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Former Member
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What are different types of Aggregates? And What perfomance parameters should be considered while planning an infocube?

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Former Member
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For detailed information about aggregates, you can visit

Hope this will help you..

Assign points if it helps..



Former Member
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Hello SRup

For aggregate

InfoCube planning

1. What kind of cube.Baisc/Transactional

2. What will be the size..if it is going to be very big than create seprate cube year wise and than use multiprovider...this will improve loading and reporting performence.

3. What are the dimensions...use max dimension

4. Put similar char in one dimension.....put line item in single dimension...

5. Do not combine dynamic characteristics in the same dimension in order to keep dimensions rather small: for example, don’t combine customer and material in one dimension if the two characteristics are completely independent

6. Use high cardinality for dimension if u fill that dimension size can be large...

7. Make sure dimension size should not be more than 20% of fact data...if it is than either use line item dimension or move some characteristic to other dimension

8 Dimensional attributes generally have a performance advantage over navigational attributes for query operations. Fewer tables are joined to access the values of dimensional attributes than navigational attributes; therefore less overhead is seen during query execution. The requirements should be carefully analyzed to determine whether a certain attribute needs to be navigational or if dimensional or display attribute will suffice.

9. Time-dependent navigational attributes have impact on the use of aggregates. Either no aggregates can be used or aggregates have to be realigned regularly when the key date changes, which is an expensive process. If time-dependent master data is really necessary, consider modeling the attribute twice: as time-dependent and time-independent. Prefer the time-independent in your queries if possible.

10. InfoCubes containing non-cumulative key figures should not be too granular. A high granularity will result in a huge amount of reference points which will impact aggregate build significantly.If you use non-cumulative key figures, use as few validity objects as possible. Do not misuse validity objects for termination.



Tripple k

Former Member
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