What Is Cube Dimension?
A cube dimension is an instance of a database dimension within a cube is called as cube dimension. A database dimension can be used in multiple cubes, and multiple cube dimensions can be based on a single database dimension
Difference Between Database Dimension And Cube Dimension?
The Database dimension has only Name and ID properties, whereas a Cube dimension has several more properties.
Database dimension is created one where as Cube dimension is referenced from database dimension.
Database dimension exists only once. Where as Cube dimensions can be created more than one using ROLE PLAYING Dimensions concept.
What Is Molap And Its Advantage?
MOLAP (Multi dimensional Online Analytical Processing) : MOLAP is the most used storage type. Its designed to offer maximum query performance to the users. the data and aggregations are stored in a multidimensional format, compressed and optimized for performance. This is both good and bad. When a cube with MOLAP storage is processed, the data is pulled from the relational database, the aggregations are performed, and the data is stored in the AS database. The data inside the cube will refresh only when the cube is processed, so latency is high.
Advantages
Since the data is stored on the OLAP server in optimized format, queries (even complex calculations) are faster than ROLAP.
The data is compressed so it takes up less space.
And because the data is stored on the OLAP server, you don’t need to keep the connection to the relational database.
Cube browsing is fastest using MOLAP.
What Are Translations And Its Use?
Translation: The translation feature in analysis service allows you to display caption and attributes names that correspond to a specific language. It helps in providing GLOBALIZATION to the Cube.
What Is Hierarchy, What Are Its Types And Difference Between Them?
A hierarchy is a very important part of any OLAP engine and allows users to drill down from summary levels hierarchies represent the way user expect to explore data at more detailed level
hierarchies is made up of multipule levels creating the structure based on end user requirements.
->years->quarter->month->week ,are all the levels of calender hierarchy
They are 2 types of hierarchies they are
Natural hierarchy
Unnatural hierarchy
Natural hierarchy: This means that the attributes are intuitively related to one another. There is a clear relationship from the top of the hierarchy to the bottom.
Example: An example of this would be date: year, quarter and month follow from each other, and in part, define each other.
Unnatural hierarchy: This means that the attributes are not clearly related.
Example: An example of this might be geography; we may have country -> state -> city, but it is not clear where Province might sit.
What Are The Types Of Database Schema?
They are 3 types of database schema they are
Star
Snowflake
Starflake
What Is Star, Snowflake And Star Flake Schema?
Star schema: In star schema fact table will be directly linked with all dimension tables. The star schema’s dimensions are denormalized with each dimension being represented by a single table. In a star schema a central fact table connects a number of individual dimension tables.
Snowflake: The snowflake schema is an extension of the star schema, where each point of the star explodes into more points. In a star schema, each dimension is represented by a single dimensional table, whereas in a snowflake schema, that dimensional table is normalized into multiple lookup tables, each representing a level in the dimensional hierarchy. In snow flake schema fact table will be linked directly as well as there will be some intermediate dimension tables between fact and dimension tables.
Star flake: A hybrid structure that contains a mixture of star(denormalized) and snowflake(normalized) schema’s.
How Will You Hide An Attribute?
We can hide the attribute by selecting “AttributeHierarchyVisible = False” in properties of the attribute.
How Will You Make An Attribute Not Process?
By selecting “ AttributeHierarchyEnabled = False”, we can make an attribute not in process.
What Is Use Of Isaggregatable Property?
In Analysis Service we generally see all dimension has All member. This is because of IsAggregatable property of the attribute. You can set its value to false, so that it will not show All member. Its default member for that attribute. If you hide this member than you will have to set other attribute value to default member else it will pick some value as default and this will create confusion in browsing data if someone is not known to change in default member.
What Are Key, Name And Value Columns Of An Attribute?
Key column of any attribute: Contains the column or columns that represent the key for the attribute, which is the column in the underlying relational table in the data source view to which the attribute is bound. The value of this column for each member is displayed to users unless a value is specified for the NameColumn property.
Name column of an attribute: Identifies the column that provides the name of the attribute that is displayed to users, instead of the value in the key column for the attribute. This column is used when the key column value for an attribute member is cryptic or not otherwise useful to the user, or when the key column is based on a composite key. The NameColumn property is not used in parent-child hierarchies; instead, the NameColumn property for child members is used as the member names in a parent-child hierarchy.
Value columns of an attribute: Identifies the column that provides the value of the attribute. If the NameColumn element of the attribute is specified, the same DataItem values are used as default values for the ValueColumn element. If the NameColumn element of the attribute is not specified and the KeyColumns collection of the attribute contains a single KeyColumn element representing a key column with a string data type, the same DataItem values are used as default values for the ValueColumn element.
What Are The Difficulties Faced In Cube Development?
Giving attribute relationships
Calculations
Giving dimension usage (many to many relationship)
Analyzing the requirements
What Is Impersonation? What Are The Different Impersonation Options Available In Ssas?
Impersonation allows SSAS to assume the identity/security context of the client application which is used by SSAS to perform the server side data operations like data access, processing etc. As part of impersonation, the following options are available in SSAS
Use a specific Windows user name and password: This option lets you to specify Windows account credentials which will be used by SSAS to perform operations like source data access, processing etc.
Use the service account: When this option is selected, SSAS uses the credentials of the service account under which the Analysis Services service is configured/running for source data access, processing etc.
Use the credentials of the current user: When this option is set, SSAS uses the credentials of the current user for performing operations like DMX Open Queries, Local cubes etc. This option cannot be used for performing server side operations like source data access, processing etc.
Inherit: This option let's the SSAS server decide which impersonation mode is suitable for each type of operation. When this option is set, by default SSAS will use the service account for operations like processing and the credentials of the current user for operations like Local cubes, querying the data mining models, etc.
