profile Generator to manage user authorization.
After finishing the guided tours, we will appreciate BWs ease of use and
get ready to explore other BW functionalities.
1
Contents
CHAPTER 1 Business Scenario and SAP BW
CHAPTER 2 Creating an InfoCube
CHAPTER 3 Loading Data into the InfoCube
CHAPTER 4 Checking Data Quality
CHAPTER 5 Creating Queries and Workbooks
CHAPTER 6 Managing User Authorization
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28925 FU 01 001-016 r3rg.ps 6/27/02 4:04 PM Page 2
Chapter
1
Business Scenario
and SAP BW
The objective of data warehousing is to analyze
data from diverse sources to support decision
making. To achieve this goal, we face two challenges:
Poor system performance. Adata warehouse usually contains a large volume
of data. It is not an easy job to retrieve data quickly from the data
warehouse for analysis purposes. For this reason, the data warehouse
design uses a special technique called a star schema.
Difficulties in extracting, transferring, transforming, and loading (ETTL)
data from diverse sources into a data warehouse. Data must be cleansed
before being used. ETTL has been frequently cited as being responsible for
the failures of many data warehousing projects. You would feel the pain if
you had ever tried to analyze SAP R/3 data without using SAP BW.
3
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SAP R/3 is an ERP (Enterprise Resources Planning) system that most large
companies in the world use to manage their business transactions. Before the
introduction of SAP BW in 1997, ETTL of SAP R/3 data into a data warehouse
seemed an unthinkable task. This macro-environment explained the urgency
with which SAP R/3 customers sought a data warehousing solution. The result
is SAP BW from SAP, the developer of SAP R/3.
In this chapter we will introduce the basic concept of data warehousing. We
will also discuss what SAP BW (Business Information Warehouse) is, explain
why we need it, examine its architecture, and define Business Content.
First, we use sales analysis as an example to introduce the basic concept of
data warehousing.
1.1 Sales AnalysisA Business Scenario
Suppose that you are a sales manager, who is responsible for planning and
implementing sales strategy. Your tasks include the following:
Monitoring and forecasting sales demands and pricing trends
Managing sales objectives and coordinating the sales force and distributors
Reviewing the sales activities of each representative, office, and region
Suppose also that you have the data in Tables 1.1 through 1.3 available
about your firms materials, customers, and sales organization.
4 PART I: GUIDED TOURS
TABLE 1.1
MATERIALS
Material Number Material Name Material Description
MAT001 TEA Ice tea
MAT002 COFFEE Hot coffee
MAT003 COOKIE Fortune cookie
MAT004 DESK Computer desk
MAT005 TABLE Dining table
MAT006 CHAIR Leather chair
MAT007 BENCH Wood bench
MAT008 PEN Black pen
MAT009 PAPER White paper
MAT010 CORN America corn
MAT011 RICE Asia rice
MAT012 APPLE New York apple
MAT013 GRAPEFRUIT Florida grapefruit
MAT014 PEACH Washington peach
MAT015 ORANGE California orange
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You also have three years of sales data, as shown in Table 1.4.
CHAPTER 1: BUSINESS SCENARIO AND SAP BW 5
TABLE 1.2
CUSTOMERS
Customer ID Customer Name Customer Address
CUST001 Reliable Transportation Company 1 Transport Drive, Atlanta, GA
23002
CUST002 Finance One Corp 2 Finance Avenue, New York, NY,
10001
CUST003 Cool Book Publishers 3 Book Street, Boston, MA 02110
CUST004 However Forever Energy, Inc. 4 Energy Park, Houston, TX 35004
CUST005 Easy Computing Company 5 Computer Way, Dallas, TX 36543
CUST006 United Suppliers, Inc. 6 Suppliers Street, Chicago, IL
61114
CUST007 Mobile Communications, Inc. 7 Electronics District, Chicago, IL
62643
CUST008 Sports Motor Company 8 Motor Drive, Detroit, MI 55953
CUST009 Swan Stores 9 Riverside Road, Denver, CO
45692
CUST010 Hollywood Studio 10 Media Drive, Los Angeles, CA
78543
CUST011 One Source Technologies, Inc. 11 Technology Way, San Francisco,
CA 73285
CUST012 Airspace Industries, Inc. 12 Air Lane, Seattle, WA 83476
TABLE 1.3
SALES
ORGANIZATION
Sales Region Sales Office Sales Sales
Representative Representative ID
EAST ATLANTA John SREP01
NEW YORK Steve SREP02
Mary SREP03
MIDWEST DALLAS Michael SREP04
Lisa SREP05
CHICAGO Kevin SREP06
Chris SREP07
WEST DENVER* Sam SREP08
LOS ANGELES Eugene SREP09
SEATTLE Mark SREP10
*Prior to January 1, 2000, the Denver office was in the Midwest region.
