What Are The Two Storage Options Available In Essbase And What Are Th

1.	What Are The Two Storage Options Available In Essbase And What Are The Differences?
Answer» ASO (Aggregate Storage Option) USED for If we have large number of dimensions (generally more than 10). Most of the dimensions are sparse. We cannot write back in ASO. I heard recently that ASO will also have the write back capability. BSO (Block Storage Option) Dimensions are less and dense (recommended values for no of dim are between 4 and 8) . We can write back hence much suited for planning applications.Financial applications are generally dense structures and normal analytical cubes are sparse. Here we also NEED to understand what these dense and sparse members are. When the intersections or cells of two dimensions MOSTLY contain a value it is dense. Say for example we have two dimensions period(Month or quarter) and region and we are calculation the sales amount. Generally maximum regions(COUNTRIES, cities) will have some sales value for a particular month or quarter. Hence we can say that our period dimensions would be dense. But now instead of period, consider another dimension product. Now there are some products which are sold in only some particular regions hence for them there will be no values in other regions or will have very low percentage of data values hence the structure will become sparse. ASO (Aggregate Storage Option) Used for BSO (Block Storage Option)

What Are The Two Storage Options Available In Essbase And What Are The Differences?

Answer»

ASO (Aggregate Storage Option) USED for

If we have large number of dimensions (generally more than 10).
Most of the dimensions are sparse.
We cannot write back in ASO. I heard recently that ASO will also have the write back capability.

BSO (Block Storage Option)

Dimensions are less and dense (recommended values for no of dim are between 4 and 8) .
We can write back hence much suited for planning applications.Financial applications are generally dense structures and normal analytical cubes are sparse. Here we also NEED to understand what these dense and sparse members are. When the intersections or cells of two dimensions MOSTLY contain a value it is dense.
Say for example we have two dimensions period(Month or quarter) and region and we are calculation the sales amount. Generally maximum regions(COUNTRIES, cities) will have some sales value for a particular month or quarter. Hence we can say that our period dimensions would be dense. But now instead of period, consider another dimension product. Now there are some products which are sold in only some particular regions hence for them there will be no values in other regions or will have very low percentage of data values hence the structure will become sparse.