AttributeHierarchyOptimizedState: 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.
What is use of AttributeHierarchyOrdered ?
AttributeHierarchyOrdered: 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.
What is the use of AttributeHierarchyVisible ?
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.
What are types of storage modes?
There are three standard storage modes in OLAP applications
MOLAP
ROLAP
HOLAP
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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What is ROLAP and its advantage?
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.
What is HOLAP and its advantage?
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:
HOLAP is best used when large amounts of aggregations are queried often with little detail data, offering high performance and lower storage requirements.
Cubes are smaller than MOLAP since the detail data is kept in the relational database.
Processing time is less than MOLAP since only aggregations are stored in multidimensional format.
Low latency since processing takes place when changes occur and detail data is kept in the relational database.
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 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.
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.
How will you add a dimension to cube?
To add a dimension to a cube follow these steps.
In Solution Explorer, right-click the cube, and then click View Designer.
In the Design tab for the cube, click the Dimension Usage tab.
Either click the Add Cube Dimension button, or right-click anywhere on the work surface and then click Add Cube Dimension.
In the Add Cube Dimension dialog box, use one of the following steps:
To add an existing dimension, select the dimension, and then click OK.
To create a new dimension to add to the cube, click New dimension, and then follow the steps in the Dimension Wizard.
What is SCD (slowly changing dimension)?
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.
What are types of SCD?
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
SCD type1
SCD type2
SCD type3
What is role playing dimension with two examples?
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.
What is measure group, measure?
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.
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