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The data in these tables represent a simplified business scenario. In the real
world, you might have years of data and millions of records.
To succeed in the face of fierce market competition, you need to have a
complete and up-to-date picture of your business and your business environment.
The challenge lies in making the best use of data in decision support. In
decision support, you need to perform many kinds of analysis.
This type of online analytical processing (OLAP) consumes a lot of computer
resources because of the size of data. It cannot be carried out on an
online transaction processing (OLTP) system, such as a sales management
system. Instead, we need a dedicated system, which is the data warehouse.
6 PART I: GUIDED TOURS
Customer Sales Material Per Unit Unit of Quantity Transaction
ID Representative ID Number Sales Price Measure Sold Date
CUST001 SREP01 MAT001 2 Case 1 19980304
CUST002 SREP02 MAT002 2 Case 2 19990526
CUST002 SREP02 MAT003 5 Case 3 19990730
CUST003 SREP03 MAT003 5 Case 4 20000101
CUST004 SREP04 MAT004 50 Each 5 19991023
CUST004 SREP04 MAT005 100 Each 6 19980904
CUST004 SREP04 MAT005 100 Each 7 19980529
CUST005 SREP05 MAT006 200 Each 8 19991108
CUST006 SREP06 MAT007 20 Each 9 20000408
CUST007 SREP07 MAT008 3 Dozen 10 20000901
CUST007 SREP07 MAT008 3 Dozen 1 19990424
CUST008 SREP08 MAT008 3 Dozen 2 19980328
CUST008 SREP08 MAT009 2 Case 3 19980203
CUST008 SREP08 MAT010 1 U.S. pound 4 19991104
CUST009 SREP09 MAT011 1.5 U.S. pound 5 20000407
CUST010 SREP10 MAT011 1.5 U.S. pound 6 20000701
CUST010 SREP10 MAT011 1.5 U.S. pound 7 19990924
CUST010 SREP10 MAT012 2 U.S. pound 8 19991224
CUST010 SREP10 MAT013 3 Case 9 20000308
CUST011 SREP10 MAT014 1 U.S. pound 10 19980627
CUST012 SREP11 MAT014 2 U.S. pound 1 19991209
CUST012 SREP11 MAT015 3 Case 2 19980221
CUST012 SREP11 MAT015 2 Case 3 20000705
CUST012 SREP11 MAT015 3.5 Case 4 20001225
TABLE 1.4 SALES DATA
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1.2 Basic Concept of Data Warehousing
A data warehouse is a system with its own database. It draws data from
diverse sources and is designed to support query and analysis. To facilitate
data retrieval for analytical processing, we use a special database design technique
called a star schema.
1.2.1 Star Schema
The concept of a star schema is not new; indeed, it has been used in industry
for years. For the data in the previous section, we can create a star schema like
that shown in Figure 1.1.
The star schema derives its name from its graphical representationthat is,
it looks like a star. Afact table appears in the middle of the graphic, along with
several surrounding dimension tables. The central fact table is usually very
large, measured in gigabytes. It is the table from which we retrieve the interesting
data. The size of the dimension tables amounts to only 1 to 5 percent of the
size of the fact table. Common dimensions are unit and time, which are not
shown in Figure 1.1. Foreign keys tie the fact table to the dimension tables.
Keep in mind that dimension tables are not required to be normalized and that
they can contain redundant data.
As indicated in Table 1.3, the sales organization changes over time. The
dimension to which it belongssales rep dimensionis called the slowly
changing dimension.
