chennai

ROLAP Cube is a datastructure that is used for faster analysis of data.ROLAP is slow at analysis but large at capacity(Multibillion rows can b stored) u can use good indexes to improve its performance

ROLAP Cubes: stands for Relational OLAP. In ROLAP cubes a 
copy of data from the fact table is not made and the data 
aggregates are stored in tables in the source relational 
database. A ROLAP cube is best when there is limited space 
on the Analysis Server and query performance is not very 
important. ROLAP local cubes contain the dimensions and 
cube definitions but aggregates are calculated when they 
are needed. ROLAP cubes require less storage space than 
MOLAP and HOLAP cubes.

in the second report you should not mention columns as is 
prompted

ROLAP and MOLAP both are storage models which are governed by OLAP engines.

etl performance.

i dont know currect answer...

3.)business requirements.

Answer is c.)add time period simply and make it
not null.

The answer should be option (a). Add time period as a part 
of primary key

Delta loading is a way to extract data in a optimized way. Doing the incremental load by tracking the last update date or modified date makes the ETL processing faster.

The number of rows in a fact table depends upon the grain of the fact. Dimensions can only describe the data in the fact table. Adding more dimensions will not increase the number of rows unless it impacts the grain of the fact. e.g You have fact at the granularity of day and u want to now add time intervals dimension.... this will increase the number of rows in fact.

Yes, if we increase the dimension obviously that key need 
to added in the fact ,

Ex 

1 fact joined 1 dim of 4 key,
I have added 1 dim with 2 keys 

Now my fact will be 8 keys.

Number of rows wouldn't increase. Number of cloumns will 
increase in fact table as we will have to store the keys of 
those dimensions.

You can very well put Inner / Left Outer / Right Outer join 
in the same SQL Query.

Whether table T1 and T2 are dimension or fact table depends
on the information they store. If the all the measures are
in T1 with dimensions key in it then T1 table is fact and if
T1 is having a pk and textual information then it is
dimension and visa versa.

Idealy the number of row in fact table is very high. In this
case it looks like T2 may be the fact and T1 may be the
dimension. But cann't be said for sure as the information
given is incomplete.

A table being set as a Fact or Dimension depends on the 
kind of data it stores.Measures and keys are stored in 
Facts and Dimension stores a primary key and other 
information depending up on the dimension.

Dimensions are wide - facts are deep.
T1 will be dimension table (example "customer" with all the 
related attributes like address, tel etc)
T2 will be your fact table (example "orders") which uses 
one column as surrogate key to link to T1 ("customers" in 
this case)

A confirmed Dimension, by definition, means a single
dimension table is actually connected to more than one fact
table in your dimensional model.
So, if a dimension table is shared across two fact tables,
for example, then that dimension is called as Conformed
Dimension.

PS: It is NOT Confirmed Dimension but it is Conformed Dimension.

This dimensions are something that is built once in your model and can be reused multiple times with different fact tables.  For example consider a model containing multiple fact tables representing different data marts. Now look for a dimension that is common to these facts tables. In this example let’s consider that the product dimension is common and hence can be reused by creating short cuts and joining the different fact tables.Some of the examples are time dimension customer dimensions product dimension.