The whole batch of data is processed at once, and then the output is also produced as a batch. So everything happens in blocks of data.īatch data processing is efficient when you need to process large volumes of data and don’t need it to be in real time. Employee payslip generation and daily reporting are some examples of batch processing.īatches can be decided based on the size of data or the period in which the data is collected. In the case of a batch based on size, you can create them based on the number of entries/records or the size of data. For example, you can create a batch when you have 1,000 records of data or when the size of the data is 1 GB. This would be helpful when your operation on processed data has a predefined size-for example, if you want to create a graph based on 1,000 entries. You will always need 1,000 entries for this operation. So you can create batches of 1,000 entries each. On the other hand, in time-based batches, each batch comprises data collected in a particular period of time. For example, if you’re analyzing data of previous working days, your batch size can be of five days. ![]() ![]() ![]() So what would happen is, data from Monday to Friday is collected, and this creates one batch. There’s no need for specialized hardware.The effect of system failure or downtime is minimal on data processing.It’s efficient in processing large volumes of data at once.This batch is processed over the weekend, and processed data is ready for your analysis on Monday.
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