Impact of hourly Data load on BW and R/3

Hi...
As of now, the delta load from R/3 to BW is taking place every night. But, the users claim that they need it hourly.
Is this practice (hourly data load from r/3 to bw) is usually done? What would be the impact on BW and R/3? (any performance issues!)
What is the minimum best time interval for the delta loads?
Thanks,
Sai.

I think you have to let user business requirements dictate the answers as long as there are not any system or application constraints.
We have a load that at run 4 times a day during a 9 hour window, once at the beginning to pickup any overnight work and then about every 3 hrs during the business day to pick up new information that is needed.
No discernible impact on R3 here, but that probably depends on your transaction volume and efficiency of the extractors that are used.
The user base does need to understand that loads will occur during the day and that depending on when the run a query, the results could change - but presumably that's the whole reason you are doing this in the first place.
This does result in more Requests, so you want to make sure you are compressing your cubes based on the Request's age rather number of requests.
Can't really think of much else.

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Guilherme
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I try to start this discussion again, because we still have the same problem, and no idea of why (no idea from the Oracle support too) and my client is waiting for an explanation :-/
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Due to huge no of data, internal table memory space is used and data loading get fails.
Then we changed the data package size to 10,000, which executes the data load successfully.
DTP with package size of 50,000 took 40 minutes to execute and failed, but DTP with package size of 10,000 took 15 minutes (for same amount of data).
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Why a DTP with bigger size of packet runs longer than a DTP with lower packet size ?
Also by reducing the standard data package size 50,000 to 10,000, will it impact any other data loading?
Thanks

Hi Sri,
If your DTP is taking more time then check your transformation .
1.Transformation with Routines always take more time so you if you want to reduce the time of execution then routine should be optimized for good performance .
2.Also check if you have filter at DTP level .Due to filters DTP takes long time .If same data get filtered at routine level it take much lesser time .
3.If you cannot change routine then you can set semantic keys at your DTP .The package data will be sorted as per semantic keys and thus it may be helpful at routine level for fast processing.
4.Your routine is getting failed due to internal table memory space so check if you have select statement in routine without FOR ALL ENTRIES IN RESULT_PACKAGE or SOURCE_PACKAGE line .if you will use this It will reduce record count .
5.Wherever possible delete duplicate records and if possible filter useless data at start routine itself .
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7.The maximum memory that can be occupied by an internal table (including its internal administration) is 2 gigabytes. A more realistic figure is up to 500 megabytes.
8.Also check no of jobs running that time .May be you have lots of jobs active at the same time so memory availability will be less and DTP may get failed .
Why a DTP with bigger size of packet runs longer than a DTP with lower packet size ?
*Start and end routine works at package level so routine run for each package one by one .By default package have sorted data based on keys (non unique keys (characteristics )of source or target) and by setting semantic keys you can change this order.So Package having more data will take more time in processing then package have lesser data .
by reducing the standard data package size 50,000 to 10,000, will it impact any other data loading?
It will only impact running of that load .but yes if lots of other loads are running simultaneously then server can allocate more space to them .So better before reducing package size just check whether it is helpful in routine performance (start and end ) or increasing overhead .
Hope these points will be helpful .
Regards,
Jaya Tiwari

Impact of hourly Data load on BW and R/3

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