Info Cube
Info Cube is
structured as Star Schema (extended) where a fact table is surrounded by
different dim table that are linked with DIM'ids. And the data wise,
you will have aggregated data in the cubes.
Infocube
contains maximum 16(3 are sap defines and 13 are customer defined)
dimensions and minimum 4(3 Sap defined and 1 customer defined)
dimensions with maximum 233 key figures and 248 characteristic.
The following InfoCube types exist in BI:
. InfoCubes
. VirtualProviders
There are two subtypes of InfoCubes: Standard, and Real-Time. Although both have an extended star schema design, Real-Time InfoCubes (previously called Transactional
InfoCubes) are optimized for direct update, and do not need to use the
ETL process. Real-Time InfoCubes are almost exclusively used in the BI
Integrated Planning tool set. All BI InfoCubes consists of a quantity of
relational tables arranged together in a star schema.
Star Schema
In
Star Schema model, Fact table is surrounded by dimensional tables. Fact
table is usually very large, that means it contains millions to
billions of records. On the other hand dimensional tables are very
small. Hence they contain a few thousands to few million records. In
practice, Fact table holds transactional data and dimensional table
holds master data.
The
dimensional tables are specific to a fact table. This means that
dimensional tables are not shared to across other fact tables. When
other fact table such as a product needs the same product dimension data
another dimension table that is specific to a new fact table is needed.
This
situation creates data management problems such as master data
redundancy because the very same product is duplicated in several
dimensional tables instead of sharing from one single master data table.
This problem can be solved in extended star schema.
Extended star schema
In
Extended Star Schema, under the BW star schema model, the dimension
table does not contain master data. But it is stored externally in the
master data tables (texts, attributes, hierarchies).
The
characteristic in the dimensional table points to the relevant master
data by the use of SID table. The SID table points to characteristics
attribute texts and hierarchies.
This
multistep navigational task adds extra overhead when executing a query.
However the benefit of this model is that all fact tables (info cubes)
share common master data tables between several info cubes.
Moreover
the SID table concept allows users to implement multi languages and
multi hierarchy OLAP environments. And also it supports slowly changing
dimension.
MultiProvider
A MultiProvider is a special InfoProvider that combines data from several InfoProviders, providing it for reporting. The MultiProvider itself (InfoSets and VirtualProviders) does not contain any data. Its data comes exclusively from the InfoProviders on which it is based. A MultiProvider can be made up of various combinations of the following InfoProviders:. InfoCubes
. DataStore objects
. InfoObjects
. InfoSets
. Aggregation levels (slices of a InfoCube to support BI Integrated Planning)
Use
A BEx query can only be written against a single InfoProvider. A MultiProvider is a single InfoProvider to a query but through it, multiple providers can be indirectly accessed.
DataStore object
Since a DataStore object
is designed like a table, it contains key fields (document number and
item, for example) and data fields. Data fields can not only be key
figures but also character fields (order status,
customer, or time, for example). You can use a delta update to update
DataStore object data into connected InfoCubes or into additional
DataStore objects or master data tables
(attributes or texts) in the same system or in different systems. In
contrast to multidimensional DataStores for InfoCubes, data in DataStore
objects is stored in flat, transparent database tables. Fact and dimension tables are not created.
With DataStore objects, you can
not only update key figures cumulatively, as with InfoCubes, but also
overwrite data fields. This is especially important for
transaction-level documents that change in the source system. Here,
document changes not only involve numerical fields, such as order
quantities, but also non-numerical ones such as ship-to parties,
delivery date, and status. Since the OLTP system overwrites these
records when changes occur, DataStore objects must often be moceled to
overwrite the corresponding fields and update to the current value in
BI.
DS Oject Types
SAP BI distinguishes between three
DataStore object types: Standard, Write Optimized, and Direct Update.
These three flavors of DataStore Objects are shown in the following
figure.
1. The Standard DataStore Object
consists of three tables (activation queue, active data table, and
change log). It is completely integrated in the staging process. In
other words, data can be loaded into and out of the DataStore Objects
during the staging process. Using a change log means that all changes
are also written and are available as delta uploads for connected data
targets.
Architecture and Functions of Standard DataStore Objects
Architecture and Functions of Standard DataStore Objects
Standard DataStore objects consist of three tables:
Active Data table
This is where the current status
of the data is stored. This table contains a semantic
(business-related) key that can be defined by the modeler (order number,
item, or schedule line, for
example). It is very important that the key be correctly defined by the
modeler, as a match on the key initiates special delta processing during
the activation phase (discussed later). Also, reporting via the BEx
uses this table.
Change Log table
During the activation run, changes are stored in the change log. Here, you can find the complete
history of the changes, since the content of the change log is not
automatically deleted. The connected targets are updated from the change
log if they are supplied with data from the DataStore object in the
delta method. The change log is a PSA table and can also be maintained
in the PSA tree of the Data Warehousing Workbench. The change log has a
technical key consisting of a request, data package, and data record
number.
Activation Queue table
During
the DTP, the records are first written to this table. This step is
necessary due to the complex logic that is then required by the
activation process.
Schema for a Standard DataStore Objects
2. Write optimized is a new kind of DataStore Object . It is targeted for the warehouse level of the architecture, and has the advantage of quicker loads.
3. A direct update DataStore object
(previous 3.x transactional ODS) has only the table with active data.
This means it is not as easily integrated in the staging process.
Instead, this DataStore object type is filled using APIs and can be read
via a BAPI.
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