In the Conventional Loading in TM1 we have the Source like an external data warehouse or TM1 cube. We have a Target Cube and in order to load data from source to the target we build a Turbo Integrated Process. As soon as we are in this process data will flow from source to target. But, it will process only one record at the time. With relative small number of records in the speed of TM1 this is in most cases more than sufficient. But what if you need to load huge number of records?
3. Conventional Loading in TM1
•1 record at the time
•No problem with small number of records
•But what if you need to load hughe numbers of
records
Turbo Integrated Process
Data Source
Target Cube
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10. Parallel Loading Results
•Multiple records at the same time
•Extreme decrease of loading time
•Higher frequency of loads
•Supports business needs
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Welcome to this small video on the concept of parallel loading in TM1
At one of our customers we had a challenge: Loading lot of data within a reasonable time frame. Spare a little of your time and we show you how we solved this challenge
In the conventional loading in tm1 we have an external source like a data warehouse or a tm1 cube
We have a target cube. In order to load data from the source to the target we build a turbo integrated process. As soon as we run this process data will flow from source to target.
BUT it will process only 1 record at the time. With relative small number of records and the speed of TM1 this is most case sufficient.
But what if you need to load hughe number of records?
You wait !
So in order to solve our problem we looked at out server in more detail
We saw 8 processors. During the process of loading data we saw processor1 Working but the others were sleeping. How could we make the others work too?
We looked at the measure dimension and decided that we had to devide the elements over 8 buckets or subsets. So we wrote a nice Turbo integrated process to do this for us
In the parallel loading we see our Source and target again. But now we have create 8 Turbo integrated process which will process 8 records at the time.
During the loading process we timed for each element in the measure dimension how long it took to be processed. Then we build one other process that devides the element again over the buckets so each bucket has more or less the same workload. This is used the parallel loading again.
So what were the results
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