SAP BI/BW Full Training Material

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SAP BI/BW Full Training Material

  1. 1. SAP BW Table of contents1 INTRODUCTION TO BUSINESS INTELLIGENCE & DATAWAREHOUSING .................................................................................................... 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)......................... 132 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 ........................ 433 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 ................................................................................ 934 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: ........................ 1105 MULTIPROVIDERS................................................................................... 112 5.1. ADVANTAGES OF MULTIPROVIDER.......................................................... 113 5.2. MULTIPROVIDER, APPLICATION EXAMPLE ............................................... 113 5.3. CREATING A MULTIPROVIDER ................................................................. 116 Page 1 of 196
  2. 2. SAP BW6 AGGREGATES ........................................................................................... 119 6.1. USING AGGREGATES ............................................................................... 119 6.2. AGGREGATES AND MASTER DATA CHANGES ........................................... 1257 ADMIN COCKPIT ...................................................................................... 1328 PROCESS CHAINS..................................................................................... 132 8.1. OVERVIEW OF PROCESS CHAINS .............................................................. 132 8.2. STRUCTURE OF PROCESS CHAINS............................................................. 1339 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 ........................................................ 14010 LOGISTICS COCKPIT .......................................................................... 145 10.1. WHAT IS LOGISTIC COCKPIT (LC)? ...................................................... 145 10.2. LOGISTIC COCKPIT FUNCTIONS ............................................................ 14611 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........................................................................................ 18712 BEX WEB APPLICATION DESIGNER ................................................ 189 12.1. INTRODUCTION ................................................................................... 189 12.2. FEATURES ........................................................................................... 189 12.3. SAMPLE WEB DASHBOARDS ................................................................ 196 Page 2 of 196
  3. 3. SAP BW 1 Introduction to Business Intelligence & Data Warehousing 1.1. Business Intelligence and Data WarehousingBusiness Intelligence is a technology based on customer and profit orientedmodels that reduce operating costs and provide increased profitability byimproving productivity, sales, and service and help to make decision-makingcapabilities at no time. Business Intelligence Models are based on multidimensional 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 fasterTo 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 managements 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 – Page 3 of 196
  4. 4. SAP BW 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 organizations OLTP organizations 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 SchemaThe star schema derives its name from its graphical representation like astar. This database schema classifies two groups of data: facts (sales orquantity, for example) and dimension attributes (customer, time, andmaterial, for example). Page 4 of 196
  5. 5. SAP BWA fact is measure that answers the questions like “how much?” and “howmany?” The fact data (values for the facts) are stored in a highly normalizedfact table. A dimension is a textual description of the dimensions/featuresof the business. The dimension answers the questions “Who? What? When?”For example, the dimensions of a product may include product name, brandname, size, and packaging type. The values of the dimension attributes arestored 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 isusually very large, measured in gigabytes. It is the table from which weretrieve the statistical data. The size of the dimension tables amounts toonly 1 to 5 percent of the size of the fact table. Foreign keys tie the facttable to the dimension tables. Figure 1.1: Classic Star Schema 1.3. Introduction to SAP BWThe SAP Business Information Warehouse (SAP BW) is a state-of-the-art,end-to-end data warehouse solution developed by SAP. It enables users toanalyze data from operative SAP applications as well as from other businessapplications and external data sources such as databases, online servicesand the Internet.SAP BW enables Online Analytical Processing (OLAP) for staging ofinformation from large amounts of operative and historical data. SAP BWserver is pre-configured for core areas and processes and allows users toexamine the relationships in all areas of an organization. Page 5 of 196
  6. 6. SAP BWWith the Business Explorer (BEx), SAP BW gives a flexible reporting andanalysis tool to support strategic analyses and decision-making processeswithin an organization. These tools include querying, reporting and OLAPfunctions. 1.4. SAP BW ArchitectureSAP 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 SystemsA source system is a reference system that functions as a data provider forSAP BW. SAP BW distinguishes between four kinds of source systems: 1.4.1.1. mySAP.com ComponentsSAP BW is fully integrated into the new mySAP.com world. SAP has provideda set of predefined extraction structures and programs, called DataSources,to extract the source data from mySAP.com components and then to loadthe data directly into SAP BW.A SAPI (Service Application Programming Interface) is an SAP-internalcomponent that is delivered as of Basis release 3.1i. Communicationbetween mySAP.com components and SAP BW takes place via this SAPI. Page 6 of 196
