Categories: Data Science

8 Types of Data Processing – A Comprehensive Guide

Introduction

Data Processing is a method of manipulation of data. It means the conversion of raw data into meaningful and machine-readable content. It basically is a process of converting raw data into meaningful information. “It can refer to the use of automated methods to process commercial data.” Typically, this uses relatively simple, repetitive activities to process large volumes of similar information. Raw data is the input that goes into some sort of processing to generate meaningful output.

Types of Data Processing

There are different types of data processing techniques, depending on what the data is needed for. In this article, we are going to discuss the five main types of data processing.

1.Commercial Data Processing

The commercial data processing means a method of applying standard relational databases, and it includes the usage of batch processing. It involves providing huge data as input into the system and creating a large volume of output but using less computational operations. It basically combines commerce and computers for making it useful for a business. The data that is processed through this system is usually standardized and therefore has a much lower chance of errors.

Many manual works are automated through the use of computers to make it easy and error-proof. Computers are used in business to take raw data and process it into a form of information that is useful to the business. Accounting programs are prototypical examples of data processing applications. An Information System (IS) is the field that studies such as organizational computer systems.

2.Scientific Data Processing 

Unlike commercial data processing, Scientific data processing involves a large use of computational operations but lower volumes of inputs as well as outputs. The computational operations include arithmetical and comparison operations. In this type of processing, any chances of errors are not acceptable as it would lead to wrongful decision making. Hence the process of validating, sorting, and standardizing the data is done very carefully, and a wide variety of scientific methods are used to ensure no wrong relationships and conclusions are reached.

This takes a longer time than in commercial data processing. The common examples of scientific data processing include processing, manage, and distribute science data products and facilitate scientific analysis of algorithms, calibration data, and data products as well as maintaining all software, calibration data, under strict configuration control. 

3. Batch Processing

Batch Processing means a type of Data Processing in which a number of cases are processed simultaneously. The data is collected and processed in batches, and it is mostly used when the data is homogenous and in large quantities. Batch Processing can be defined as concurrent, simultaneous, or sequential execution of an activity. Simultaneous Batch processing occurs when they are executed by the same resource for all the cases at the same time. Sequential Batch processing occurs when they are executed by the same resource for different cases either immediately or immediately after one another.

Concurrent Batch processing means when they are executed by the same resources but partially overlapping in time. It is used mostly in financial applications or at the places where additional levels of security are required. In this processing, the computational time is relatively less because by applying a function to the whole data altogether extracts the output. It is able to complete work with a very less amount of human intervention.

4. Online Processing 

In the parlance of today’s database systems, “online” that signifies “interactive”, within the bounds of patience.” Online processing is the opposite of “batch” processing. Online processing can be built out of a number of relatively more simple operators, much as traditional query processing engines are built. Online Processing Analytical operations typically involve major fractions of large databases. It should, therefore be surprising that today’s Online analytical systems provide interactive performance. The secret to their success is precomputation.

In most Online Analytical Processing systems, the answer to each point and click is computed long before the user even starts the application. In fact, many Online processing systems do that computation relatively inefficiently, but since the processing is done in advance, the end-user does not see the performance problem. This type of processing is used when data is to be processed continuously, and it is fed into the system automatically.

5. Real-Time Processing 

The current data management system typically limits the capacity of processing data on as and when basis because this system is always based on periodic updates of batches due to which there is a  time lag by many hours in happening of an event and recording or updating it. This caused a need for a system that would be able to record, update and process the data on as and when basis, i.e. in real-time which would help in reducing the time lag between occurrence and processing to almost nil. Huge chunks of data are being poured into systems off organizations, hence storing and processing it in a real-time environment would change the scenario.

Most organizations want to have real-time insights into the data so as to understand the environment within or outside their organization fully. This is where the need for a system arises that would be able to handle real-time data processing and analytics.  This type of processing provides results as and when it happens. The most common method is to take the data directly from its source, which may also be referred to as stream, and draw conclusions without actually transferring or downloading it. Another major technique in real-time processing is Data virtualization techniques where meaningful information is pulled for the needs of data processing while the data remains in its source form.

6. Distributed data processing

Distributed data processing (DDP) is a technique for breaking down large datasets and storing them across multiple computers or servers. In this type of processing the task is shared by several resources/machines and is executed in parallel rather than being run synchronously and arranged in a queue. Because the data is processed in a shorter period, it is more cost-effective for businesses and allows them to move more quickly. Also, the fault tolerance of a distributed data processing system is extremely high. 

7. Multi-Processing

Multiprocessing is a type of data processing in which two or more processors work on the same dataset at the same time. In this multiple processors are housed within the same system. Data is broken down into frames, and each frame is processed by two or more CPUs in a single computer system, all working parallel. 

8. Time-Sharing Processing

The central processing unit (CPU) of a large-scale digital computer interacts with multiple users with different programs almost simultaneously in this type of processing. It is possible to solve several discrete problems during the input/output process because the CPU is significantly faster than most peripheral equipment (e.g., printers and video display terminals ). The CPU addresses each user’s problem sequentially, but remote terminals have the impression that access to and retrieval from the time-sharing system is instantaneous because the solutions are immediately available as soon as the problem is fully entered.

Conclusion

This is a basic introduction to the concept of data processing and its five main types. All the types have been discussed briefly, and all these methods have their relevance in their respective fields, but it seems in today’s dynamic environment, Real-time and online processing systems are going to be the most widely used ones.

If you are interested in making a career in the Data Science domain, our 9-month-long (Live Online sessions) Postgraduate Certificate Program in Data Science and Machine Learning course can help you immensely in becoming a successful Data Science professional. 

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Ajay Sarangam

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Ajay Sarangam

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