Like this: Like Loading Published by ish. Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in:. Email required Address never made public. Name required. Follow Following. Infocubes have a multidimensional structure with dimension tables max 16, 13 custom and one fact table. ODS store data at a more granular level. They have a unique feature "overwrite" which is absent in case of cubes. You can use ODS to load to cube further.
Anyway, one major difference is the manner of data storage. In ODS, data is stored in flat tables. It will accept duplicate records and during reporting, SUM the keyfigures up. Overwrite feature available while loading records Infocube - Stores summarized data, less granular.
It consists of flat transparent tables. In infocubes there are characteristics and keyfigures but in ods key fields and data fields. Some times we need detailed reports we can get through ODS.
ODS are used to store data in a granular form i. The data in the infocube is in aggregated form. From reporting point of view ods is used for operational reporting where as infocubes for multidimensional reporting. ODS are used to merge data from one or more infosources but infocubes does not have that facility.
The default update type for an ODS object is overwrite for infocube it is addition. ODS are used to implement delta in BW. Data is loaded into the ODS object as new records or updating existing records in change log or overwrite existing records in active data table using 0record mode.
You cannot load data using Idoc transfer method in ODS but u can do in infocube. You cannot create aggregate on ODS. You cannot create infosets on infocube. ODS objects can be used. When you want to use the facility of overwrite. Virtual providers allow only read access on BI data. VirtualProviders based on DTP. This type of Virtual Providers are based on the data source or an Info Provider and they take characteristics and key figures of source.
Same extractors are used to select data in source system as you use to replicate data into BI system. When only some amount of data is used. The data can be local or remote. This is used primarily for SEM application. Transformation process is used to perform data consolidation, cleansing and data integration. When data is loaded from one BI object to other BI object, transformation is applied on the data. Transformation is used to convert a field of source into the target object format.
Transformation rules are used to map source fields and target fields. Different rule types can be used for transformation. Real time data acquisition is based on moving data to Business Warehouse in real time. Data is sent to delta queue or PSA table in real time. To process data to InfoPackage and data transfer process DTP at regular intervals, you can use a background process known as Daemon.
Info Objects are created in Info Object catalog. It is possible that an Info Object can be assigned to different Info Catalog. To access data for reporting and analysis immediately after it is loaded. In BW 3. An InfoPackage is used to specify how and when to load data to BI system from different data sources. An InfoPackage contains all the information how data is loaded from source system to a data source or PSA.
InfoPackage consists of condition for requesting data from a source system. Note that using an InfoPackage in BW 3. In Extended Star schema one fact table can connect to 16 dimensions tables and each dimension table is assigned with maximum SID tables.
Each Dimension is represented by only one dimension and is not further normalized. DataStore object for direct update allows you to access data for reporting and analysis immediately after it is loaded. Data is stored in same format in which it was loaded to DataStore object for direct update by the application. In Write optimized DSO, data that is loaded is available immediately for the further processing.
Write optimized DSO provides a temporary storage area for large sets of data if you are executing complex transformations for this data before it is written to the DataStore object. The data can then be updated to further InfoProviders. You only have to create the complex transformations once for all data. Business rules are only applied when the data is updated to additional InfoProviders. It only contains table of active data and there is no need to activate the data as required with standard DSO.
This allows you to process the data more quickly. Infosets are defined as special type of InfoProviders where data sources contains Join rule on DataStore objects, standard InfoCubes or InfoObject with master data characteristics.
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