sap hana information modeler
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SAP HANA Information Modeler
Information Models are multiple database views of transactional data stored in the physical tables of SAP
HANA Database used for Analytical purposes. Analytical Data Modeling is only Possible ForColumn
Tables i.e. Information Modeler only works with column storage tables. For that reason Replication Server creates SAP HANA tables in column store by default. Data Services also
creates target tables in column store as default for SAP HANA database. The SQL command to create
column table: "CREATE COLUMN TABLE Table_Name..". Also the data storage type of a table can be
modified from Row to Column storage with the SQL command "ALTER TABLE Table_Name COLUMN".
We can Choose to publish and consume SAP HANA tables data at four levels of modeling using SAP HANA
Studio Information Modeler Perspective.They are Attribute View, Analytic View,Calculation
View and Analytic Privilege. These content data models are basically the combination
of Attributes and Measures.
Attributes
Attributes are individual non-measurable analytical elements. Attributes add context to data. These are
qualitative descriptive data similiar to Characteristics of SAP BW. For example, MATERIAL_NAME. There are
three types of Attributes in Information Modeling:
Simple Attributes are individual non-measurable analytical elements that are derived from the data
foundation. For example, MATERIAL_ID and MATERIAL_NAME are attributes of a MATERIAL subject
area.
Calculated Attributes are derived from one or more existing attributes or constants. The attribute is
based on static value or dynamic calculation. For example, extracting the year part from the
customer registration date, assigning a constant value to an attribute which can be used for
arithmetic calculations.
Private Attributes are used to model Analytic Views and cannot be used outside the view. Private
attributes add more information to the data model. Private attributes of Fact tables are used to link
to the subject area or dimensions i.e. Attribute Views. For example, we create an analytic view
ANV_SALES to analyze the sales of materials, and select MATERIAL_ID as a private attribute from
the database table SALES_ITEM. In this case, MATERIAL_ID could be used only for modeling data for
ANV_SALES. We will learn about private attributes later when we will design Analytic Views.
Measures
Measures are simple measurable analytical elements. Data that can be quantified and calculated are called
measures. They are similar to Key Figures in SAP BW. Measures are defined in Analytic and Calculation
Views. Three types of measures can be defined in Information Modeling:
Simple Measure is a measurable analytical element that is derived from the data foundation i.e.
defined in the fact table. For example, SALES_AMOUNT.
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Calculated Measures are defined based on a combination of data from OLAP cubes, arithmetic
operators, constants, and functions. For example, Net Revenue equals Gross Revenue - Sales
Deduction, assigning a constant value to a measure for some calculation.
Restricted Measures are used to filter the value based on the user-defined rules for the attribute
values. For example, Gross Revenue of a material for country = US.
Attribute View Attribute Views are the Reusable Dimensions or subject areas used for business analysis. Attribute Views
are defined in Information Modeling to separate Master Data Modeling from Fact data. Examples of
Attribute Views can be Customer, Material, Time. We define the Key or Non-key Attribute of the physical
database tables of Master Data. We can join Text tables to Master data tables or two Master data tables
like product and product group. Also tables can be selected from multiple schemas and are not restricted
to one schema per Attribute View. Activated Attribute Views can be consumed for reporting or can be
linked to the fact tables in Analytical Views.
Table Joins and PropertiesThe various Join Types available while modeling Attribute Views are Referential, Inner, Left Outer, Right
Outer and Text Join. Apart from that the Join Condition and Cardinality ( 1:1, 1:N or N:1 ) needs to be
defined accordingly. If we select the Join Type as Text Join then we need to define the Language Column
and Description Mapping. The Output structure of the Attribute View must be explicitly defined. At least one Key Attribute is
mandatory. However any number of Non-key Attributes may be defined. We can also apply
static Filter values ( List of Values ) on any columns of the tables selected in the Attribute View. Also this
column does not need to selected as a Non-key Attribute for output.
Hierarchies
Hierarchies are used to structure and define the relationship between attributes of an Attribute View that
are used for business analysis. Exposed models that consist of attributes in hierarchies simplify the
generation of reports. For example, consider the TIME Attribute View with YEAR, QUARTER, and MONTH
attributes. We can use these YEAR, QUARTER, and MONTH attributes to define a hierarchy for the TIME
Attribute View. Two types of hierarchies are supported in Attribute Views of Information Modeler:
Level Hierarchy - This hierarchy is rigid in nature, where the root and the child nodes can be
accessed only in the defined order. This need one attribute per hierarchy level and number of levels
defined are fixed. For example, COUNTRY, STATE and CITY.
