Textual Data in Fact Table

Hi
I would like to understand what can be the probable issues which I may face in OBIEE 11g if my fact table has non- numeric data (textual data) like type or rejection reason in the fact table.
I am new to the team and the data warehouse consist of textual data in fact table. I am not very much sure whether this approach is correct(unless I am missing latest trend)
Can anybody explain me with example on what can be the issues which we can face with textual data in fact table
Regards
Sameer

You need to explain your requirement a bit more. Why you want to add type or rejection reason in fact table.
In general you should avoid adding textual data in fact.
Fact tables are only supposed to store keys and measures. It can hold textual data in case of degenerate dimension ( i.e if the number of records are same in dimension as well as fact ).
Type or Rejection reason are going to have much less records. You can create a new dimension for them having probably 20 or 30 records.
Disadvantages of adding textual data in fact is, once the fact has millions of records the fact table updates are going to take huge time and also your reports will take long time.
If you have a rejection reason dimension with some 20 records, you can use rejection reason in prompts.
But if the same rejection reason is coming from fact table your prompts performance is going to be very slow since it has to fetch 20 distinct records from millions of records.
Consider these before adding textual data in facts.
Thanks
Edited by: Maqsood Hussain on Dec 5, 2012 9:40 PM

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Edited by: Vivek Srivastava on Jan 13, 2010 9:07 AM

You need to explain your requirement a bit more. Why you want to add type or rejection reason in fact table.
In general you should avoid adding textual data in fact.
Fact tables are only supposed to store keys and measures. It can hold textual data in case of degenerate dimension ( i.e if the number of records are same in dimension as well as fact ).
Type or Rejection reason are going to have much less records. You can create a new dimension for them having probably 20 or 30 records.
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If you have a rejection reason dimension with some 20 records, you can use rejection reason in prompts.
But if the same rejection reason is coming from fact table your prompts performance is going to be very slow since it has to fetch 20 distinct records from millions of records.
Consider these before adding textual data in facts.
Thanks
Edited by: Maqsood Hussain on Dec 5, 2012 9:40 PM

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You need to explain your requirement a bit more. Why you want to add type or rejection reason in fact table.
In general you should avoid adding textual data in fact.
Fact tables are only supposed to store keys and measures. It can hold textual data in case of degenerate dimension ( i.e if the number of records are same in dimension as well as fact ).
Type or Rejection reason are going to have much less records. You can create a new dimension for them having probably 20 or 30 records.
Disadvantages of adding textual data in fact is, once the fact has millions of records the fact table updates are going to take huge time and also your reports will take long time.
If you have a rejection reason dimension with some 20 records, you can use rejection reason in prompts.
But if the same rejection reason is coming from fact table your prompts performance is going to be very slow since it has to fetch 20 distinct records from millions of records.
Consider these before adding textual data in facts.
Thanks
Edited by: Maqsood Hussain on Dec 5, 2012 9:40 PM

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Hi Ram ,
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I cant see any join conditions in your query posted. Whats the purpose of the query above. It will just bring you a cartesian product (cross join) of tables involved subjected to filters. Are you sure this is the correct query?
The error you're getting may be because you've not yet populated DimProduct or may be because of logic you used in popultaing DimProduct causing it to miss some records which is what query is referring to in above case.
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Hi A,
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Thanks for all your replies.
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Edited by: TomChan on Jun 3, 2009 9:36 AM

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Hi Experts
I am working on OBIEE 11g
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I want to show all month value from JAN to DEC [ either fact table have records exist or not]
then I use left outer join to date table .
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Hi, this looks a many to many relationship scenario, follow these steps:
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- you will have count of Extractid measure after you perform above step
- Assuming Variant is already a dimension, go into dimension usage tab, link variant dimension with cube_requirement fact using many to many relationship and selecting cube_applicability
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Populate Fact Table

Hi all,
Can you please assist, am currently working a dataware housing project. I need to produce a report but due to the fact that the data I need to use is not available in the existing data
warehouse I need to add the additional tables to do so, and also ensure that the additional table will provide thourough details for future reporting.
The scope of the additional details on the datawarehouse, is to ensure at the end of the day I have a report that is going to show details of attendance.'
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Kind Regards

You can create a SSIS package for that.
It will have a data flow task as the main step
Inside that you'll have a OLEDB source which will connect to your source server where fact data is present. You can write a query as per your logic to extract the data.
Following OLEDB source you'll have a set of Lookup tasks to lookup the Site,Member, etc details against each of dimensional tables and get the keya corresponding to that.
Then you will have a oledb destination where you will select table to which you want to dump the data ie fact table. If it doesnt exist you can create it from within the package itself and map the columns
There will also be a incremental logic implemented inside package to make sure you take only the new data each time. FOr this purpose you will have a sql task to get maximum date value captured in the fact table prior to data flow task and store it inside
a variable. Then inside your source query you will use this date as reference and take all data which have a date value > this max value in your source.
In case your table can have modifications then you also need to have a intermediate step to load to staging table inside data flow task instead of actual table and then have a further logic to delete all records from fact which matched the incoming
source rows based on your primary key. Then you'll have the final step to insert all data from stage to your actual fact table.
So your final package will have below steps
1. Execute sql task - to get max date from the destination ad store it in a variable (@[user::MaxDate])
2. data flow task
a. OLEDB Source - connect to source
uses a query like
SELECT columns...
FROM table1
WHERE AuditDateField > ?
and map parameter to @MaxDate variable created above
b. Lookup tasks to lookup to each of dimensions and get keys based on reference values
c. OLEDB destination - dump data to fact table (if no further modifications at source) or stage table (in case of modifications)
3. Execute SQL task * - Delete all records from fact matching records in stage table based on key fields and do a final insert into fact with all data from stage
3 is required only if modifications are involved
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Textual Data in Fact Table

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