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How to handle different levels of granularity of the data in data model design.
For example i have two fact tables and different measures. How to take care while data model design.
please give an example.
thanks in advance.
Hi ,
The granularity of data refers to the size in which the data is subdivided.Usually we will have different level of granularity in the data for example:
For sales data we have data at each granular level:
Country -> District ->Dealer
Fine grained level of data is dealer level.It can be usually achieved through data drill-down or up option. Time dimension table is one of best example to fine grain the data at different levels eg:Year -> Quarter ->Month->Week ->Day->hour etc..,
In Qlikview we have a associative data model which will automatically allow the user to have cyclic drill option - it means user can see any granular level of data by selection.
You should concentrate on only data model design - try to avoid symmetric key and circular reference issue in data model.Rest of the stuff will be taken care of Qlikview itself for any any number of fact and dimension table.
Regards
Lathaa
Hi ,
The granularity of data refers to the size in which the data is subdivided.Usually we will have different level of granularity in the data for example:
For sales data we have data at each granular level:
Country -> District ->Dealer
Fine grained level of data is dealer level.It can be usually achieved through data drill-down or up option. Time dimension table is one of best example to fine grain the data at different levels eg:Year -> Quarter ->Month->Week ->Day->hour etc..,
In Qlikview we have a associative data model which will automatically allow the user to have cyclic drill option - it means user can see any granular level of data by selection.
You should concentrate on only data model design - try to avoid symmetric key and circular reference issue in data model.Rest of the stuff will be taken care of Qlikview itself for any any number of fact and dimension table.
Regards
Lathaa