SAP BW Assignment August 2015
These are objects that provide data for a query. Infoproviders can be persistent (in which case data is stored physically and persistently) or non-persistent (where they provide only data stored in other objects). InfoProviders include, InfoObjects, DSO (DataStore Objects) and Infocube, all known as persistent infoproviders. Also the non-persistent infoproviders include MultiProvider, InfoSet and VirtualProviders.
InfoObjects are the basic building blocks of SAP BW. They are the basis for defining or configuring all of the other infoProviders. In some cases, infoObjects can act as Infoproviders. There are five (5) types of infoObjects;
Characteristics (Eg: Employee, Material, Customer)Ø
Key Figures (Eg: Quantity sold, Amount, Weight)Ø
Time Characteristics (Eg: Year, Month, Period, Quarter)Ø
Unit (Eg: Currency, Measurement)Ø
Technical Characteristics (Eg: Data Load request ID, Change Run ID)Ø
InforArea is likened to a folder where infoObjects and infoCubes are contained and organized in groups. They represent the highest level of grouping. Eg., All objects related to sales would be grouped together in one InfoArea. In some cases, there may be InfoAreas within an InfoArea.
To create an InfoArea, run DWW, click on InfoObjects to display the tree of existing InfoAreas. Then add a new infoArea as a subset of an existing one or right click on an empty area and choose the create infoArea option.
4) Data Source:
A BW DataSource is a structure which is created in a source system and replicated to the BW system. For non-SAP source system, the DataSource is created directly in BW. After creating a DataSource, it must be activated in order for it to be available in SAP BW.
There are four types of DataSources;
DataSources for Characteristic attributesØ
DataSouces for Characteristic textsØ
DataSources for characteristic hierarchiesØ
DataSouces for transaction data.Ø
The maximum number characters allowed for the DataSource technical name is 32.
5) Process Chain:
The goal of process chains is to execute repetitive processes automatically according to predefined rules. Some of the processes trigger separate events that can, in turn, start other processes. The process chains are accessible from the Modelling or the Administration section of the DWW. Using a process chain, a user can;
a) Automate the complex schedules in BW with the help of the event-controlled processing ,
b) Visualize the processes by using network graphics and
c) Centrally control and monitor the processes.
6) Staging Engine:
Data staging refers to the process of cleansing, manipulating and homogenizing data. In SAP BW, a staging engine is used for cleansing, manipulating and homogenizing data within the DataSource.
7) PSA (Persistent Staging Area)
The PSA is the inbound storage area in BI for data from the source systems. The requested data is saved in the original form they were in the source systems. The PSA actually represent the entry layer for the BW system, data is then extracted from the source system using the InfoPackage.
The temporary storage facility in the PSA also allows you to check and change the data before the update into targets.
8) Admin Workbench:
The Administrator Workbench for SAP BW is the main tool for tasks in the data warehousing process. It provides data modelling functions as well as for control, monitoring and
maintenance of all processes in SAP BW having to do with procurement, data retention and data processing.
InfoCubes are the main objects used for reporting and analytics purposes. InfoCube design is based on a multidimensional modeling implemented in a star schema. The fact table which is at the center of the star schema contains the key figures and the dimension IDs. An infoCube must contain at least four dimensions. SAP BW automatically assigns three of them;
The data packageØ
The fourth dimension is user defined.
The limitations of the InfoCube schema is that it has a maximum of 16 dimensions, the act table can contain up to 233 key figures and 248 maximum characteristics for each dimension.
There are four types of InfoCubes namely;
Standard InfoCubes used and optimized for read access or reporting.Ø
Real-time InfoCubes are those that can be loaded via interface. Data is written and read concurrently.Ø
VirtualProvoder InfoCubes do not store data; they link to the data in a source system.Ø
Semantically Partitioned InfoCubes consists of smaller InfoCubes automatically partitioned by the system.Ø
10) DSO (DataStore Objects):
A DSO stores data in transparent tables. Data is extracted and unified at a very detailed level and for that reason, DSOs are not optimized for reporting purposes. DSOs has two types of components, Key fields and data fields. Key fields are InfoObjects that uniquely identify each line. Data fields contain characteristics and key figures loaded from the operational system. Different types of DSO objects are;
Standard DSO; most common DSO, consists of three table, Activation Queue table, Active Data table and the Change log table.Ø
Write-optimized DSO; optimized for fast writing and consist of only the Active Data table.Ø
Direct Update DSO; here data is loaded using an API interface supplied by SAP BW.Ø
In-memory DSO; the standard DSO that works only with SAP HANA.Ø
Semantically Partitioned DSO; the standard DSO partitioned into smaller DSOs according to selected key fields.Ø
11) OLTP (Online Transaction Processing):
These are operational systems dedicated to the company’s business to assist in daily management of tasks and they are therefore directly operational. OLTP systems are used to facilitate and manage transaction-oriented applications, typically for data entry and retrieval transaction processing. The data contained in the OLTP system are current, continual, dynamic and constantly updated. It could be read, written and updated.
12) OLAP (Online Analytical Processing):
These are systems that provide key information to the management of a company. This information is needed to make appropriate business decisions based on the data available internally or externally to the company. The information system that can process this type of data is commonly called a data warehouse. Data required for decision processes is derived from various sources and aggregated. Thus data is extracted, transformed and loaded (ETL process) from an operational system such as SAP ERP to an analytical one for more complex analyses. In the OLAP system, data is summarized, historical, de-normalized with few tables in a star schema. The query is usually complex and ad-hoc.
13) Star Schema:
The star schema is the most commonly used multi-dimensional model for OLAP implementation. The data cube is stored as a star model. The star has a fact table at its center, surrounded by several dimension tables. The fact table contains two kinds of attributes: the primary keys to dimension table and the measures. Dimension tables correspond to axes of the cube. The number of the dimensions and the number of measures are limited only by the technology used to implement the schema.
14) Data Warehouse:
A data warehouse is a time variant, integrated, non-volatile and subject oriented collection of data in support of a management’s decision making process.
It can be said to be a system with its own database. It draws data from diverse sources and is designed to support query and analysis. Sometimes, where only a portion of detailed data is required, it may be worth considering using a data mart. A data mart is generated from the data warehouse and contains data focused on a given subject and data that is frequently accessed or summarized.