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Copyright by SAP AG
FIGURE 1-17 Architecture of the BI platform services and warehouse components
Data Acquisition Layer: Architecture
Datasource/Persistent Staging Area DB Connect UD Connect BI Service API File Interface Web Service
BAPI not available for SAP NetWeaver 2004s Datasources
MultiDimensional Source
e.g., Hyperion
SAP Source
e.g., SAP CRM
File
Legacy Applications
e.g., CSV
Typically from Internet Based Applications
Source System Types
Copyright by SAP AG
FIGURE 1-18 Data acquisition
Data Flow Control/Process Chains
1:
SAP Business Intelligence Overview
is XML-based communication using HTTP. The application-specific contents are transferred in messages in user-defined XML from the sender to the receiver using the Integration Server (part of SAP XI). Another datasource would be non-SAP systems; the BI architecture offers the ability to integrate with any external source of data. One of the methods within BI to facilitate this process is the Open Hub Service. This functionality offers the ability to link external file systems to BI, then upload the data directly into another database file or flat file (Excel document) from the InfoCube. You can also access information from one BW and upload it to another BW (specifically to a Data Target in that BW system) system or share information within a BW system with other data targets in that same system (InfoCube-toInfoCube exchange of data DataMarting) so that you can stage the granularity of the data in the BI system in storage layers. Another method of uploading data into the BI data warehouse is via the use of DB Connect, which offers direct connections to an Oracle Database system. For example, the DB Connect functionality offers a direct connection to a legacy Oracle DB and the ability to create a series of structures that directly link the BI objects to the Oracle DB and offer direct uploads of data. For more complex uploads, you can use the UDI (Universal Data Integration), which offers an integration process similar to the use of a third-party system, to integrate BW with another source of data such as Hyperion. ETL is the process of extracting, transforming, and loading data. It describes the process of moving data from one source system to another. The source system types describe the extraction activity (see Figure 1-18). In the data extraction process, the transformation process can take place. This can be any sort of manipulation of the raw data cleansing of the data, aggregation of the data, and other activities to make the data ready and usable in the BI system. As of 7.0 BI, this process is enabled with a new drag-and-drop visual transformation tool for configuration purposes. There are numerous topics in this area, such as the management of delta uploads, full uploads of data, real-time sourcing of data (new for 7.0 BI), currency conversion, and more. These are critical topics that are too involved to get into during this overview, but they are important to understand and manage. Transformation of the data can involve the summary of the data, enhancing the data (adding more characteristics to a record for example, if customer is A100, then customer group is R45), developing calculations that occur during the upload (for example, based on sales volume, a salesperson receives a bonus; if the sales volume is $1,000,000, the salesperson receives a bonus of $10,000, and that calculated bonus is included in the record), or any number of different realignments of the data. This is important to understand so that as you query the information, you understand what has happened to the data between the source system tables and the InfoCube you are querying against for strategic information.
Data Transformation
The InfoPackage (IP) and the Data Transfer Process (DTP) are the objects used to execute the transfer of data through the transformation process. As you can see in Figure 1-19, these two items can be used to manage the data loads by filtering the data uploaded (for example, only upload the data from North America) or to manage a check for data consistency, among other responsibilities. The PSA (Persistent Staging Area) is the physical storage level that holds a set of data that is exactly what the source system has, and it can therefore be used for a number of tasks, such as a consistency checks against the data in the source system table to validate the accuracy of the uploaded data. Figure 1-20 shows the configuration screen from the SAP BI Transformation Rule. The basic process is to map the fields from the source system to the fields in the target system.
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