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When it comes to information analytics, it is possible to use one of two approaches to deal with data. It refers to batch processing and real-time processing, and each of these phenomena implies its own peculiarities. In general, batch processing is suitable for transactions that are independent of one another, while real-time processing deals with subordinate units. However, the most significant differences also include the time lag and resource lag, which emphasizes the advantages and disadvantages of the issues under consideration.
To begin with, one should mention that the time lag is one of the essential characteristics used to distinguish between real-time and batch systems. According to Dataflair Team (2018), batch processing means that similar pieces of information are collected and analyzed at the end of a particular period. Printing utility bills is a suitable example of batch processing. At the same time, real-time processing is used to generate results either immediately or in a few seconds (Dataflair Team, 2018). For example, the reservation of tickets online shows how data is analyzed on a real-time basis. Thus, the time delay represents a significant difference between the two approaches, while its absence is an essential advantage of real-time processing.
In addition to that, the resource use is another peculiarity that makes a difference. On the one hand, Rehman (2018) stipulates that real-time processing requires more resources to cope with its task. For example, it refers to more sophisticated hardware and software that can analyze data immediately. On the other hand, batch processing does not require many resources because it does not need to follow strict time limits. Consequently, one can say that real-time processing is a more complex task for an organization, which implies many resources.
According to the information above, it is possible to state that operational efficiency can be improved with the help of batch processing. Pufahl and Weske (2019) point out that this kind of processing is “a common phenomenon in operational processes to reduce cost or time” (p. 1909). The general principles of this data processing method make it possible to achieve any positive results. It relates to the fact that batch processing is not used to deal with individual transactions. Instead, this phenomenon groups separate transactions into batches and analyze them as a whole. This fact results in fewer efforts that should be made to obtain the required results.
Even though the two phenomena under consideration are different, it is possible to combine them. Such an opportunity emerges when an organization deals with large numbers of transactions that include common data records. In this case, the large volumes of information justify the use of batch processing to save cost and efforts (Vaseekaran, 2017). However, the fact that each transaction has individual and unique records means that real-time processing is also suitable. Thus, the information above stipulates that the two models can be used together in particular situations.
In conclusion, one can say that batch processing and real-time processing are suitable variants to analyze data. Each of them implies its own pros and cons that allow organizations to choose between them in different cases. Thus, the resource use and time lag stand for the most significant differences between the phenomena under analysis. However, specific conditions can result in the fact that the two are used together to obtain the required results.
Dataflair Team. (2018). Batch processing vs real time processing – comparison. Web.
Pufahl, L., & Weske, M. (2019). Batch activity: Enhancing business process modeling and enactment with batch processing. Computing, 101(12), 1909-1933.
Rehman, J. (2018). Difference between batch processing and real time processing. Web.
Vaseekaran, G. (2017). Big Data battle: Batch processing vs stream processing. Web.