Explore topic-wise InterviewSolutions in .

STEPS to CREATE a CUBE in ssas:

1. Create a data source.
2. Create a datasource VIEW.
3. Create Dimensions
4. Create a cube.
5. Deploy and Process the cube.

Steps to create a cube in ssas:

The data SOURCE is the Physical CONNECTION information that ANALYSIS service USES to connect to the database that host the data. The data source contains the connection string which specifies the server and the database hosting the data as well as any necessary AUTHENTICATION credentials.

The data source is the Physical Connection information that analysis service uses to connect to the database that host the data. The data source contains the connection string which specifies the server and the database hosting the data as well as any necessary authentication credentials.

A named calculation is a SQL expression represented as a CALCULATED column. This expression appears and behaves as a column in the table. A named calculation LETS you extend the relational schema of EXISTING tables or views in a data source view without modifying the tables or views in the underlying data source.

Named calculation is used to create a new column in the DSV using HARD coded values or by using existing columns or even with both.

A named calculation is a SQL expression represented as a calculated column. This expression appears and behaves as a column in the table. A named calculation lets you extend the relational schema of existing tables or views in a data source view without modifying the tables or views in the underlying data source.

Named calculation is used to create a new column in the DSV using hard coded values or by using existing columns or even with both.

A data source view is a persistent SET of tables from a data source that supply the data for a particular cube. BIDS also includes a wizard for creating data source views, which you can invoke by right-clicking on the Data Source Views FOLDER in Solution EXPLORER.

1. DATASOURCE view is the logical view of the data in the data source.
2. Data source view is the only thing a cube can see.

A data source view is a persistent set of tables from a data source that supply the data for a particular cube. BIDS also includes a wizard for creating data source views, which you can invoke by right-clicking on the Data Source Views folder in Solution Explorer.

Named query in DSV is SIMILAR to View in Database. This is USED to CREATE Virtual table in DSV which will not impact the underlying database. Named query is MAINLY used to merge the two or more table in the datasource view or to filter COLUMNS of a table.

Named query in DSV is similar to View in Database. This is used to create Virtual table in DSV which will not impact the underlying database. Named query is mainly used to merge the two or more table in the datasource view or to filter columns of a table.

A named query is used to join MULTIPLE tables, to remove unnecessary columns from a table of a database. You can achieve the same in database using VIEWS but this Named Queries will be the best BET WHE you don’t have access to CREATE Views in database.

A named query is used to join multiple tables, to remove unnecessary columns from a table of a database. You can achieve the same in database using Views but this Named Queries will be the best bet whe you don’t have access to create Views in database.

By using NAMED calculations we can ADD a new COLUMN to an existing table in the data source VIEW.

By using named calculations we can add a new column to an existing table in the data source view.

This is very important interview question. The “Factless Fact Table” is a table which is similar to Fact Table except for having any measure; I mean that this table just has the links to the dimensions. These tables ENABLE you to track events; INDEED they are for RECORDING events.

Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. They are MERE key values with REFERENCE to the dimensions from which the stats can be collected

This is very important interview question. The “Factless Fact Table” is a table which is similar to Fact Table except for having any measure; I mean that this table just has the links to the dimensions. These tables enable you to track events; indeed they are for recording events.

Factless fact tables are used for tracking a process or collecting stats. They are called so because, the fact table does not have aggregatable numeric values or information. They are mere key values with reference to the dimensions from which the stats can be collected

Attribute relationships are the way of telling the analysis service engine that how the attributes are related with each other. It will help to relate two or more attributes to each other.Processing time will be decreased if proper relationships are given. This increases the Cube Processing performance and MDX query performance too.

In Microsoft SQL Server Analysis Services, attributes within a dimension are ALWAYS related either directly or indirectly to the KEY attribute. When you define a dimension based on a star schema, which is where all dimension attributes are derived from the same relational table, an attribute relationship is automatically defined between the key attribute and each non-key attribute of the dimension.

When you define a dimension based on a snowflake schema, which is where dimension attributes are derived from multiple related tables, an attribute relationship is automatically defined as follows:

• Between the key attribute and each non-key attribute bound to columns in the main dimension table.
• Between the key attribute and the attribute bound to the FOREIGN key in the secondary table that links the underlying dimension tables.
• Between the attribute bound to foreign key in the secondary table and each non-key attribute bound to columns from the secondary table.

Attribute relationships are the way of telling the analysis service engine that how the attributes are related with each other. It will help to relate two or more attributes to each other.Processing time will be decreased if proper relationships are given. This increases the Cube Processing performance and MDX query performance too.

In Microsoft SQL Server Analysis Services, attributes within a dimension are always related either directly or indirectly to the key attribute. When you define a dimension based on a star schema, which is where all dimension attributes are derived from the same relational table, an attribute relationship is automatically defined between the key attribute and each non-key attribute of the dimension.

When you define a dimension based on a snowflake schema, which is where dimension attributes are derived from multiple related tables, an attribute relationship is automatically defined as follows:

They are 2 types of attribute relationships they are

1. Rigid
2. Flexible

• Rigid: In Rigid relationships where the relationship between the attributes is fixed, attributes will not change levels or their respective attribute relationships.
• Example: The TIME DIMENSION. We KNOW that month “January 2009” will ONLY belong to YEAR “2009” and it wont be moved to any other year.
• Flexible : In Flexible relationship between the attributes is changed.
• Example: An EMPLOYEE and department. An employee can be in accounts department today but it is possible that the employee will be in Marketing department tomorrow.

They are 2 types of attribute relationships they are

They are 3 TYPES of dimensions:

1. confirm DIMENSION
2. junk dimension
3. degenerate ATTRIBUTE

They are 3 types of dimensions: