Scalability is about how resource consumption increases when more requests are to be serviced and how well the application can be scaled up to handle greater usage demands (e.g., number of input files, number of users, request rates, volume of data, etc.). It is important to answer the following questions:
What is the expected annual growth in volume (for each of the source systems) in the next five years? This information is useful in validating if the batch cycle can meet SLAs in years ahead.
What is the projected increase in number of source systems? If the number of source systems providing data to the batch cycle is likely to increase, this knowledge can be used to build the batch process to have flexibility to accommodate the same loads with minimal change.
How many jobs to be executed in parallel? There is a difference between executing jobs sequentially and doing so in parallel. The answer to this question impacts performance and could affect the degree to which the given application can scale.
Is there any need for distributed processing? If yes, details around distributed processing can also impact the expectations around scalability. If the data integration batch process is done centrally, it will have fewer complexities compared to when done in a distributed environment because synchronization between instances could potentially become critical.
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