A cube dimension is an instance of a database dimension within a cube is called as cube dimension. A database dimension can be used in multiple cubes, and multiple cube dimensions can be based on a single database dimension
Difference Between Database Dimension And Cube Dimension?
The Database dimension has only Name and ID properties, whereas a Cube dimension has several more properties.
Database dimension is created one where as Cube dimension is referenced from database dimension.
Database dimension exists only once. Where as Cube dimensions can be created more than one using ROLE PLAYING Dimensions concept.
What Is Molap And Its Advantage?
MOLAP (Multi dimensional Online Analytical Processing) : MOLAP is the most used storage type. Its designed to offer maximum query performance to the users. the data and aggregations are stored in a multidimensional format, compressed and optimized for performance. This is both good and bad. When a cube with MOLAP storage is processed, the data is pulled from the relational database, the aggregations are performed, and the data is stored in the AS database. The data inside the cube will refresh only when the cube is processed, so latency is high.
Advantages
Since the data is stored on the OLAP server in optimized format, queries (even complex calculations) are faster than ROLAP.
The data is compressed so it takes up less space.
And because the data is stored on the OLAP server, you don’t need to keep the connection to the relational database.
Cube browsing is fastest using MOLAP.
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| TIBCO Software Most Frequently Asked Latest SSAS Interview Questions Answers |
Translation: The translation feature in analysis service allows you to display caption and attributes names that correspond to a specific language. It helps in providing GLOBALIZATION to the Cube.
What Is Hierarchy, What Are Its Types And Difference Between Them?
A hierarchy is a very important part of any OLAP engine and allows users to drill down from summary levels hierarchies represent the way user expect to explore data at more detailed level
hierarchies is made up of multipule levels creating the structure based on end user requirements.
->years->quarter->month->week ,are all the levels of calender hierarchy
They are 2 types of hierarchies they are
Natural hierarchy
Unnatural hierarchy
Natural hierarchy: This means that the attributes are intuitively related to one another. There is a clear relationship from the top of the hierarchy to the bottom.
Example: An example of this would be date: year, quarter and month follow from each other, and in part, define each other.
Unnatural hierarchy: This means that the attributes are not clearly related.
Example: An example of this might be geography; we may have country -> state -> city, but it is not clear where Province might sit.
What Are The Types Of Database Schema?
They are 3 types of database schema they are
Star
Snowflake
Starflake
What Is Star, Snowflake And Star Flake Schema?
Star schema: In star schema fact table will be directly linked with all dimension tables. The star schema’s dimensions are denormalized with each dimension being represented by a single table. In a star schema a central fact table connects a number of individual dimension tables.
Snowflake: The snowflake schema is an extension of the star schema, where each point of the star explodes into more points. In a star schema, each dimension is represented by a single dimensional table, whereas in a snowflake schema, that dimensional table is normalized into multiple lookup tables, each representing a level in the dimensional hierarchy. In snow flake schema fact table will be linked directly as well as there will be some intermediate dimension tables between fact and dimension tables.
Star flake: A hybrid structure that contains a mixture of star(denormalized) and snowflake(normalized) schema’s.
How Will You Hide An Attribute?
We can hide the attribute by selecting “AttributeHierarchyVisible = False” in properties of the attribute.
How Will You Make An Attribute Not Process?
By selecting “ AttributeHierarchyEnabled = False”, we can make an attribute not in process.
What Is Use Of Isaggregatable Property?
In Analysis Service we generally see all dimension has All member. This is because of IsAggregatable property of the attribute. You can set its value to false, so that it will not show All member. Its default member for that attribute. If you hide this member than you will have to set other attribute value to default member else it will pick some value as default and this will create confusion in browsing data if someone is not known to change in default member.
What Are Key, Name And Value Columns Of An Attribute?
Key column of any attribute: Contains the column or columns that represent the key for the attribute, which is the column in the underlying relational table in the data source view to which the attribute is bound. The value of this column for each member is displayed to users unless a value is specified for the NameColumn property.
Name column of an attribute: Identifies the column that provides the name of the attribute that is displayed to users, instead of the value in the key column for the attribute. This column is used when the key column value for an attribute member is cryptic or not otherwise useful to the user, or when the key column is based on a composite key. The NameColumn property is not used in parent-child hierarchies; instead, the NameColumn property for child members is used as the member names in a parent-child hierarchy.
Value columns of an attribute: Identifies the column that provides the value of the attribute. If the NameColumn element of the attribute is specified, the same DataItem values are used as default values for the ValueColumn element. If the NameColumn element of the attribute is not specified and the KeyColumns collection of the attribute contains a single KeyColumn element representing a key column with a string data type, the same DataItem values are used as default values for the ValueColumn element.
What Are The Difficulties Faced In Cube Development?
Giving attribute relationships
Calculations
Giving dimension usage (many to many relationship)
Analyzing the requirements
What Is Impersonation? What Are The Different Impersonation Options Available In Ssas?
Impersonation allows SSAS to assume the identity/security context of the client application which is used by SSAS to perform the server side data operations like data access, processing etc. As part of impersonation, the following options are available in SSAS
Use a specific Windows user name and password: This option lets you to specify Windows account credentials which will be used by SSAS to perform operations like source data access, processing etc.
Use the service account: When this option is selected, SSAS uses the credentials of the service account under which the Analysis Services service is configured/running for source data access, processing etc.
Use the credentials of the current user: When this option is set, SSAS uses the credentials of the current user for performing operations like DMX Open Queries, Local cubes etc. This option cannot be used for performing server side operations like source data access, processing etc.
Inherit: This option let's the SSAS server decide which impersonation mode is suitable for each type of operation. When this option is set, by default SSAS will use the service account for operations like processing and the credentials of the current user for operations like Local cubes, querying the data mining models, etc.
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