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SQL Server Analysis Services (SSAS) is the On-Line Analytical Processing (OLAP) COMPONENT of SQL Server. SSAS allows you to build multidimensional STRUCTURES called Cubes to pre-calculate and STORE complex aggregations, and also to build mining models to perform data analysis to identify valuable information like trends, patterns, RELATIONSHIPS ETC. within the data using Data Mining capabilities of SSAS, which otherwise could be really difficult to determine without Data Mining capabilities.

SSAS comes bundled with SQL Server and you get to choose whether or not to install this component as part of the SQL Server Installation.

SQL Server Analysis Services (SSAS) is the On-Line Analytical Processing (OLAP) Component of SQL Server. SSAS allows you to build multidimensional structures called Cubes to pre-calculate and store complex aggregations, and also to build mining models to perform data analysis to identify valuable information like trends, patterns, relationships etc. within the data using Data Mining capabilities of SSAS, which otherwise could be really difficult to determine without Data Mining capabilities.

SSAS comes bundled with SQL Server and you get to choose whether or not to install this component as part of the SQL Server Installation.

A Data Source contains the connection information used by SSAS to connect to the UNDERLYING database to load the data into SSAS during processing. A Data Source primarily contains the FOLLOWING information (apart from various other properties like Query timeout, Isolation etc.):

• Provider
• Server Name
• Database Name
• Impersonation Information

• SSAS Supports both .Net and OLE DB Providers. Following are some of the major sources SUPPORTED by SSAS: SQL Server, MS Access, Oracle, Teradata, IBM DB2, and other relational databases with the appropriate OLE DB provider.

A Data Source contains the connection information used by SSAS to connect to the underlying database to load the data into SSAS during processing. A Data Source primarily contains the following information (apart from various other properties like Query timeout, Isolation etc.):

• SSAS Supports both .Net and OLE DB Providers. Following are some of the major sources supported by SSAS: SQL Server, MS Access, Oracle, Teradata, IBM DB2, and other relational databases with the appropriate OLE DB provider.

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...

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:

• GIVING attribute RELATIONSHIPS
• Calculations
• Giving dimension usage (MANY to many relationship)
• ANALYZING the requirements

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.

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.

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.

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.

By selecting “ ATTRIBUTEHIERARCHYENABLED = FALSE”, we can MAKE an ATTRIBUTE not in process.

By selecting “ AttributeHierarchyEnabled = False”, we can make an attribute not in process.

We can hide the ATTRIBUTE by SELECTING “ATTRIBUTEHIERARCHYVISIBLE = FALSE” in properties of the attribute.

We can hide the attribute by selecting “AttributeHierarchyVisible = False” in properties of the attribute.

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.

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.

They are 3 TYPES of database SCHEMA they are

1. STAR
2. Snowflake
3. Starflake

They are 3 types of database schema they are

There are THREE standard storage modes in OLAP applications

1. MOLAP
2. ROLAP
3. HOLAP

There are three standard storage modes in OLAP applications

AttributeHierarchyVisible : Determines whether the ATTRIBUTE hierarchy is visible to client applications. The default VALUE is TRUE. However, if an attribute hierarchy will not be used for querying, you can save processing TIME by CHANGING the value of this property to False.

AttributeHierarchyVisible : Determines whether the attribute hierarchy is visible to client applications. The default value is True. However, if an attribute hierarchy will not be used for querying, you can save processing time by changing the value of this property to False.

Attribute Hierarchy Ordered: Determines whether the ASSOCIATED attribute hierarchy is ordered. The default VALUE is True. However, if an attribute hierarchy will not be USED for querying, you can save PROCESSING time by changing the value of this property to False.

Attribute Hierarchy Ordered: Determines whether the associated attribute hierarchy is ordered. The default value is True. However, if an attribute hierarchy will not be used for querying, you can save processing time by changing the value of this property to False.

