- What is the difference between SSAS 2005 and SSAS2008?
- 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.
You can answer more but if you end this with these then the interviewer feel that you are REAL EXPERIENCED.
- What is datawarehouse in short DWH?
- Provides an integrated and total view of the enterprise
- Makes the enterprise’s current and historical information easily available for decision making
- Makes decision-support transactions possible without hindering operational systems
- Renders the organization’s information consistent
- Presents a flexible and interactive source of strategic information
- Subject oriented
- Time variant
- Non volatile for doing decision support
Collection of data in support of management’s decision making process”. He defined the terms in the sentence as follows.
It define the specific business domain ex: banking, retail, insurance, etc…..
It should be in a position to integrated data from various source systems
Ex: sql,oracle,db2 etc……
It should be in a position to maintain the data the various time periods.
Once data is inserted it can’t be changed
- What is data mart?
Data marts are often derived from subsets of data in a data warehouse, though in the bottom-up data warehouse design methodology the data warehouse is created from the union of organizational data marts.
They are 3 types of data mart they are
- Logical data mart
- What are the difference between data mart and data warehouse?
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.
- Have you ever worked on performance tuning, if yes what are the steps involved in it?
- Avoid named queries
- Unnecessary relationships between tables
- Proper attribute relationships to be given
- Proper aggregation design
- Proper partitioning of data
- Proper dimension usage design
- Avoid unnecessary many to many relationships
- Avoid unnecessary measures
- Set AttributeHierarchyEnabled = FALSE to Attributes that is not required
- Won’t take even single measure which is not necessary.
- What are the difficulties faced in cube development?
You can tell any area where you feel difficult to work. But always the best answers will be the following.
- Giving attribute relationships
- Giving dimension usage (many to many relationship)
- Analyzing the requirements
- Explain the flow of creating a cube?
- Create a data source.
- Create a datasource view.
- Create Dimensions
- Create a cube.
- Deploy and Process the cube.
- What is a datasource or DS?
- What is datasourceview or DSV?
- Datasource view is the logical view of the data in the data source.
- Data source view is the only thing a cube can see.
- What is named calculation?
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.
- What is named query?
- Why we need named queries?
- How will you add a new column to an existing table in data source view?
- What is dimension table?
The key attribute in the dimension must contain a unique value for each member of the dimension. This key attribute is called “primary key column”
The primary key column of each dimension table corresponding to the one of the key column in any related fact table.
- What is fact table?
It consists of 2 sections
1) Foregine key to the dimesion
2) measures/facts(a numerical value that used to monitor business activity)
- What is Factless fact table?
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
- What is attribute relationships, why we need it?
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.
- How many types of attribute relationships are there?
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.
- How many types of dimensions are there and what are they?
- confirm dimension
- junk dimension
- degenerate attribute
- What are confirmed dimensions, junk dimension and degenerated dimensions?
junk dimension: A number of very small dimensions might be lumped (a small irregularly shaped) together to form a single dimension, a junk dimension – the attributes are not closely related. Grouping of Random flags and text Attributes in a dimension and moving them to a separate sub dimension is known as junk dimension.
Degenerated dimension: In this degenerate dimension contains their values in fact table and the dimension id not available in dimension table. Degenerated Dimension is a dimension key without corresponding dimension.
Example: In the PointOfSale Transaction Fact table, we have:
Date Key (FK), Product Key (FK), Store Key (FK), Promotion Key (FP), and POS Transaction Number
Date Dimension corresponds to Date Key, Production Dimension corresponds to Production Key. In a traditional parent-child database, POS Transactional Number would be the key to the transaction header record that contains all the info valid for the transaction as a whole, such as the transaction date and store identifier. But in this dimensional model, we have already extracted this info into other dimension. Therefore, POS Transaction Number looks like a dimension key in the fact table but does not have the corresponding dimension table.
- What are the types of database schema?
- What is star, snowflake and star flake schema?
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?
- How will you make an attribute not process?
