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From: guser78 on 29 Mar 2010 09:12
In some of the other posts that I have posted, I have mentioned the
business usecase that I'm dealing with.
Anyway, here is a brief overview of what we are working on:

Our Dataware house contains the data retrieved from many data sources,
and all the data are somehow related to the information specific to
our Customers.

We have a marketing application running, and this requires summary
information on each of the Customer, like whether he is a ''Heavy
Buyer; Frequent buyer; Chocolate lover; Smoker'.
To calculate the above characteristics, we have to keep evaluating the
specific attributes in the Datawarehouse.
Also, due to the high performance requirements from the Marketing
application, we have planned to have a dedicated server to store the
pre-calculated characteristics information for all the subscribers.
This server should get periodically updated based on the latest
evaluation based on the data stored in the Data warehouse.

What do you suggest, for this dedicated server?
- Maintaining a 'Data mart' of all the characteristics, for all the
Subscribers
- Maintaining OLAP cubes, to represent all the characteristics, for
all the Subscribers

Which one do you suggest among the above-mentioned approaches? Do you
have any other alternative suggestions? Why?

I humbly agree that I'm not a database architect. But, I promise to go
into the details, once I get a clear picture on what way I should go
ahead with.
From: guser78 on 30 Mar 2010 04:43
On Mar 29, 6:12 pm, guser78 <qazmlp1...(a)rediffmail.com> wrote:
> In some of the other posts that I have posted, I have mentioned the
> business usecase that I'm dealing with.
> Anyway, here is a brief overview of what we are working on:
>
> Our Dataware house contains the data retrieved from many data sources,
> and all the data are somehow related to the information specific to
> our Customers.
>
> We have a marketing application running, and this requires summary
> information on each of the Customer, like whether he is a ''Heavy
> Buyer; Frequent buyer; Chocolate lover; Smoker'.
> To calculate the above characteristics, we have to keep evaluating the
> specific attributes in the Datawarehouse.
> Also, due to the high performance requirements from the Marketing
> application, we have planned to have a dedicated server to store the
> pre-calculated characteristics information for all the subscribers.
> This server should get periodically updated based on the latest
> evaluation based on the data stored in the Data warehouse.
>
> What do you suggest, for this dedicated server?
>  - Maintaining a 'Data mart' of all the characteristics, for all the
> Subscribers
>  - Maintaining OLAP cubes, to represent all the  characteristics, for
> all the Subscribers
>
> Which one do you suggest among the above-mentioned approaches? Do you
> have any other alternative suggestions? Why?
>
> I humbly agree that I'm not a database architect. But, I promise to go
> into the details, once I get a clear picture on what way I should go
> ahead with.

Some of the experts might suggest to have both Datamart and OLAP
cubes.

But, my concern is that when the ETL layer which loads the data from a
Data warehouse, can already to the pre-calculations and store the
Customer characteristics directly in the Data mart. When this is
already done, why should I require OLAP cubes additionally?

Probably, now the comparison boils down to:
- ETL layer performing pre-calculations and storing into Data mart
VS
- ETL layer just extracting selected raw attributes into Datamart and
forming OLAP cubes with the calculations

Please pour-in your suggestions. Thanks.
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