Roll back data load

Hi All,
I am using essbase 7.1.5. Is there any way to rollback the database in case if it finds any error. For example if source file (Flat file) contains 1000 records where at 501 record essbase found an error and has rejected that record (501) and aborted the load. In this case is there a way to rollback the database before loading the 500 records.If so where should i do those settings?? Please advice.
Thanks in advance.
Hari

A 6 Hour data load? I've never heard of such a thing. It sounds like the data should be sorted for a more optimum dataload to me.
As for the two stage approach, this assumes that you are loading variances (delta's) to the existing values, rather than replacing the data itself. It further assumes that you can create an input level cube to handle the conversion. You have your base data in one scenario, and your variances/delta's in another. You can reload your delta's (as absolutes) any time, and derive the absolutes from the sum of the base and variance values.
- Scenario
-- Base (+) <--- this get's recalculated when the Delta's are considered "good"
-- Delta (+) <--- this get's loaded for changes only, and reset to zero when the base is recalculated.
You export the modified data from this cube to your existing/consolidation cube. If you can "pre-load" the changes (outside of your calc window for the main cube), you can optimize the calculation window -- although if it takes 6 hours to load the database your calc window is probably shot no matter what you do.
However, if you mean that the load AND calc takes 6 hours, and it takes a relatively short time to load the data alone, this can be a performance enhancement because you can recalc this "input cube" in seconds from a new/complete load relative to the "in place" reset and reload changes approach (in your existing cube).
You are simply redirecting your data into a staging table, essentially, and the staging table handles the conversion of variances to absolutes so you can make the process more efficient over all (it is often more efficient to break the process up into smaller pieces).

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