Showing posts with label BI. Show all posts
Showing posts with label BI. Show all posts
Wednesday 12 September 2012
SAP BW Architecture
Theoretically, SAP BW Architecture can be divided into 3 layers: Sources System, SAP BW Server and SAP BW OLAP.
SAP BW Architecture
Source system of SAP BW Architecture:
Source system is a reference system that functions as data provider for SAP BW.
There four type source system than can be SAP BW provider:
mySAP.com components: SAP BW is fully integrated into mySAP.com component. Currently, predefined extraction structures and program from mySAP.com component are delivered by SAP. Therefore, we can upload data source from mySAP.com component directly into SAP BW.
Non-SAP systems: SAP BW is an open architecture that can interface vis-à-vis tp external OLTP or legacy system. Based on information or data collected from heterogeneous system, SAP BW has possibility to be a consolidated data basis reporting to support management in decision making.
Data Providers: Market research result from, for example, AC Nielsen USDun & Bradstreet can be loaded into SAP BW. The uploaded information can be used as benchmarking and measurement again our operative data.
External databases: Data from external relational database can be loaded to SAP BW too.
SAP BW Server
SAP BW Server has several features:
Administration Service: The administration service is responsible for organization within SAP BW. Administration service is available through the Administrator Workbench (AWB), a single point of entry for data warehouse development, administration and maintenance tasks in SAP BW.
Metadata Service: SAP BW metadata services component provide both an integrated metadata repository where all metadata is stored and a Metadata Manager that handles all requests for retrieving, adding, changing, or deleting meta data.
Extraction, Transformation, and Loading (ETL) Services: Depending on the source systems and type of data basis, the process of loading data into the SAP BW is technically supported in different ways.
Storage Services: the storage service (known as SAP BW Data Manager) manages and provides access to different data target available in SAP BW, as well as aggregates stored in relational or multidimensional database management system.
SAP BW OLAP
The Online Analytical Processing (OLAP) layer allows us to carry out multi-dimensional analysis of SAP BW data sets. In principle, the OLAP layer can be divided into three components:
BEx Analyzer (Microsoft Excel Based)
BEx Web Application
BEx Mobile Intelligence.
Classic and Extended Star Schema Comparisons
In Classic star schema, dimension and master data table are same. But in Extend star schema, dimension and master data table are different. (Master data resides outside the Infocube and dimension table, inside Infocubecube).
In Classic star schema we can analyze only 16 angles (perspectives) whereas in extended star schema we can analyze in 16*248 angles. Plus the performance is faster to that extent.
Below are some of the basic differences between the two.
Classic Star Schema SAP BW Star Schema
Cube InfoCube
Fact Table Key Figure or KPI
Dimension Attribute Characteristic, Attributes, Hierarchy Node
Dimension Table Dimension Table, Master Data Table, External Table, SID Table
- Standard Business Content
- Hierarchies
- MultiCube
- Remote Cube
Advantages of Classic Star Schema
Data access runs performantly due to the small number of joining operations.
There are only join operations between the fact tables and the involved dimension tables.
Disadvantages of Classic Star Schema
Redundant entries exist in the dimension tables
Historization of dimensions is not easy to model. The dimension changing may be done slowly.
No multi language capability
It is difficult to model some hierarchy’s dimension.
Query performance is also made worse, since aggregates and fact data are stored in the same table (fact table).
Advantages of the SAP BW Star Schema
Faster access to data than via long alpha-numeric keys. SAP BW use automatically generated INT4 keys for SID and Dimension ID
Can model in easy way: Historizing, multi-lingual, and shared dimensions. It is happen because of the excavation of master data from the dimension tables using the SID technique.
The query performance is improved here as aggregated key figures can be stored in their own fact tables.
OLAP and OLTP
Online Transaction Processing (OLTP) refers to a class of systems that facilitate and managetransaction-oriented applications, typically for data entry and retrieval transaction processing.
On Line Analytical Processing (OLAP), a series of protocols used mainly for business reporting. Using OLAP, businesses can analyze data in all manner of different ways planning, simulation, data warehouse reporting, and trend analysis.
OLAP and OLTP are two absolutely different systems since they have different purpose and environments. OLAP for analytical compare to OLTP for transactional.
Difference between OLAP and OLTP
Target
OLTP is used in operative environment to get efficiency through automation of businessprocesses. OLAP is used in informative environment, usually used by management to support in decisions making.
Priorities
As transactional system, OLTP has high availability and higher data volume.OLAP as analytical system is very simple data and has flexible data access.
Level of detail
OLTP stores data in a very high level of detail, whereas OLAP stores data in aggregation.