- What is use of IsAggregatable property?
- What are key, name and value columns of an attribute?
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 is hierarchy, what are its types and difference between them?
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
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 is Attribute 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.
- What is use of AttributeHierarchyDisplayFolder property ?
- What is use of AttributeHierarchyEnabled?
- What is use of AttributeHierarchyOptimizedState?
- What is use of AttributeHierarchyOrdered ?
- What is the use of AttributeHierarchyVisible ?
- What are types of storage modes?
- Compare the Three Storage Modes ?
|Basic Storage Mode||Storage Location for Detail Data||Storage Location for Summary/ Aggregations||Storage space requirement||Query Response Time||Processing Time||Latency|
|MOLAP||Multidimensional Format||Multidimensional Format||MediumBecause detail data is stored in compressed format.||Fast||Fast||High|
|HOLAP||Relational Database||Multidimensional Format||Small||Medium||Fast||Medium|
|ROLAP||Relational Database||Relational Database||Large||Slow||Slow||Low|
- What is MOLAP and its advantage?
- 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 is ROLAP and its advantage?
Disadvantage of this mode is the performance, this type gives the poorest query performance because no objects benefit from multi dimensional storage.
- 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?
- 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?
- What is Database dimension?
- What is Cube 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?
- 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)?
- What are types of SCD?
- SCD type1
- SCD type2
- SCD type3
- What is Type1, Type2, Type3 of SCD?
In our example, recall we originally have the following table:
Disadvantages: All history is lost. By applying this methodology, it is not possible to trace back in history.
Usage: About 50% of the time.
When to use Type 1: Type 1 slowly changing dimension should be used when it is not necessary for the data warehouse to keep track of historical changes.
Type 2: In Type 2 Slowly Changing Dimension, a new record is added to the table to represent the new information. Therefore, both the original and the new record will be present. The new record gets its own primary key.
In our example, recall we originally have the following table:
- This will cause the size of the table to grow fast. In cases where the number of rows for the table is very high to start with, storage and performance can become a concern.
- This necessarily complicates the ETL process.
Type3 : In Type 3 Slowly Changing Dimension, there will be two columns to indicate the particular attribute of interest, one indicating the original value, and one indicating the current value. There will also be a column that indicates when the current value becomes active.
In our example, recall we originally have the following table:
Customer Key,Name,OriginalState,CurrentState,Effective Date
After Christina moved from Illinois to California, the original information gets updated, and we have the following table (assuming the effective date of change is January 15, 2003):
|Customer Key||Name||OriginalState||CurrentState||Effective Date|
- This does not increase the size of the table, since new information is updated.
- This allows us to keep some part of history.
Usage: Type 3 is rarely used in actual practice.
- What is role playing dimension with two examples?
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?
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.
- What is attribute?
- What is surrogate key?
Ex: An employee may be recruited before the year 2000 while another employee with the same name may be recruited after the year 2000. Here, the primary key will uniquely identify the record while the surrogate key will be generated by the system (say a serial number) since the SK is NOT derived from the data.
- How many types of relations are there between dimension and measure group?
- No Relationship
- Many to Many
- Data Mining
- What is regular type, no relation type, fact type, referenced type, many-to-many type with example?
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.
- What are calculated members and what is its use?
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.
- What are KPIs and what is its use?
- What are actions, how many types of actions are there, explain with example?
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 mutlidimensional 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.
- What is partition, how will you implement it?
- What is the minimum and maximum number of partitions required for a measure group?
In any version the MINIMUM is ONE Partition per measure group.
- What are Aggregations and its use?
- Selecting standard or custom settings for the storage and caching options of a partition, measure group, or cube.
- Providing estimated or actual counts for objects referenced by the partition, measure group, or cube.
- Specifying aggregation options and limits to optimize the storage and query performance delivered by designed aggregations.
- Saving and optionally processing the partition, measure group, or cube to generate the defined aggregations.
- 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.
- What is perspective, have you ever created perspective?