Attribute Hierarchy Optimized State:

Determines the level of optimization applied to the attribute hierarchy. By default, an attribute hierarchy is FullyOptimized, which means that Analysis Services BUILDS INDEXES for the attribute hierarchy to improve query PERFORMANCE. The other option, NotOptimized, means that no indexes are built for the attribute hierarchy. USING NotOptimized is useful if the attribute hierarchy is used for purposes other than querying, because no additional indexes are built for the attribute. Other uses for an attribute hierarchy can be helping to ORDER another attribute.

Attribute Hierarchy Optimized State:

Determines the level of optimization applied to the attribute hierarchy. By default, an attribute hierarchy is FullyOptimized, which means that Analysis Services builds indexes for the attribute hierarchy to improve query performance. The other option, NotOptimized, means that no indexes are built for the attribute hierarchy. Using NotOptimized is useful if the attribute hierarchy is used for purposes other than querying, because no additional indexes are built for the attribute. Other uses for an attribute hierarchy can be helping to order another attribute.

AttributeHierarchyEnabled: Determines whether an attribute hierarchy is GENERATED by ANALYSIS SERVICES for the attribute. If the attribute hierarchy is not enabled, the attribute cannot be used in a user-defined hierarchy and the attribute hierarchy cannot be referenced in MULTIDIMENSIONAL Expressions (MDX) STATEMENTS.

AttributeHierarchyEnabled: Determines whether an attribute hierarchy is generated by Analysis Services for the attribute. If the attribute hierarchy is not enabled, the attribute cannot be used in a user-defined hierarchy and the attribute hierarchy cannot be referenced in Multidimensional Expressions (MDX) statements.

ATTRIBUTEHIERARCHYDISPLAYFOLDER: Identifies the folder in which to display the associated attribute hierarchy to end users. For example if I set the property value as “TEST” to all the ATTRIBUTES of a DIMENSION then a folder with the name “Test” will be created and all the Attributes will be PLACED into the same.

AttributeHierarchyDisplayFolder: Identifies the folder in which to display the associated attribute hierarchy to end users. For example if I set the property value as “Test” to all the Attributes of a dimension then a folder with the name “Test” will be created and all the Attributes will be placed into the same.

An attribute hierarchy is created for EVERY attribute in a dimension, and each hierarchy is available for dimensioning FACT data. This hierarchy CONSISTS of an “All” LEVEL and a detail level CONTAINING all members of the hierarchy.

you can organize attributes into user-defined hierarchies to provide navigation paths in a cube. Under certain circumstances, you may want to disable or hide some attributes and their hierarchies.

An attribute hierarchy is created for every attribute in a dimension, and each hierarchy is available for dimensioning fact data. This hierarchy consists of an “All” level and a detail level containing all members of the hierarchy.

you can organize attributes into user-defined hierarchies to provide navigation paths in a cube. Under certain circumstances, you may want to disable or hide some attributes and their hierarchies.

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

1. Natural hierarchy
2. 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.

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

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.

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.

Hybrid Online Analytical Processing (HOLAP): HOLAP is a combination of MOLAP and ROLAP. HOLAP stores the detail data in the relational database but stores the aggregations in multidimensional FORMAT. Because of this, the aggregations will need to be processed when changes are occur. With HOLAP you kind of have MEDIUM query PERFORMANCE: not as slow as ROLAP, but not as fast as MOLAP. If, however, you were only querying aggregated data or using a cached query, query performance would be similar to MOLAP. But when you need to get that detail data, performance is closer to ROLAP.

Advantages:

1. HOLAP is best USED when large amounts of aggregations are queried often with little detail data, offering high performance and lower storage requirements.
2. Cubes are smaller than MOLAP since the detail data is kept in the relational database.
3. Processing TIME is less than MOLAP since only aggregations are stored in multidimensional format.
4. Low latency since processing takes place when changes occur and detail data is kept in the relational database.

