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1. SAP BW
Table of contents
1 INTRODUCTION TO BUSINESS INTELLIGENCE & DATA
WAREHOUSING .................................................................................................... 3
1.1. BUSINESS INTELLIGENCE AND DATA WAREHOUSING ................................... 3
1.2. THE CLASSIC STAR SCHEMA ....................................................................... 4
1.3. INTRODUCTION TO SAP BW ........................................................................ 5
1.4. SAP BW ARCHITECTURE ............................................................................ 6
1.5. THE SAP BW STAR SCHEMA ...................................................................... 8
1.6. INTRODUCTION TO ADMINISTRATOR WORKBENCH (AWB)......................... 13
2 INTRODUCTION TO INFOOBJECTS & INFOCUBES ........................... 16
2.1. INTRODUCTION TO INFOOBJECTS ............................................................... 16
2.2. TYPES OF INFOOBJECTS............................................................................. 16
2.3. CHARACTERISTIC INFOOBJECT .................................................................. 18
2.4. CREATING A CHARACTERISTIC IN THE INFOOBJECT TREE ........................... 28
2.5. KEY FIGURES ............................................................................................ 30
2.6. INFOCUBES ............................................................................................... 34
2.7. BASISCUBES ............................................................................................. 35
2.8. CREATING AN INFOCUBE IN THE INFOPROVIDER TREE ............................... 39
2.9. TECHNICAL IMPLEMENTATION OF SAP BW STAR SCHEMA ........................ 43
3 DATA TRANSFER PROCESS IN SAP BI .................................................. 59
3.1. OVERVIEW OF DATA TRANSFER PROCESS .................................................. 59
3.2. DATA TRANSFER PROCESS – EXAMPLE ...................................................... 61
3.3. CREATING AND MANAGING DTP ............................................................... 62
3.4. ERROR HANDLING OF DTP ........................................................................ 67
3.5. ERROR STACK IN DTP............................................................................... 68
3.6. TEMPORARY STORAGE FOR DTP ............................................................... 72
3.7. DTP MONITOR.......................................................................................... 74
3.8. MANAGING INFOCUBES-DATA MAINTENANCE .......................................... 80
3.9. USING BW MONITOR ................................................................................ 93
4 DATA STORE OBJECTS (DSO) ................................................................. 98
4.1. DATA STORE OBJECT DEFINITION: ............................................................. 98
4.2. DATA STORE OBJECT TYPES.................................................................... 100
4.3. DATA STORE OBJECT ADMINISTRATION .................................................. 107
4.4. DATASTORE OBJECT ADMINISTRATION - PERFORMANCE: ........................ 110
5 MULTIPROVIDERS................................................................................... 112
5.1. ADVANTAGES OF MULTIPROVIDER.......................................................... 113
5.2. MULTIPROVIDER, APPLICATION EXAMPLE ............................................... 113
5.3. CREATING A MULTIPROVIDER ................................................................. 116
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6 AGGREGATES ........................................................................................... 119
6.1. USING AGGREGATES ............................................................................... 119
6.2. AGGREGATES AND MASTER DATA CHANGES ........................................... 125
7 ADMIN COCKPIT ...................................................................................... 132
8 PROCESS CHAINS..................................................................................... 132
8.1. OVERVIEW OF PROCESS CHAINS .............................................................. 132
8.2. STRUCTURE OF PROCESS CHAINS............................................................. 133
9 GENERIC R/3 DATA EXTRACTION ....................................................... 137
9.1. CREATING VIEWS IN R/3 ......................................................................... 137
9.2. CREATING DATASOURCES IN R/3. ........................................................... 139
9.3. LOADING DATA FROM R/3 INTO BW ........................................................ 140
10 LOGISTICS COCKPIT .......................................................................... 145
10.1. WHAT IS LOGISTIC COCKPIT (LC)? ...................................................... 145
10.2. LOGISTIC COCKPIT FUNCTIONS ............................................................ 146
11 REPORTING AND ANALYSIS ............................................................. 151
11.1. SAP BW BUSINESS EXPLORER ............................................................. 151
11.2. WORKING WITH BEX........................................................................... 153
11.3. BEX ANALYZER .................................................................................. 159
11.4. RESTRICTED KEY FIGURES .................................................................. 167
11.5. CALCULATED KEY FIGURES ................................................................ 170
11.6. VARIABLES ......................................................................................... 175
11.7. CONTENT VARIABLES.......................................................................... 179
11.8. EXCEPTIONS........................................................................................ 180
11.9. CREATING EXCEPTIONS ....................................................................... 180
11.10. CONDITIONS........................................................................................ 187
12 BEX WEB APPLICATION DESIGNER ................................................ 189
12.1. INTRODUCTION ................................................................................... 189
12.2. FEATURES ........................................................................................... 189
12.3. SAMPLE WEB DASHBOARDS ................................................................ 196
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3. SAP BW
1 Introduction to Business Intelligence
& Data Warehousing
1.1. Business Intelligence and Data Warehousing
Business Intelligence is a technology based on customer and profit oriented
models that reduce operating costs and provide increased profitability by
improving productivity, sales, and service and help to make decision-making
capabilities at no time. Business Intelligence Models are based on multi
dimensional analysis capabilities.