- What is deploy, process and build?
Deploy: Deploy the structure of the cube(Skeleton) to the server.
Process: Read the data from the source and build the dimesions and cube structures
Elaborating the same is given below.
Build: Its is a used to process the data of the cube database. Build is a version of a program. As a rule, a build is a pre-release version and as such is identified by a build number, rather than by a release number. Reiterative (repeated) builds are an important part of the development process. Throughout development, application components are collected and repeatedly compiled for testing purposes, to ensure a reliable final product. Build tools, such as make or Ant, enable developers to automate some programming tasks. As a verb, to build can mean either to write code or to put individual coded components of a program together.
Deployment: During development of an Analysis Services project in Business Intelligence Development Studio, you frequently deploy the project to a development server in order to create the Analysis Services database defined by the project. This is required to test the project.
for example, to browse cells in the cube, browse dimension members, or verify key performance indicators (KPIs) formulas.
- What is the maximum size of a dimension?
- What are the types of processing and explain each?
- Process Full
- Process Data
- Process Index
- Process Incremental
- Process Structure
Process Data: Processes data only without building aggregations or indexes. If there is data is in the partitions, it will be dropped before re-populating the partition with source data. This processing option is supported for dimensions, cubes, measure groups, and partitions.
Process Index: Creates or rebuilds indexes and aggregations for all processed partitions. This option causes an error on unprocessed objects. This processing option is supported for cubes, dimensions, measure groups, and partitions.
Process Increment: Adds newly available fact data and process only to the relevant partitions. This processing option is supported for measure groups, and partitions.
Process Structure: If the cube is unprocessed, Analysis Services will process, if it is necessary, all the cube’s dimensions. After that, Analysis Services will create only cube definitions. If this option is applied to a mining structure, it populates the mining structure with source data. The difference between this option and the Process Full option is that this option does not iterate the processing down to the mining models themselves. This processing option is supported for cubes and mining structures.
Unprocess : Drops the data in the object specified and any lower-level constituent objects. After the data is dropped, it is not reloaded. This processing option is supported for cubes, databases, dimensions, measure groups, mining models, mining structures, and partitions.
Process Default: Detects the process state of an object, and performs processing necessary to deliver unprocessed or partially processed objects to a fully processed state. This processing option is supported for cubes, databases, dimensions, measure groups, mining models, mining structures, and partitions.
- What is a cube?
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.
- What is AMO?
- After creating the cube, if we added a new column to the OLTP table then how you add this new attribute to the cube?
REAL TIME INTERVIEW QUESTIONS -
- What is the size of the Cube in your last Project?
- What is size of the database in your last Project?
You can expect this question immediately after you answer 100GB to the last question. The database size will be 600 to 800GB for which the cube will come to 100 GB. So go with 800GB for this question.
- What is size of the fact(Transaction) table in your last Project?
This will be the next question if you answer 800GB as your dataabase size. Here he is not expecting SIZE in GBs but the interviewer will be expecting NUMBER OF ROWS in the Transaction table. Go with 57Crore records for this question.
- How frequently you process the cube?
You have to be very careful here. Frequency of processing cube depends on HOW FREQUENTLY YOU ARE GETTING NEW DATA. Once the new data comes then SSIS team loads it and send a mail to SSAS team after load is completed successfully. Once SSAS team receives the mail then these guys will look for best time to PROCESS.
Typically we get data either Weekly or Monthly. So you can say that the processing of the cube will be done either Weekly or monthly.
- How frequently you get DATA from clients?
This answer should be based on your last answer. IF you answered WEEKLY to last question then the Answer to this question also should be WEEKLY. IF MONTHLY for last question then this answer also should be MONTHLY.
- What type of Processing Options you used to process the cube in your Project?
This is the toughest question to answer. This depends on DATA you have and CLIENTS requirements. Let me explain here.
- If the database is SMALL, let’s say it has only 1 crore records then people do FULL PROCESS as it wont take much time.