Parent/Child Hierarchy - This hierarchy is very similar to BOM (Parent and Child) and Employee
Master(Employee and Manager). The hierarchy can be explored based on a selected parent, and
there can be cases where the child can be a parent. This hierarchy is derived based on the value.
Variable number of levels for sub-trees within the hierarchy is possible. For example, EMPID,
MGRID.
At present, Hierarchies defined in the Information Modeler are only accessible via MDX. Which means that
at present such hierarchies can only be used from MS Excel.
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Time Dimension Attribute View
Two types of Time Dimension Attribute Views are supported in Information Modeler. ForGregorian type
Time Dimension the data is stored in _SYS_BI.M_TIME_DIMENSION. For Fiscaltype Time Dimension data is
stored in _SYS_BI.M_FISCAL_CALENDAR. Time Dimension Attribute Views are very often used while
defining the Analytical View.
Analytic View
Analytic views are the Multidimensional Views or OLAP cubes. Analytic Views are used to analyze values
from a single fact table of the data foundation based on the related attributes from the Attribute Views,
looks very similar to Star Schema. We create a Cube-like view by joining Attribute Views to the Fact table
data. For example, total sales of a material in a given region at a given time.
Data Foundation & Logical View
In the Data Foundation tab we need to select the physical fact table. Next we define the <attributes< b="" style="color: rgb(68, 68, 68); font-family: 'Lucida Grande', 'Lucida Sans Unicode',Helvetica, Arial, sans-serif; font-size: 12px; font-style: normal; font-variant: normal; font-
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Measure. In the Output structure the attributes of the fact table will appear under the Private
Attributes as these as related only with the fact table. Optionally we can apply static Filter values
on attributes of the fact table. We can also define Calculated Measures or Restricted Measureswhile designing the data foundation. Optionally we can also join database tables. We can select
attributes from several tables but they must be joinable. But we can select measurse from onlyone table( transactional data ).In the Logical View tab we can join as many Attribute Views from any package to the Data
Foundation. Attribute views are joined to the Private Attributes of the Data Foundation.
Typically we include all key attributes of the Attribute View in the join definition. The default
join type isInner Join and the default Cardinality being N:1.
The foundation view shows the physical table with all fields that can be incorporated in to the
final model. The logical view displays only those fields which have been choosen to be included
in the data model including the restricted and calculated measures defined.
Calculation View
Calculation Views are used to create data foundation using database tables, Attribute Views,
Analytic Views, and Calculation Views to address a complex business requirement. If joins are
not sufficient, we create a calculation view with SQLScript. Also Calculation Views are requiredif the Key Figures span across tables. A Calculation View is a composite column view visible to
the reporting tools. When the view is accessed a function is implicitly executed. Calculation
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Views can be modeled via Graphical or SQL Script. Calculation Views support UNION. An
example, comparing the sales of a material in a particular region for the last two years.
Analytic Privilege
Defines Privileges to partition data among various Users sharing the same Data Foundation.
Analysis Authorizations for row-level security can be based on Attributes in an Analytic Views.The SAP HANA database supports Analytic Privileges that represent filters or hierarchydrilldown limitations for analytic queries. Analytic Privileges grant access to values with a
certain combination of dimension attributes. For example, if we want to restrict access to a cube
with sales data to values with dimension attributes of region = US and year = 2010. As AnalyticPrivileges are defined on dimension attribute values and not on metadata, they are evaluated
dynamically during query execution.
Package
SAP HANA Packages are used to Group various related information objects in a structured way.Attribute Views do not need to be in the same package while defining Analytic View in some
other package. Packages do not restrict access to Information objects for Modeling.
Procedure
SAP HANA Database Stored Procedure defines sets of SQL statements that can process data.
We will learn how to write Procedures at a later section. Anyhow these follows the same
constructs like T-SQL of Microsoft SQL Server or PL/SQL of Oracle database.
Notes
On Activation of the Information Models database Column Views are created in theschema _SYS_BIC. These Column Views can be accessed from reporting tools. Analytic Views
do not store data. Data is read from the joined database tables. Joins and calculated measures areevaluated at runtime.
So typically while designing Information Models we will start with Creating a Package. Next we
will design Attribute Views, Analytic Views and Calculation Views within the Content Package.
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