Hybrid Online Analytical Processing (HOLAP): HOLAP is a combination of MOLAP and ROLAP. HOLAP stores the detail data in the relational database but stores the aggregations in multidimensional format. Because of this, the aggregations will need to be processed when changes are occur. With HOLAP you kind of have medium query performance: not as slow as ROLAP, but not as fast as MOLAP. If, however, you were only querying aggregated data or using a cached query, query performance would be similar to MOLAP. But when you need to get that detail data, performance is closer to ROLAP.

Advantages:

ROLAP (Relational Online Analytical PROCESSING) : ROLAP does not have the high latency disadvantage of MOLAP. With ROLAP, the data and AGGREGATIONS are stored in relational FORMAT. This means that there will be zero latency between the relational source database and the cube.

Disadvantage of this mode is the performance, this type gives the poorest query performance because no objects benefit from multi dimensional storage.

Advantages:

• Since the data is kept in the relational database instead of on the OLAP server, you can view the data in almost REAL TIME.
• Also, since the data is kept in the relational database, it allows for much larger amounts of data, which can mean better scalability.
• Low latency.

ROLAP (Relational Online Analytical Processing) : ROLAP does not have the high latency disadvantage of MOLAP. With ROLAP, the data and aggregations are stored in relational format. This means that there will be zero latency between the relational source database and the cube.

Disadvantage of this mode is the performance, this type gives the poorest query performance because no objects benefit from multi dimensional storage.

Advantages:

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:

1. Since the data is stored on the OLAP server in optimized format, queries (even complex calculations) are faster than ROLAP.
2. The data is compressed so it takes up LESS space.
3. And because the data is stored on the OLAP server, you don’t need to keep the connection to the relational database.
4. Cube browsing is fastest USING MOLAP.

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:

• 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.

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

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

All the DIMENSIONS that are created using NEW DIMENSION Wizard are database dimensions. In other words, the dimensions which are at Database LEVEL are CALLED Database Dimensions.

All the dimensions that are created using NEW DIMENSION Wizard are database dimensions. In other words, the dimensions which are at Database level are called Database Dimensions.

To add a DIMENSION to a cube follow these steps.

1. In Solution Explorer, right-click the cube, and then click View DESIGNER.
2. In the Design TAB for the cube, click the Dimension USAGE tab.
3. Either click the Add Cube Dimension button, or right-click anywhere on the WORK surface and then click Add Cube Dimension.
4. In the Add Cube Dimension dialog box, use one of the following steps:
5. To add an existing dimension, select the dimension, and then click OK.
6. To create a new dimension to add to the cube, click New dimension, and then follow the steps in the Dimension Wizard.

To add a dimension to a cube follow these steps.

It is a concept of STORING HISTORICAL Changes and when EVER an IT guy finds a new way to store then a new Type will come into picture. BASICALLY there are 3 types of SCD they are given below

1. SCD type1
2. SCD type2
3. SCD type3

It is a concept of STORING Historical Changes and when ever an IT guy finds a new way to store then a new Type will come into picture. Basically there are 3 types of SCD they are given below

SLOWLY CHANGING dimensions (SCD) determine how the historical changes in the dimension tables are handled. Implementing the SCD mechanism enables users to know to which category an ITEM belonged to in any given date.

Slowly changing dimensions (SCD) determine how the historical changes in the dimension tables are handled. Implementing the SCD mechanism enables users to know to which category an item belonged to in any given date.

Role play dimensions: We already discussed about this. This is nothing but CONFIRMED Dimensions. A dimension can play different role in a fact table you can recognize a roleplay dimension when there are multiple columns in a fact table that each have foreign keys to the same dimension table.

EX1: There are three dimension keys in the factinternalsales,factresellersales tables which all refer to the dimtime table,the same time dimension is used to track sales by that contain either of these fact table,the corresponding role-playing dimension are automatically added to the CUBE.