CHAPTER 1: BUSINESS SCENARIO AND SAP BW 7
Customer ID
Customer Name
Customer Address
Customer ID
Sales Rep ID
Material Number
Per Unit Sales Price
Unit of Measure
Quantity Sold
Sales Revenue
Transaction Date
Material Number
Material Name
Material Description
Customer Dimension
Sales Rep ID*
Sales Rep Name
Sales Office*
Sales Region*
Sales Rep Dimension
Material Dimension
Fact Table
FIGURE 1.1
STAR SCHEMA
*Sales Region, Sales Office, and Sales Rep ID are in a hierarchy as shown in Table 1.3.
Sales Revenue = Per Unit Sales Price Quantity Sold.
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The following steps explain how a star schema works to calculate the total
quantity sold in the Midwest region:
1. From the sales rep dimension, select all sales rep IDs in the Midwest
region.
2. From the fact table, select and summarize all quantity sold by the sales
rep IDs of Step 1.
1.2.2 ETTLExtracting, Transferring, Transforming,
and Loading Data
Besides the difference in designing the database, building a data warehouse
involves a critical task that does not arise in building an OLTP system: to
extract, transfer, transform, and load (ETTL) data from diverse data sources
into the data warehouse (Figure 1.2).
In data extraction, we move data out of source systems, such as an SAP R/3
system. The challenge during this step is to identify the right data. A good
knowledge of the source systems is absolutely necessary to accomplish this
task.
In data transfer, we move a large amount of data regularly from different
source systems to the data warehouse. Here the challenges are to plan a realistic
schedule and to have reliable and fast networks.
In data transformation, we format data so that it can be represented consistently
in the data warehouse. For example, we might need to convert an entity
with multiple names (such as AT&T, ATT, or Bell) into an entity with a single
8 PART I: GUIDED TOURS
Data Warehouse
Load
Source System
Transform
Transfer
Extract
FIGURE 1.2
ETTL PROCESS
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name (such as AT&T). The original data might reside in different databases
using different data types, or in different file formats in different file systems.
Some are case sensitive; others may be case insensitive.
In data loading, we load data into the fact tables correctly and quickly. The
challenge at this step is to develop a robust error-handling procedure.
ETTL is a complex and time-consuming task. Any error can jeopardize data
quality, which directly affects business decision making. Because of this fact
and for other reasons, most data warehousing projects experience difficulties
finishing on time or on budget.
To get a feeling for the challenges involved in ETTL, lets study SAP R/3 as
an example. SAP R/3 is a leading ERP (Enterprise Resources Planning) system.
According to SAP, the SAP R/3 developer, as of October 2000, some 30,000 SAP
R/3 systems were installed worldwide that had 10 million users. SAP R/3
includes several modules, such as SD (sales and distribution), MM (materials
management), PP (production planning), FI (financial accounting), and HR
(human resources). Basically, you can use SAP R/3 to run your entire business.
SAP R/3s rich business functionality leads to a complex database design.
In fact, this system has approximately 10,000 database tables. In addition to the
complexity of the relations among these tables, the tables and their columns
sometimes dont even have explicit English descriptions. For many years,
using the SAP R/3 data for business decision support had been a constant
problem.
Recognizing this problem, SAP decided to develop a data warehousing
solution to help its customers. The result is SAP Business Information Warehouse,
or BW. Since the announcement of its launch in June 1997, BW has
drawn intense interest. According to SAP, as of October 2000, more than 1000
SAP BW systems were installed worldwide.
In this book, we will demonstrate how SAP BW implements the star
schema and tackles the ETTL challenges.
1.3 BWAn SAP Data Warehousing Solution
BW is an end-to-end data warehousing solution that uses preexisting SAP technologies.
BW is built on the Basis 3-tier architecture and coded in the ABAP
(Advanced Business Application Programming) language. It uses ALE (Application
Link Enabling) and BAPI (Business Application Programming Interface)
to link BW with SAP systems and non-SAP systems.
CHAPTER 1: BUSINESS SCENARIO AND SAP BW 9
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1.3.1 BW Architecture
Figure 1.3 shows the BW architecture at the highest level. This architecture has
three layers:
1. The top layer is the reporting environment. It can be BW Business
Explorer (BEx) or a third-party reporting tool. BEx consists of two components:
BEx Analyzer
BEx Browser
BEx Analyzer is Microsoft Excel with a BW add-in. Thanks to its easy-touse
graphical interface, it allows users to create queries without coding
SQL statements. BEx Browser works much like an information center,
allowing users to organize and access all kinds of information. Thirdparty
reporting tools connect with BW OLAP Processor through ODBO
(OLE DB for OLAP).