Age of data
OLTP data are current data. It means the data stored in OLTP with minimal history. OLAP data are historical data.
Database operation
Frequent data changes are a feature of operative system. So, in OLTP system we can read, add, change, delete or refresh data. In OLAP, we only can read the data since they are frozen after a certain point for analysis purpose.
Integration of data from various applications (system)
Since the OLTP system is for operation, it has minimal integration with other applications. In contrast to the OLTP system, OLAP need high integration of information from many application or system because it used for analysis.
Normalization in database
Due to reduction in data redundancy, normalization is very high requirement in OLTP. In OLAP, typically de-normalized with fewer tables; use of extended star schema and lower performance.
SAP BI Terminology
InfoArea
Info Area is like “Folder” in Windows. InfoArea is used to organize InfoCubes, InfoObjects, MultiProviders, and InfoSets in SAP BW.
InfoObject Catalog
Similar to InfoArea, InfoObject Catalog is used to organize the InfoObject based on their type. So we will have InfoObjects Catalogs of type Characteristics & KeyFigures.
Info Objects
It is the bsic unit or object in SAP BI used to create any structures in SAP BI.
Each field in the source system is referred as InfoObject on SAP BI.
We have 5 types of Info Objects: Characteristic, KeyFigure, Time Characteristic, Unit Characteristic, and Technical Characteristic.
Data Source
Data Source defines Transfer Structure.
Transfer Structure indicates what fields and in what sequence are they being transferred from the source system.
We have 4 types of data source:
Attr: used to load master data attr
Text: Used to load text data
Hier: used to load hierarchy data
Transcation data: used to load transaction data to Info cube or ODS.
Source System
Source system is an application from where SAP BW extracts the data.
We use Source system connection to connect different OLTP applications to SAP BI.
We have different adapters / connectors available:
SAP Connection Automatic
SAP Connection Manually
My Self Connection
Flat file Interface
DB connect
External Systems with BAPI
Info Package
Info package is used to schedule the loading process.
Info package is specific to data source.
All properties what we see in the InfoPackage depends on the properties of the DataSource.
Administrator Workbench (AWB) in SAP BW
The Administrator Workbench (AWB) is the tool for data warehouse management in SAP BW (Business Information Warehouse). Using AWB we can manage, control and monitor all relevant objects and processes in SAP BW including scheduling, data load monitoring and metadata maintenance. Following are the functions that we can perform using AWB:modeling, monitoring, transport connection, documents, business content, translation, and metadata repository.
Modeling
Here we can create and maintenance objects relevant to the data staging process in SAP BW. For example, create InfoProvider, InfoObject, InfoSource, maintenance and define source system and PSA.
Transaction code (T-Code): RSA1
Monitoring
In monitoring Function area, we can monitor and control data loading process and other data process in SAP BW.
Transaction code (T-Code): RSMON
Transport Connection
This function is used to maintenance and move object between SAP systems: development to quality assurance (QA/Test) and QA to production.
Document
The document function area, we can maintenance links for one or more documents in various formats, versions and languages for SAP BW Objects.
Business Content
Pre-configured information models based on metadata is maintenance in this area. Business Content Function are provides us a selection of information that we can use to fulfill our tasks.
Transaction code (T-Code): RSORBCT.
Translation
The use of translation function area is to translate short and long texts belonging to SAP BW- objects.
Metadata Repository
In the HTML-based SAP BW- Metadata Repository, all SAP BW meta objects and the corresponding links to each other are managed centrally. Together with an integrated Metadata Repository browser, a search function is available enabling a quick access to the meta objects. In addition, metadata can also be exchanged between different systems, HTML pages can be exported, and graphics for the objects can be displayed.
Transaction code: RSOR.
Modeling, monitoring, transport connection, documents, business content, translation, and metadata repository are tasks or functions that we can do in SAP BW Administrator Workbench (AWB).
Tuesday 11 September 2012
Step-by-Step: From the Data Model to the BI Application in the Web
Task
This tutorial guides you step-by-step through the basic procedures for creating a simple but complete SAP NetWeaver BI scenario. Complete means that you create a simple data model, define the data flow from the source to the BI store of your data model, and then load data or enter data directly in the BI system. To be able to analyze the data, you then create a Web-based BI application that you broadcast by E-mail to your employees.
The company in our scenario produces laptops, PCs and computer accessories, and distributes its products over various channels. An advertising campaign for the Internet distribution channel was started in July by the marketing department. The success of the campaign is to be checked in October of the same year in order to decide whether and how the campaign should be continued. A revenue report containing the data of the past quarter and showing the revenue for the various distribution channels during this time is therefore required.