EX2 : In retail banking, for checking account cube we could have transaction date dimension and effective date dimension. Both dimensions have date, month, quarter and year attributes. The formats of attributes are the same on both dimensions, for example the date attribute is in ‘dd-mm-yyyy’ FORMAT. Both dimensions have MEMBERS from 1993 to 2010.

Role play dimensions: We already discussed about this. This is nothing but CONFIRMED Dimensions. A dimension can play different role in a fact table you can recognize a roleplay dimension when there are multiple columns in a fact table that each have foreign keys to the same dimension table.

Ex1: There are three dimension keys in the factinternalsales,factresellersales tables which all refer to the dimtime table,the same time dimension is used to track sales by that contain either of these fact table,the corresponding role-playing dimension are automatically added to the cube.

Ex2 : In retail banking, for checking account cube we could have transaction date dimension and effective date dimension. Both dimensions have date, month, quarter and year attributes. The formats of attributes are the same on both dimensions, for example the date attribute is in ‘dd-mm-yyyy’ format. Both dimensions have members from 1993 to 2010.

• Measure groups : These measure groups can contain different dimensions and be at different granularity but so long as you MODEL your CUBE correctly, your users will be able to use measures from each of these measure groups in their queries easily and without worrying about the underlying complexity.
• Creating multiple measure groups : To create a new measure group in the Cube Editor, go to the Cube Structure TAB and right-click on the cube name in the Measures pane and select ‘New Measure Group’. You’ll then need to select the fact table to create the measure group from and then the new measure group will be created; any columns that aren’t used as foreign key columns in the DSV will automatically be created as measures, and you’ll ALSO get an extra measure of aggregation type Count. It’s a good idea to delete any measures you are not going to use at this stage.
•  Measures : Measures are the numeric values that our users want to aggregate, slice, dice and otherwise analyze, and as a result, it’s important to make sure they behave the way we want them to. One of the fundamental reasons for using Analysis Services is that, unlike a relational DATABASE it allows us to build into our cube design business rules about measures: how they should be formatted, how they should aggregate up, how they interact with specific dimensions and so on.

An attribute is a specification that defines a property of an OBJECT, ELEMENT, or FILE. It MAY ALSO refer to or set the specific value for a given instance of such.

An attribute is a specification that defines a property of an object, element, or file. It may also refer to or set the specific value for a given instance of such.

They are six RELATION between the dimension and MEASURE group, they are

1. No RELATIONSHIP
2. Regular
3. Refernce
4. Many to Many
5. DATA Mining
6. FACT

They are six relation between the dimension and measure group, they are

• No relationship: The DIMENSION and measure group are not RELATED.
• Regular: The dimension table is joined directly to the fact table.
• Referenced: The dimension table is joined to an intermediate table, which in TURN,is joined to the fact table.
• Many to many:The dimension table is to an intermediate fact table,the intermediate fact table is joined , in turn, to an intermediate dimension table to which the fact table is joined.
• Data MINING:The target dimension is based on a mining model built from the source dimension. The source dimension must also be INCLUDED in the cube.
• Fact table: The dimension table is the fact table.

Calculations are ITEM in the cube that are eveluated at runtime

Calculated members: You can create customized measures or dimension members, called calculated members, by combining cube data, arithmetic operators, numbers, and/or functions.

Example: You can create a calculated member called MARKS that converts dollars to marks by multiplying an existing DOLLAR measure by a CONVERSION rate. Marks can then be displayed to end users in a separate row or COLUMN. Calculated member definitions are stored, but their values exist only in memory. In the preceding example, values in marks are displayed to end users but are not stored as cube data.

Calculations are item in the cube that are eveluated at runtime

Calculated members: You can create customized measures or dimension members, called calculated members, by combining cube data, arithmetic operators, numbers, and/or functions.