  7. 7. SAP BW 1.4.1.2. Non-SAP SystemsThe open architecture of SAP BW allows data to be extracted fromheterogeneous sources across the organization thus making it possible tohave 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 thedifferent data structures and content are consolidated before being loadedinto SAP BW. You can use an ETL tool such as Ascential DataStage to loaddata from heterogeneous systems, such as Siebel and PeopleSoft, transformthis data into a single format and then load it via a Business ProgrammingInterface into SAP BW. BAPI is the interface used for the structuredcommunication between SAP BW and external systems. Both data providersand ETL tools use this interface.SAP automatically supports automatic import of files in CSV or ASCII formatfor flat files as standard.The SOAP (Simple Object Access Protocol) RFC Service is used to read XMLdata and to store it in a delta queue in SAP BW. The data can then beprocessed further with a corresponding DataSource and SAPI. 1.4.1.3. Data ProvidersSAP BW can also be supplied with target-orientated data from variousproviders. For example, you can compare the market research dataprovided by an agency with your own operative data. Again, BAPI is used forthe transfer of data supplied by the data providers to SAP BW. 1.4.1.4. DatabasesSAP BW allows data to be loaded from external relational database systems.A DataSource is generated based on the external table structure, enablingtable content to be loaded quickly and consistently into SAP BW.DB Connect is a way, which allows relational databases to be accesseddirectly. Here, SAP DB MultiConnect is used to create a connection to thedatabase management system (DBMS) in the external database. Byimporting metadata and original data, the necessary structures can begenerated in SAP BW and the data can be loaded into the SAP BW system. 1.4.2. SAP BW ServerSAP BW server provides a Staging Engine, which controls the data loadingprocess. It also features SAP BW databases, which store master, transactionand metadata.The Administrator WorkBench (AWB) is responsible for the control,monitoring and maintenance of all data procurement processes. TheAdministrator WorkBench is the place where you define all relevantinformation objects, plan load processes using a scheduler, and monitor Page 7 of 196
  8. 8. SAP BWthem using a monitor tool. However, before the data is in a suitable form tobe stored, it must be prepared by the Extraction, Transformation and Load(ETL) process. 1.4.3. SAP BW OLAPThe Online Analytical Processing (OLAP) processor allows you to carry outmulti-dimensional analyses of SAP BW data sets. It also provides the OLAPtools 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 IntelligenceYou can use these tools to carry out both Microsoft Excel and Web-basedanalyses across several dimensions (such as time, place, product, and so on)simultaneously. 1.5. The SAP BW Star SchemaThe multi-dimensional model in SAP BW is based on the SAP BW starschema. SAP came up with the enhanced star schema to resolve theproblems experienced with the classic star schema. Figure 1.3 shows thecrossover between the classic star schema shown in the Figure 1.1 and theSAP BW star schema. For the time being, only components relevant to themodeling view are taken into consideration. Figure 1.3: SAP BW Star Schema Page 8 of 196
  9. 9. SAP BWThe main distinction between a classic start schema and SAP BW starschema is that in the SAP BW star schema the dimension tables do notcontain master data information. This master data information is stored inseparate tables, called master data tables. We can think of the SAP BW starschema as two self-contained areas:  InfoCube  Master Data Tables/Surrogate ID (SID-) Tables 1.5.1. InfoCubeInfoCubes are the central objects of the multi-dimensional model in SAPBW. Reports and analyses are based on these. From a reporting perspective,an InfoCube describes a self-contained data set within a business area, forwhich you can define queries.An InfoCube (BasisCube) consists of a number of relational tables- a centralfact table surrounded by several dimension tables- combined on a multi-dimensional basis.Note: There are various types of InfoCube in BW, which will be discussedlater. Till then an InfoCube will always refer to a BasisCube. The BasisCubeis the InfoCube relevant for modeling, since only physical objects (objectsthat contain data) are considered in the modeling within the SAP BW- datamodel. Figure 1.4: InfoCubeIn the SAP BW- star schema, the facts in the fact table are referred to askey figures and the dimension attributes as characteristics. The dimensiontables are linked relationally with the central fact table by way of foreignor primary key relationships. In contrast to the classic star schema, thecharacteristic values are not stored in the dimension tables. A numerical SIDkey is generated for each characteristic. This foreign key replaces the Page 9 of 196
  10. 10. SAP BWcharacteristic as the component of the dimension table. Here, SID standsfor Surrogate ID (replacement key). In the graphic above, these keys aregiven the prefix SID_. For example, SID_MATERIAL is the SID key for thecharacteristic MATERIAL (MATERIAL_ID).Each dimension table has a generated numerical primary key, called thedimension key. In the graphic above, this dimension key is denoted withthe prefix DIM_ID_. Here, DIM_ID_MATERIAL is the dimension key for thematerial dimension table.As in the classic star schema, the primary key of the fact table is made upof dimension keys (DIM_ID_DATENPAKET, DIM_ID_ZEIT, DIM_ID_EINHEIT,DIM_ID_KUNDE, DIM_ID_MATERIAL). 1.5.2. Master Data Tables/SID TablesAdditional information about characteristics is referred to as master data inthe SAP BW. The master data is classified into three types:  Attributes  Texts  (External) hierarchiesMaster data information is stored in separate tables called master datatables (separately for attributes, texts and hierarchies). These tables areindependent of the InfoCube. For example, as shown in the Figure 1.3, theattribute ‘material group’ is stored in the attribute table, the textdescription for material name is stored in the text table and the materialhierarchy is stored in the hierarchy table for the characteristic MATERIAL.In this way, the characteristic MATERIAL is the primary key for the masterdata tables belonging to this characteristic.As mentioned earlier, precisely one numerical SID key is assigned to eachcharacteristic. This assignment is made in a SID table for the respectivecharacteristic, whereby the characteristic becomes the primary key in theSID table. As shown in the Figure 1.5, the SID key SID_MATERIAL is assignedto the characteristic MATERIAL in the SID table for characteristicMATERIAL. The SID table is connected to the associated master data tablesvia the characteristic key. Page 10 of 196
  11. 11. Preview Original paying document published on :http://expertplug.com/materials/training/sap-bi-bw-full-training-materialYou 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. Weoffer a simple way for the global SAP workforce, consulting companies and industry to market theirskills and find quality information.As an SAP Expert, you can also market your SAP skills and make extra revenue by publishing SAPdocuments on http://expertplug.com/.

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