BI solutions differ from and add value to standard operational systems
(OLTP systems – Online Transaction Processing systems) in three ways -
 By providing the ability to extract, cleanse and aggregate data from
multiple operational systems into a separate data mart or data
warehouse
 By storing data often in a star or multi dimensional cube format, to
enable rapid delivery of summarized information and drill down to
detail
 By delivering personalized, relevant informational views and
querying, reporting and analysis capabilities for gaining deeper
business understanding and making better decisions faster
To implement BI, the following technologies are used-
 Data Marts/ Data Warehouses - A data warehouse is a subject
oriented, integrated, time variant, non-volatile collection of data in
support of management's decision-making process. To facilitate data
retrieval for multi dimensional analytical processing, a special
database design technique called a star schema is used very often.
 Extraction, Transformation and Loading (ETL) - Data is extracted
from multiple source systems. Data is cleansed and transformed and
into a consistent format and structure. The cleansed data is loaded
into the data warehouse.
 On-Line Analytical Processing (OLAP) and Data Mining - Analysis tools
are applied against the data warehouse to analyze and mine the
data.
The main differences between an OLTP and an OLAP system are as follows –
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Criteria OLTP data OLAP data
Purpose OLTP servers handle OLAP servers handle
mission critical management critical
production data accessed data accessed through
through simple queries. an iterative analytical
investigation.
Time Scale Organization’s day-to- Historical data for trend
day operational data. analysis.
Current data.
Indexing Optimize update Optimize ad hoc query
performance by performance by
minimizing the number including lots of
of indexes. indexes.
Normalization Fully normalized. Possibly partially
denormalized for
performance reasons.
Organization Organized around Organized around
business functions. information topics.
Values Typically coded data Typically descriptive
(e.g. product codes) for data (e.g. product
efficiency reasons. names) for ease-of-use
reasons.
Operations Insert, Delete, Update. Read only.
performed
Homogeneity Possibly scattered among Centralized into a single
a variety of databases, homogeneous data store
under a mix of DBMS and in the case of a data
operating systems, and warehouse; or a
using different value collection of
coding schemes. homogeneous subject-
oriented data marts.
DBMS Chosen primarily for its Chosen primarily for its
ability to meet the ability to meet the
organization's OLTP organization's OLAP
needs. Usually an RDBMS. needs. Usually a multi-
dimensional database.
Table 1.1: Comparison of OLTP and OLAP Data
1.2. The Classic Star Schema
The star schema derives its name from its graphical representation like a
star. This database schema classifies two groups of data: facts (sales or
quantity, for example) and dimension attributes (customer, time, and
material, for example).
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A fact is measure that answers the questions like “how much?” and “how
many?” The fact data (values for the facts) are stored in a highly normalized
fact table. A dimension is a textual description of the dimensions/features
of the business. The dimension answers the questions “Who? What? When?”
For example, the dimensions of a product may include product name, brand
name, size, and packaging type. The values of the dimension attributes are
stored in various demoralized dimension tables.
As shown in figure 1.1, a fact 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 statistical data. The size of the dimension tables amounts to
only 1 to 5 percent of the size of the fact table. Foreign keys tie the fact
table to the dimension tables.
