Hi,
I'm getting back to DataStage after a couple of years, so I apologize first in case the question below has been answered earlier. I tried searching but did not get the answer.
I have 130 Tables in my ODS schema. Data from all these tables needs to be extracted everyday by using last_update_dt column. Everyone in my team is suggesting that they would write 130 jobs to extract data from each table. But I think it can be done in one generic job since all columns need to be extracted from each table.
I had done something similar 5 years back, something with RCP column..but age is playing tricks with memory. I had something like a for loop passing all table names to this one multi instance job.
Someone please tell me it can be done in one job rather than 130 jobs.
-Regards
Wah
One generic job to extract data from all tables
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I, too, have done this in the past, and I'm sure at least the basic ideas exist on DSXchange.
On another occasion I used the content of the system tables to create SELECT and INSERT statements "on the fly" for each table.
And don't forget that Modify specifications can be completely parameterized.
On another occasion I used the content of the system tables to create SELECT and INSERT statements "on the fly" for each table.
And don't forget that Modify specifications can be completely parameterized.
Last edited by ray.wurlod on Mon Oct 20, 2014 3:53 pm, edited 1 time in total.
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Yes.
Does Create_Dt come from source or is it being generated in your job?
In the former case use a Transformer or Modify stage, in the latter case use a Transformer or Column Generator stage.
Does Create_Dt come from source or is it being generated in your job?
In the former case use a Transformer or Modify stage, in the latter case use a Transformer or Column Generator stage.
IBM Software Services Group
Any contribution to this forum is my own opinion and does not necessarily reflect any position that IBM may hold.
Any contribution to this forum is my own opinion and does not necessarily reflect any position that IBM may hold.