Example: You can create a calculated member called Marks that converts dollars to marks by multiplying an existing dollar measure by a conversion rate. Marks can then be displayed to end users in a separate row or column. Calculated member definitions are stored, but their values exist only in memory. In the preceding example, values in marks are displayed to end users but are not stored as cube data.

In Analysis Services, a KPI is a COLLECTION of CALCULATIONS that are associated with a measure group in a cube that are used to evaluate business success. We USE KPI to see the business at the particular point, this is REPRESENTS with some graphical items such as traffic signals,ganze etc

In Analysis Services, a KPI is a collection of calculations that are associated with a measure group in a cube that are used to evaluate business success. We use KPI to see the business at the particular point, this is represents with some graphical items such as traffic signals,ganze etc

Actions are powerful way of extending the value of SSAS cubes for the end user. They can CLICK on a cube or portion of a cube to start an application with the selected item as a parameter, or to RETRIEVE information about the selected item.

One of the objects supported by a SQL Server Analysis Services cube is the action. An action is an event that a user can initiate when accessing cube data. The event can take a number of forms. For example, a user might be able to view a Reporting Services report, open a WEB page, or drill through to detailed information related to the cube data

Analysis Services supports three types of actions..

• Report action: Report action Returns a Reporting Services report that is associated with the cube data on which the action is based.
• Drill through: Drillthrough Returns a result set that provides detailed information related to the cube data on which the action is based.
• Standard: Standard has five action subtypes that are based on the specified cube data.

• Dataset: Returns a multidimensional dataset.
• Proprietary: Returns a string that can be interpreted by a client application.
• Rowset: Returns a tabular rowset.
• Statement: Returns a command string that can be RUN by a client application.
• URL: Returns a URL that can be opened by a client application, usually a browser.

Actions are powerful way of extending the value of SSAS cubes for the end user. They can click on a cube or portion of a cube to start an application with the selected item as a parameter, or to retrieve information about the selected item.

One of the objects supported by a SQL Server Analysis Services cube is the action. An action is an event that a user can initiate when accessing cube data. The event can take a number of forms. For example, a user might be able to view a Reporting Services report, open a Web page, or drill through to detailed information related to the cube data

Analysis Services supports three types of actions..

You can use the Partition Wizard to define partitions for a measure group in a cube. By default, a single partition is defined for each measure group in a cube. Access and processing performance, however, can degrade for large partitions. By CREATING MULTIPLE partitions, each containing a PORTION of the DATA for a measure group, you can improve the access and processing performance for that measure group.

You can use the Partition Wizard to define partitions for a measure group in a cube. By default, a single partition is defined for each measure group in a cube. Access and processing performance, however, can degrade for large partitions. By creating multiple partitions, each containing a portion of the data for a measure group, you can improve the access and processing performance for that measure group.

• In 2005 a MAX of 2000 partitions can be created per measure group and that limit is lifted in later versions.
• In any VERSION the MINIMUM is ONE Partition per measure group.

Aggregations provide performance improvements by allowing Microsoft SQL Server Analysis Services (SSAS) to retrieve pre-calculated TOTALS DIRECTLY from cube storage instead of having to recalculate data from an underlying data source for each query. To design these aggregations, you can USE the Aggregation Design Wizard.

This wizard guides you through the following steps:

1. SELECTING standard or custom settings for the storage and caching options of a partition, measure group, or cube.
2. Providing estimated or actual counts for OBJECTS referenced by the partition, measure group, or cube.
3. Specifying aggregation options and limits to optimize the storage and query performance delivered by designed aggregations.
4. Saving and optionally processing the partition, measure group, or cube to generate the defined aggregations.
5. After you use the Aggregation Design Wizard, you can use the Usage-Based Optimization Wizard to design aggregations based on the usage patterns of the business users and client applications that query the cube.

Aggregations provide performance improvements by allowing Microsoft SQL Server Analysis Services (SSAS) to retrieve pre-calculated totals directly from cube storage instead of having to recalculate data from an underlying data source for each query. To design these aggregations, you can use the Aggregation Design Wizard.