Figure 1.1: Classic Star Schema
1.3. Introduction to SAP BW
The SAP Business Information Warehouse (SAP BW) is a state-of-the-art,
end-to-end data warehouse solution developed by SAP. It enables users to
analyze data from operative SAP applications as well as from other business
applications and external data sources such as databases, online services
and the Internet.
SAP BW enables Online Analytical Processing (OLAP) for staging of
information from large amounts of operative and historical data. SAP BW
server is pre-configured for core areas and processes and allows users to
examine the relationships in all areas of an organization.
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With the Business Explorer (BEx), SAP BW gives a flexible reporting and
analysis tool to support strategic analyses and decision-making processes
within an organization. These tools include querying, reporting and OLAP
functions.
1.4. SAP BW Architecture
SAP BW architecture is made up of three functional layers.
 Source Systems
 SAP BW Server
 SAP BW OLAP
Figure 1.2: SAP BW Three Layer Architecture
1.4.1. Source Systems
A source system is a reference system that functions as a data provider for
SAP BW. SAP BW distinguishes between four kinds of source systems:
1.4.1.1. mySAP.com Components
SAP BW is fully integrated into the new mySAP.com world. SAP has provided
a set of predefined extraction structures and programs, called DataSources,
to extract the source data from mySAP.com components and then to load
the data directly into SAP BW.
A SAPI (Service Application Programming Interface) is an SAP-internal
component that is delivered as of Basis release 3.1i. Communication
between mySAP.com components and SAP BW takes place via this SAPI.
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1.4.1.2. Non-SAP Systems
The open architecture of SAP BW allows data to be extracted from
heterogeneous sources across the organization thus making it possible to
have consolidated data basis for reporting. SAP delivers various tools,
which allow these interfaces to be implemented quickly and efficiently.
In heterogeneous system landscapes, an important requirement is that the
different data structures and content are consolidated before being loaded
into SAP BW. You can use an ETL tool such as Ascential DataStage to load
data from heterogeneous systems, such as Siebel and PeopleSoft, transform
this data into a single format and then load it via a Business Programming
Interface into SAP BW. BAPI is the interface used for the structured
communication between SAP BW and external systems. Both data providers
and ETL tools use this interface.
SAP automatically supports automatic import of files in CSV or ASCII format
for flat files as standard.
The SOAP (Simple Object Access Protocol) RFC Service is used to read XML
data and to store it in a delta queue in SAP BW. The data can then be
processed further with a corresponding DataSource and SAPI.
1.4.1.3. Data Providers
SAP BW can also be supplied with target-orientated data from various
providers. For example, you can compare the market research data
provided by an agency with your own operative data. Again, BAPI is used for
the transfer of data supplied by the data providers to SAP BW.
1.4.1.4. Databases
SAP BW allows data to be loaded from external relational database systems.
A DataSource is generated based on the external table structure, enabling
table content to be loaded quickly and consistently into SAP BW.
DB Connect is a way, which allows relational databases to be accessed
directly. Here, SAP DB MultiConnect is used to create a connection to the
database management system (DBMS) in the external database. By
importing metadata and original data, the necessary structures can be
generated in SAP BW and the data can be loaded into the SAP BW system.
1.4.2. SAP BW Server
SAP BW server provides a 'Staging Engine', which controls the data loading
process. It also features SAP BW databases, which store master, transaction
and metadata.
The Administrator WorkBench (AWB) is responsible for the control,
monitoring and maintenance of all data procurement processes. The
Administrator WorkBench is the place where you define all relevant
information objects, plan load processes using a scheduler, and monitor
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them using a monitor tool. However, before the data is in a suitable form to
be stored, it must be prepared by the Extraction, Transformation and Load
(ETL) process.
1.4.3. SAP BW OLAP
The Online Analytical Processing (OLAP) processor allows you to carry out
multi-dimensional analyses of SAP BW data sets. It also provides the OLAP
tools with data via the BAPI, XML/A or ODBO (OLE DB for OLAP) interfaces.
In principle, the OLAP area can be divided into three components:
 BEx Analyzer (Microsoft Excel based)
 BEx Web Application
 BEx Mobile Intelligence
You can use these tools to carry out both Microsoft Excel and Web-based
analyses across several dimensions (such as time, place, product, and so on)
simultaneously.