- If the database is MEDIUM, let’s say it has only 15 crore records then people prefer to do INCREMENTAL PROCESS unless CLIENTS ask us to do FULL PROCESS as it takes little bit of time.
- If the database is HUGE, let’s say it has more than 35 to 40 crore records then people prefer to do INCREMENTAL PROCESS unless CLIENTS ask us to do FULL PROCESS as it takes lot of time. In this case we TRY to convince clients for INCREMENTAL and if they don’t agree then we don’t have any other option.
- Incremental process will come into picture ONLY when there is no updates to the OLD data i.e no changes to already existing data else NO OTHER OPTION than FULL PROCESS.
- How you provide security to cube?
By defining roles we provide security to cubes. Using roles we can restrict users from accessing restricted data. Procedure as follows -
- Define Role
- Set Permission
- Add appropriate Users to the role
- How you move the cube from one server to another?
There are many ways to do the same. Let me explain four here and cleverly you can say “I worked on 4 SSAS projects till date and implemented different types in all the four.”
- Backup and restore – This is the simplest way. Take the Backup from development server and copy the backup to FTP folder of clients. After doing this drop a mail to Client’s Admin and he will take care of RESTORE part.
- Directly PROCESS the cube in PRODUCTION environment. For this you need access to Production which will not be given by clients unless the clients are *********. One of the client I worked for given FULL access to me ..
- Under Srart –> All Programs –> Sql Server –> Analysis Services you can see deployment wizard. This is one way of moving the cube. This method has some steps to follow. First deploy your cube and FOUR files will be created in BIN folder of PROJECT folder. Copy those FOUR files and paste in Production server in any directory. Then OPEN this DEPLOYMENT Wizard in production and when it ask for Database file then point to the location where you copied the files. After that NEXT,NEXT,NEXT … OK .. Cube will be deployed and processed.
- This way is most beautiful one. Synchronization, In this we will first deploy and process the cube in STAGING ENVIRONMENT and then we will go to production server. Connect to Analysis services in SSMS and select Synchronize by right clicking on Databases folder in SSMS of analysis services. Then select source as STAGING SERVER and then click on OK. The changes in the cube present in the Staging server will be copied to the production server.
- What is the toughest challenge you face in your Project?
There are couple of this where we face difficulty.
- While working on RELATIONSHIPS between Measure Groups and Dimensions.
- Working on Complex calculations
- Performance tuning
- How you created Partitions of the cube in your Last Project?
Partitions can be created on different data. Few people do it on PRODUCT NAME wise and many prefer to do it on DATE data wise. you go with DATE wise.
In dates, we can create MONTH wise,WEEK wise,QUARTER wise and some times YEAR wise. This all depends on how much data you are coming per WEEK or MONTH or QUARTER … If you are getting 50 lakhs records per month then tell you do MONTH wise.
- How many dimensions in your last cube?
47 to 50.
- How many measure groups in your last cube?
Total 10 and in that 4 are Fact tables and remaining 6 are Fact less fact tables.
- What is the Schema of your last cube?
- Why not STAR Schema ?
- What are the different relationships that you are used in your cube?
- Many to Many
- No Relationship
- Have you created the KPI’s , If then Explain?
Don’t add much to this as the questions in this will be tricky. Just tell that you worked on couple of KPI and you have basic knowledge on this. (Don’t worry, this is not MANDATORY)
- How you define Aggregations in your Project?
We defined the aggregations for MOST FREQUENTLY USED data in SSRS reports.
- Size of SSAS team in your last Project?
- How many Resources worked on same Cube in your Project?
Only 2 and one in morning shift and another in Evening shift.
- How much time it take to Process the Cube?
This is Very very important question. This again depends on the SIZE of database,Complexity of the database and your server settings. For database with 50 cr transaction records, it generally takes 3.5 hrs.
- How many Calculation you done in Your Project?
I answer more than 5000 and if you tell the same then you are caught unless you are super good in MDX. Best answer for you is “Worked on 50 calculations”.