This wizard guides you through the following steps:

Perspectives are a way to reduce the complexity of cubes by hidden elements like measure groups, measures, DIMENSIONS, HIERARCHIES etc. It’s NOTHING but slicing of a cube, for ex we are having retail and hospital data and end user is subscribed to see only hospital data, then we can create PERSPECTIVE ACCORDING to it.

Perspectives are a way to reduce the complexity of cubes by hidden elements like measure groups, measures, dimensions, hierarchies etc. It’s nothing but slicing of a cube, for ex we are having retail and hospital data and end user is subscribed to see only hospital data, then we can create perspective according to it.

Bulid: VERIFIES the PROJECT files and create several local files.
Deploy: Deploy the STRUCTURE of the cube(Skeleton) to the server.
Process: Read the DATA from the source and build the dimensions and cube structures

Bulid: Verifies the project files and create several local files.
Deploy: Deploy the structure of the cube(Skeleton) to the server.
Process: Read the data from the source and build the dimensions and cube structures

The MAXIMUM SIZE of the DIMENSION is 4 GB.

The maximum size of the dimension is 4 gb.

They are 6 TYPES of processing in SSAS ,they are

1. Process Full
2. Process DATA
3. Process Index
4. Process INCREMENTAL
5. Process Structure
6. UnProcess

They are 6 types of processing in ssas ,they are

The BASIC unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that’s been aggregated to allow QUERIES to return data quickly.

For example, a cube of order data might be aggregated by time period and by TITLE, making the cube FAST when you ask questions concerning orders by week or orders by title.

The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that’s been aggregated to allow queries to return data quickly.

For example, a cube of order data might be aggregated by time period and by title, making the cube fast when you ask questions concerning orders by week or orders by title.

Just open the datasourceview and on right click we FIND the OPTION REFRESH. Click the REFRESH then it will add NEW attributes to the table which can be added to Cube.

Just open the datasourceview and on right click we find the option REFRESH. Click the REFRESH then it will add new attributes to the table which can be added to Cube.

The full FORM of AMO is ANALYSIS Management OBJECTS. This is used to CREATE or alter cubes from .NET code.

The full form of AMO is Analysis Management Objects. This is used to create or alter cubes from .NET code.

By DEFINING roles we provide SECURITY to cubes. Using roles we can restrict users from ACCESSING restricted data.

Procedure as follows: 

1. Define ROLE
2. Set Permission
3. Add APPROPRIATE Users to the role

By defining roles we provide security to cubes. Using roles we can restrict users from accessing restricted data.

Procedure as follows: 

• In 2005 its not possible to create an EMPTY cube but in 2008 we can create an empty cube.
• A new feature in Analysis SERVICES 2008 is the Attribute Relationships tab in the Dimension Designer . to implement attribute RELATIONSHIP is complex in ssas 2005
• we can create ONLY 2000 partitions per Measure Group in ssas 2005 and the same LIMIT of partitions is removed in ssas 2008.

The data warehouse is an informational environment that:

1. PROVIDES an integrated and total view of the enterprise
2. Makes the enterprise’s current and historical information easily available for decision making
3. Makes decision-support transactions possible without HINDERING OPERATIONAL systems
4. RENDERS the organization’s information consistent
5. Presents a flexible and INTERACTIVE source of strategic information

The data warehouse is an informational environment that:

Datawarehouse is complete data where as Data MART is Subset of the same.

Ex: All the ORGANISATION data may related to finance department, HR, BANKING dept are STORED in data WAREHOUSE where as in data mart only finance data or HR department data will be stored. So data warehouse is a collection of different data marts.

Datawarehouse is complete data where as Data mart is Subset of the same.

Ex: All the organisation data may related to finance department, HR, banking dept are stored in data warehouse where as in data mart only finance data or HR department data will be stored. So data warehouse is a collection of different data marts.