Today, businesses deal with loads of data and information. As a result, this calls for efficient ways of storing, managing, and processing these data (Batilong, 2017). Most long-existent companies have been using the batch processing model, but most recently, companies have been resorted to real-time processing. Well, just like any other thing used in the present world, each of these processing methods has its merits and demerits. The choice on which one to use in an organisation depends on the type of data that needs to be processed and its volume too (Batilong, 2017). The time at which the data needs to be processed is also important when choosing a method to employ successfully (Batilong, 2017). This paper looks at the two methods of data processing and the specific cases where batch processing could be preferred to real time processing.
Batch data processing is preferred to when dealing with large volumes of data (Batilong, 2017). Transactions and operations carried out over a long period can be quite hectic to process especially if using real-time data processing. Data is first collected, then entered into the system after which it is processed and produced in the form of batch outputs.
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Also, batch data processing can also be used effectively in systems that have less user interaction (Batilong, 2017). This means that if an organisation’s system’s database is not accessed by many users, then the batch could be quite effective. This could be systems like payrolls and billing systems that are only accessed to by specified individuals.
Further, in cases where an organisation deals with large amounts of work that are being repeated, batch data processing always provides effective processor performance (Walker, 2015). This is because it can work offline thus not stressing the processor much. Again, contrary to real-time systems, a batch system can determine the time-length of a job and how long it would take to execute it. This happens because it queues jobs and always knows which one to process next.
Some companies’ systems also tend to be busy during the normal business day. This makes it hard for them to find time to process the data as perhaps many users are accessing the system at the time. Having a batch data processing system allows them to be able to process data at less busy times of the day like at night (Walker, 2015). They could set a specific time to carry out their processing which will ensure effectiveness in performance. Real-time systems process data immediately after input. This could easily stress the processors especially if the system is being accessed by many users at a time and it could jam the entire system leading to a malfunction.
Additionally, batch data processing can also be effective in organisations where different outputs are required. This is because batch processes are flexible and can be adjusted to perform other functions. This kind of flexibility is the one that makes the batch processing a favourable one to many organisations. Real-time processing is not flexible because real-time systems always have specific outputs that are entirely predictable based on the input (Shahrivari, 2014).
Batch processing is also cost effective (Walker, 2015). It saves organisations a lot of cash in many ways. For example, it offers a platform for outside computing. This can be exercised primarily in companies that require clients to pay for goods and services provided. Instead of sending a bill to them every time after purchase, the company can decide to send it on a monthly basis (Walker, 2015). This could save them a lot of money on postage charges.
In conclusion, it is very vital that every company settles for a processing method that best suits its needs and the kind of data it needs to be processed. Efficiency and effectiveness of a data processor depends on how it fits into the system’s needs. The nature of data that needs to be processed and the user interaction rate with a system are key determinants of what system to deploy in an organisation.
References
Batilong, J. (2017). Data Processing Methods. Syntatics . Retrieved on 28 January 2017 from https://www.syntacticsinc.com/news-articles-cat/batch-vs-real-time-data-processing-which-best-fits-your-business/.
Shahrivari, S. (2014). Beyond Batch Processing: Towards Real-Time and Streaming Big Data. Computers , 3, 117-129.
Walker, M. (2015). Batch vs. Real Time Data Processing. Data Science Association . Retrieved on 28 January 2017 from http://www.datascienceassn.org/content/batch-vs-real-time-data-processing.