Objective
At the end of the tutorial you will be able to perform the following tasks:
● Create a simple BI data model with InfoObjects (characteristics, key figures) and an InfoCube for storing data in the BI system.
In our scenario, the "container" for the revenue data is an InfoCube. It consists of key figures and characteristics. The key figures provide the transaction data to be analyzed, in our case sales figures and amounts. The characteristics are the reference objects for the key figures; in our scenario these are Product, Product Group and Channel. They contain the master data, which remains unchanged over a long period of time. The master data of the characteristics in this scenario can be attributes and texts.
You create the data model in the following steps:
● Map the source structure of the data in the BI system and define the transformation of the data from the source structure to the target format. In this way you will be able to define the data flow in the BI system.
The structure and properties of the source data are represented in the BI system with DataSources. In our scenario, we need DataSources to copy master data for the characteristic Product as well as sales data from the relevant file to the entry layer of the BI system.
The transformations define which fields of the DataSource are assigned to which InfoObjects in the target and how the data is transformed during the load process. In our simple scenario, the transformations are kept simple and do not contain any complex rules. The assignment is direct, that is the fields of the source are copied to the InfoObjects of the target one-to-one.
You create the necessary objects for defining the data flow in the following steps:
○ Creating DataSources for Master Data of Characteristic "Product“
○ Creating DataSources for Transaction Data
○ Creating Transformations for Master Data from Characteristic „Product“
○ Creating Transformations for InfoCubes
● Load the data.
The load processes are executed using InfoPackages and data transfer processes. The InfoPackages load the data from the relevant file into the DataSource, and the data transfer processes load the master data from the DataSource into the characteristic Product or the transaction data into the InfoCube. When the data transfer process is executed, the data is subject to the corresponding transformation. For the characteristicsProduct Group and Channel, we show that it is also possible to load small amounts of master data directly in the BI system instead of from the source. In this case neither DataSources and transformations nor InfoPackages and data transfer processes are required.
You create the necessary objects for loading data in the following steps:
○ Creating Master Data Directly in the System
○ Loading Master Data for Characteristic "Product"
○ Loading Transaction Data
● Define a query that is used as the basis for a Web application and allows for an ad-hoc analysis of the data in the Web.
You create the query in the following step:
○ Defining Queries
● Create a Web application with navigation options and functions, such as printing based on the query.
You create the Web application in the following step:
○ Creating Web Applications
● Analyze the data in the Web application, add comments to it, and broadcast it by E-mail to other employees.
You analyze and broadcast the data in the following steps:
○ Analyzing Data in the Web Application
○ Broadcasting Web Applications by E-Mail
Refer the Below link for More Information
What Is Business Intelligence?
The Purpose of Business Intelligence
During all business activities, companies create data. In all departments of the company, employees at all levels use this data as a basis for making decisions. Business Intelligence (BI) collates and prepares the large set of enterprise data. By analyzing the data using BI tools, you can gain insights that support the decision-making process within your company. BI makes it possible to quickly create reports about business processes and their results and to analyze and interpret data about customers, suppliers, and internal activities. Dynamic planning is also possible. Business Intelligence therefore helps optimize business processes and enables you to act quickly and in line with the market, creating decisive competitive advantages for your company.
Key Areas of Business Intelligence
A complete Business Intelligence solution is subdivided into various areas. SAP NetWeaver Business Intelligence (SAP NetWeaver BI) provides comprehensive tools, functions, and processes for all these areas:
A data warehouse integrates, stores, and manages company data from all sources.
If you have an integrated view on the relevant data in the data warehouse, you can start the analysis and planning steps. To obtain decisive insights for improving your business processes from the data, SAP NetWeaver BI provides methods for multidimensional analysis. Business key figures, such as sales quantities or revenue, can be analyzed using different reference objects, such as Product, Customer or Time. Methods for pattern recognition in the dataset (data mining) are also available. SAP NetWeaver BI also allows you to perform planning based on the data in the data warehouse.
Tools for accessing and for visualization allow you to display the insights you have gained and to analyze and plan the data at different levels of detail and in various working environments (Web, Microsoft Excel).
By publishing content from BI, you can flexibly broadcast the information to all employees involved in your company's decision-making processes, for example by e-mail or using an enterprise portal.
Performance and security also play an important role when it comes to providing the information that is relevant for decision-making to the right employees at the right time.
Preconfigured information models in the form of BI Content make it possible to efficiently and cost-effectively introduce SAP NetWeaver BI.
The following sections give an overview of the capabilities of SAP NetWeaver BI in these areas. You can find out more about the tools, functions, and processes provided by SAP NetWeaver BI using the links to more detailed information in the documentation.
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