10 PART I: GUIDED TOURS
FIGURE 1.3
BW
ARCHITECTURE
Source: Adapted from SAP BW online documentation.
Browser Analyzer
Non-SAP OLAP Clients
ODBO
Staging Engine
ALE/BAPI
PSA
BDS/ OLAP Processor
User Roles
InfoCubes/
ODS Objects
Data
Manager
BW Server
Metadata
Manager
Metadata
Repository
Scheduler
Administrator
Workbench
Monitor
Business Explorer
Extractor
Non-SAP System
Extractor
SAP System
OLE DB for OLAP Provider
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2. The middle layer, BW Server, carries out three tasks:
Administering the BW system
Storing data
Retrieving data according to users requests
We will detail BW Servers components next.
3. The bottom layer consists of source systems, which can be R/3 systems,
BW systems, flat files, and other systems. If the source systems are SAP
systems, an SAP component called Plug-In must be installed in the
source systems. The Plug-In contains extractors. An extractor is a set of
ABAP programs, database tables, and other objects that BW uses to
extract data from the SAP systems. BW connects with SAP systems (R/3
or BW) and flat files via ALE; it connects with non-SAP systems via BAPI.
The middle-layer BW Server consists of the following components:
Administrator Workbench, including BW Scheduler and BW Monitor
Metadata Repository and Metadata Manager
Staging Engine
PSA (Persistent Staging Area)
ODS (Operational Data Store) Objects
InfoCubes
Data Manager
OLAP Processor
BDS (Business Document Services)
User Roles
Administrator Workbench maintains meta-data and all BW objects. It has
two components:
BW Scheduler for scheduling jobs to load data
BW Monitor for monitoring the status of data loads
This book mainly focuses on Administrator Workbench.
Metadata Repository contains information about the data warehouse.
Meta-data comprise data about data. Metadata Repository contains two types
of meta-data: business-related (for example, definitions and descriptions used
for reporting) and technical (for example, structure and mapping rules used for
data extraction and transformation). We use Metadata Manager to maintain
Metadata Repository.
Staging Engine implements data mapping and transformation. Triggered
by BW Scheduler, it sends requests to a source system for data loading. The
source system then selects and transfers data into BW.
CHAPTER 1: BUSINESS SCENARIO AND SAP BW 11
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PSA (Persistent Staging Area) stores data in the original format while
being imported from the source system. PSA allows for quality check before
the data are loaded into their destinations, such as ODS Objects or InfoCubes.
ODS (Operational Data Store) Objects allow us to build a multilayer structure
for operational data reporting. They are not based on the star schema and
are used primarily for detail reporting, rather than for dimensional analysis.
InfoCubes are the fact tables and their associated dimension tables in a star
schema.
Data Manager maintains data in ODS Objects and InfoCubes and tells the
OLAP Processor what data are available for reporting.
OLAP Processor is the analytical processing engine. It retrieves data from the
database, and it analyzes and presents those data according to users requests.
BDS (Business Document Services) stores documents. The documents can
appear in various formats, such as Microsoft Word, Excel, PowerPoint, PDF,
and HTML. BEx Analyzer saves query results, or MS Excel files, as workbooks
in the BDS.
User Roles are a concept used in SAP authorization management. BW
organizes BDS documents according to User Roles. Only users assigned to a
particular User Role can access the documents associated with that User Role.
Table 1.5 indicates where each of these components is discussed in this
book. As noted in the Preface, this book does not discuss third-party reporting
tools and BAPI.