1.5. The SAP BW Star Schema
The multi-dimensional model in SAP BW is based on the SAP BW star
schema. SAP came up with the enhanced star schema to resolve the
problems experienced with the classic star schema. Figure 1.3 shows the
crossover between the classic star schema shown in the Figure 1.1 and the
SAP BW star schema. For the time being, only components relevant to the
modeling view are taken into consideration.
Figure 1.3: SAP BW Star Schema
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The main distinction between a classic start schema and SAP BW star
schema is that in the SAP BW star schema the dimension tables do not
contain master data information. This master data information is stored in
separate tables, called master data tables. We can think of the SAP BW star
schema as two self-contained areas:
 InfoCube
 Master Data Tables/Surrogate ID (SID-) Tables
1.5.1. InfoCube
InfoCubes are the central objects of the multi-dimensional model in SAP
BW. Reports and analyses are based on these. From a reporting perspective,
an InfoCube describes a self-contained data set within a business area, for
which you can define queries.
An InfoCube (BasisCube) consists of a number of relational tables- a central
fact table surrounded by several dimension tables- combined on a multi-
dimensional basis.
Note: There are various types of InfoCube in BW, which will be discussed
later. Till then an InfoCube will always refer to a BasisCube. The BasisCube
is the InfoCube relevant for modeling, since only physical objects (objects
that contain data) are considered in the modeling within the SAP BW- data
model.
Figure 1.4: InfoCube
In the SAP BW- star schema, the facts in the fact table are referred to as
key figures and the dimension attributes as characteristics. The dimension
tables are linked relationally with the central fact table by way of foreign
or primary key relationships. In contrast to the classic star schema, the
characteristic values are not stored in the dimension tables. A numerical SID
key is generated for each characteristic. This foreign key replaces the
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characteristic as the component of the dimension table. Here, SID stands
for Surrogate ID (replacement key). In the graphic above, these keys are
given the prefix SID_. For example, 'SID_MATERIAL' is the SID key for the
characteristic 'MATERIAL' ('MATERIAL_ID').
Each dimension table has a generated numerical 'primary key', called the
dimension key. In the graphic above, this dimension key is denoted with
the prefix DIM_ID_. Here, 'DIM_ID_MATERIAL' is the dimension key for the
material dimension table.
As in the classic star schema, the primary key of the fact table is made up
of dimension keys ('DIM_ID_DATENPAKET', 'DIM_ID_ZEIT', 'DIM_ID_EINHEIT',
'DIM_ID_KUNDE', 'DIM_ID_MATERIAL').
1.5.2. Master Data Tables/SID Tables
Additional information about characteristics is referred to as master data in
the SAP BW. The master data is classified into three types:
 Attributes
 Texts
 (External) hierarchies
Master data information is stored in separate tables called master data
tables (separately for attributes, texts and hierarchies). These tables are
independent of the InfoCube. For example, as shown in the Figure 1.3, the
attribute ‘material group’ is stored in the attribute table, the text
description for 'material name' is stored in the text table and the material
hierarchy is stored in the hierarchy table for the characteristic 'MATERIAL'.
In this way, the characteristic 'MATERIAL' is the primary key for the master
data tables belonging to this characteristic.
As mentioned earlier, precisely one numerical SID key is assigned to each
characteristic. This assignment is made in a SID table for the respective
characteristic, whereby the characteristic becomes the primary key in the
SID table. As shown in the Figure 1.5, the SID key 'SID_MATERIAL' is assigned
to the characteristic 'MATERIAL' in the SID table for characteristic
'MATERIAL'. The SID table is connected to the associated master data tables
via the characteristic key.
Page 10 of 196

11. Preview Original paying document published on :
http://expertplug.com/materials/training/sap-bi-bw-full-training-material
You can find many more full SAP training material and SAP jobs on http://expertplug.com/.
ExpertPlug is an SAP marketplace for training materials and an online community of experts. We
offer a simple way for the global SAP workforce, consulting companies and industry to market their
skills and find quality information.
As an SAP Expert, you can also market your SAP skills and make extra revenue by publishing SAP
documents on http://expertplug.com/.