12 PART I: GUIDED TOURS
TABLE 1.5
CHAPTERS
DETAILING BW
COMPONENTS
Components Chapters
Business Explorer: Chapter 5, Creating Queries and Workbooks
Analyzer and Browser
Non-SAP OLAP Clients Not covered
ODBO
OLE DB for OLAP Provider
Extractor: Chapter 3, Loading Data into the InfoCube, on how
ALE to load data from flat files
Chapter 10, Business Content, on how to load data
from R/3 systems
Chapter 11, Generic R/3 Data Extraction
BAPI Not covered
Administrator Workbench The entire book, although not explicitly mentioned
BW Scheduler Chapter 3, Loading Data into the InfoCube, on BW
Scheduler
BW Monitor Chapter 4, Checking Data Quality, on BW Monitor
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1.3.2 BW Business Content
One of the BWs strongest selling points is its Business Content. Business Content
contains standard reports and other associated objects. For example, BW
provides you, the sales manager, with the following standard reports:
Quotation Processing
Quotation success rates per sales area
Quotation tracking per sales area
General quotation information per sales area
Order Processing
Monthly incoming orders and revenue
Sales values
Billing documents
Order, delivery, and sales quantities
Fulfillment rates
Credit memos
Proportion of returns to incoming orders
Returns per customer
Quantity and values of returns
Product analysis
Product profitability analysis
CHAPTER 1: BUSINESS SCENARIO AND SAP BW 13
Components Chapters
Metadata Repository The entire book, although not explicitly mentioned
Metadata Manager
Staging Engine Chapter 3, Loading Data into the InfoCube
PSA
Chapter 4, Checking Data Quality
ODS Objects Chapter 9, Operational Data Store (ODS)
InfoCubes Chapter 2, Creating an InfoCube
Chapter 7, InfoCube Design
Chapter 8, Aggregates and Multi-Cubes
Data Manager Chapter 12, Data Maintenance
OLAP Processor Chapter 13, Performance Tuning
BDS Chapter 5, Creating Queries and Workbooks
User Roles Chapter 6, Managing User Authorization
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Delivery
Delivery delays per sales area
Average delivery processing times
Analyses and Comparisons
Sales/cost analysis
Top customers
Distribution channel analysis
Product profitability analysis
Weekly deliveries
Monthly deliveries
Incoming orders analysis
Sales figures comparison
Returns per customer
Product analysis
Monthly incoming orders and revenue
Administrative and Management Functions
Cost center: plan/actual/variance
Cost center: responsible for orders, projects, and networks
Order reports
WBS Element: plan/actual/variance
Cost center: plan/actual/variance
Cost center: hit list of actual variances
Cost center: actual costs per quarter
Cost center: capacity-related headcount
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Try the SDN wiki? It has a lot of material on BI, including learning and certification paths.
https://www.sdn.sap.com/irj/sdn/wiki
Click on 'Business Intelligence (BI)'.
Hope this helps.
Sudha
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Hi,
SAP Business Information Warehouse (BW) is SAP´s Data Warehouse solution. It has been specially developed to allow you to gather and analyze all kinds of statistical information in the best possible way.
The SAP Business Information Warehouse (SAP BW) is a core element of mySAP.com. SAP BW is an enterprise-wide information hub that enables data analysis from R/3 and other business application, including external data sources such as databases and the Internet. SAP BW also offers easy integration with other mySAP solutions, such as mySAP Supply Chain
Management (mySAP SCM), mySAP Strategic Enterprise Management (mySAP SEM), and mySAP Customer Relationship Management (mySAP CRM).
SAP BW is a comprehensive end-to-end data warehouse solution with optimized structures for reporting and analysis. To help knowledge workers quickly mine an enterprises business data, SAP BW is equipped with preconfigured information models and reports, as well as automatic data extraction and loading methods.
With an easy-to-use Microsoft Excel-based user interface, you can create, format, and analyze reports, and publish those reports to the web. Built for high performance, SAP BW resides on its own dedicated server. Online Transaction Processing (OLTP) and reporting activities are therefore separated, and system performance is not compromised.
If you have any SAP Business Information Warehouse queries, please feel free to raise it in the SAP BW Forum.
Check these links.
www.sap-img.com/business/difference-between-bw-technical-and-functional.htm
www.sap-img.com/business/what-is-spro-in-bw-project.htm
www.sap-img.com/business/questions-answers-on-sap-bw.htm
Hope this resolves your query.
Reward all the helpful answers.
Regards
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Hi,
Check this below links.
/people/gilad.weinbach2/blog/2007/02/23/a-beginners-guide-to-your-first-bi-model-in-nw2004s
http://searchsap.techtarget.com/generic/0,295582,sid21_gci1182794,00.html
Thanks,
Sankar M
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You must be a registered user to add a comment. If you've already registered, sign in. Otherwise, register and sign in.
Hi,
The link below is a PDF for SAP-BW.
Check out this. it shud help u.
****Reward points if useful.
All the best
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