As the business landscape continues to evolve, 65% of companies say they will become irrelevant if they don’t embrace big data. However, before you can utilize any data for the benefit of your own organization, you must first process both the structured and unstructured data you collect.

In today's data-driven world, the ability to process vast amounts of information efficiently and effectively is paramount. Data processing plays a crucial role in extracting valuable insights, making informed decisions, and driving innovation across various industries. Understanding the different types of data processing is essential to harness the power of information. 

While the simplest and most well-known form of data processing in 2023 is still data visualization, several different data processing methods are commonly used to interact with data, each approach offering its own unique advantages.

Here are the 5 main data processing methods:

  1. Transaction Processing: Real-time handling of individual operations such as data entry and retrieval, commonly used in applications like banking and online transactions.
  2. Distributed Processing: Distribution of data processing tasks across multiple interconnected computers or servers for parallel processing, enhancing efficiency in large-scale systems and big data applications.
  3. Real-time Processing: Immediate processing of data as it is generated or received, requiring low latency and quick response times, used in applications like monitoring systems and financial trading.
  4. Batch Processing: Execution of a series of data processing tasks in a batch or group, collected over time and processed in large volumes, typically used for non-real-time tasks like data backups and report generation.
  5. Multiprocessing: Utilizing multiple processors or computing units to execute tasks concurrently, dividing tasks into smaller subtasks for simultaneous processing, used to improve performance in high-performance computing and parallel computing applications.

In this article, we dive deeply into the five fundamental types of data processing methods and how they differ in terms of availability, atomicity, concurrency, and other factors. Join us as we unravel the intricacies of transaction processing, distributed processing, real-time processing, batch processing, and multiprocessing, unlocking a world of possibilities for data utilization and transformation.

In addition to the 5 main data processing types there are three additional data processing types that may be helpful to understand. Commercial data processing, scientific data processing, and online processing. We’ll briefly touch on each of these types of data processing as well. 

Table of Contents


Why Do Different Data Processing Methods Matter?

The method of data processing used will determine the response time to a query and how reliable the output is. Thus, you need to choose your data processing technique carefully. For instance, in a situation where availability is crucial, such as a stock exchange portal, transaction processing should be the preferred method.

It is important to note the difference between data processing and a data processing system. Data processing refers to the rules by which raw data is converted into useful information. A data processing system is an application optimized for a specific type of data processing. For instance, a timesharing system is designed to run timesharing processing optimally. You can use it to run batch processing, too. However, it won’t scale very well for the job.

In that sense, when choosing the right data processing type for your needs, you must select the right system.

Whatever data processing type you choose, you should be wary of recent data governance frameworks in your industry or region that limit how you process data. Legislation like GDPR and CCPA, for example, will influence electronic data processing.

Learn about the most common types of data processing and their applications below.

Related Reading: Data Engineering: What is a Data Engineer and How Do I Become One?


Transaction Processing

Transaction processing is the type of data processing that handles ‘transactions’ — events or transactions that have to be recorded and stored. In general, it involves recording activities like sales and purchases in a database.

Transaction processing is deployed in mission-critical situations. These are situations that, if disrupted, will adversely affect business operations — for example, processing stock exchange transactions, as mentioned earlier. In transaction processing, availability is the most important factor. Availability is influenced by factors such as:

  • Hardware: A transaction processing system should have hardware redundancy, which allows for partial failures. That’s because redundant components are automated to take over and keep the computer system running.

  • Software: The software of a transaction processing system should recover quickly from a failure. Typically, transaction processing systems use transaction abstraction to achieve this. Simply put, in case of a failure, uncommitted transactions are aborted. This allows a system like a processing unit to reboot quickly.


Distributed Processing

Distributed processing is a computing process where operations are partitioned across several computers connected via a network. The goal of distributed processing is to provide faster and more reliable service than can be achieved by a single machine. 

Very often, datasets are too big to fit on one machine. Distributed data processing breaks down these large datasets and stores them across multiple machines or servers, improving data management. It rests on Hadoop Distributed File System (HDFS). A distributed data processing system has a high fault tolerance. If one server in the network fails, you can reallocate data processing tasks to other available servers, which is not a very time-consuming job.

Distributed processing can also save you on costs. Businesses like yours don’t need to build expensive mainframe computers with CPUs anymore and invest in their upkeep and maintenance.

Stream processing and batch processing are common examples of distributed processing, both of which are discussed below.


Real-Time Processing

Real-time processing is the process of computing data as soon as it is generated or received. It’s a form of distributed processing that allows you to capture and analyze incoming data streams in real-time, allowing you to act quickly on the insights given by the analysis.

Real-time processing is similar to transaction processing in that you use it in situations where you expect output in real-time. However, the two differ in how they handle data loss. Real-time processing computes incoming data as quickly as possible. If it encounters an error in incoming data, it ignores the error and moves to the next chunk of data input coming in. GPS-tracking applications are the most common example of real-time data processing.

Contrast this with transaction processing. In case of an error, such as a system failure, transaction processing aborts ongoing processing and reinitializes. You might prefer real-time processing over transaction processing in cases where approximate answers suffice.

In the world of data analytics, stream processing is a common application of real-time data processing. First popularized by Apache Storm, stream processing analyzes data as it comes in. Think data from IoT sensors or tracking consumer activity in real-time. Google BigQuery and Snowflake are examples of cloud data platforms that employ real-time processing. You can then run data through business intelligence tools that use artificial intelligence and machine learning to generate valuable insights that influence decision-making.

Related Reading: The Ultimate Guide to Building a Data Pipeline


Batch Processing

As the name suggests, batch processing is when chunks of data, stored over a period of time, are analyzed together or in batches. Batch processing is required when business owners and data scientists require a large volume of data to analyze for detailed insights. For example, sales figures will typically undergo batch processing, allowing businesses to use data visualization features like charts, graphs, and reports to derive value from data. Since a large volume of data is involved, the system will take time to process it. Processing the data in batches saves on computational resources.

You might prefer batch processing over real-time processing when accuracy is more important than speed. Additionally, you can measure the efficiency of batch processing in terms of throughput. Throughput is the amount of data processed per unit of time.



Multiprocessing is the method of data processing where two or more processors work on the same dataset. It might sound exactly like distributed processing, but there is a difference. In multiprocessing, different processors reside within the same system. Thus, they are present in the same geographical location. If there is a component failure, it can reduce the speed of the system.

Distributed processing, on the other hand, uses servers that are independent of each other and can be present in different geographical locations. Since almost all systems today come with the ability to process data in parallel, almost every data processing system uses multiprocessing.

In the context of this article, multiprocessing can be seen as having an on-premise data processing system. Typically, companies handling very sensitive information might choose on-premise data processing as opposed to distributed processing — for example, pharmaceutical companies or businesses working in the oil and gas extraction industry.

The most obvious downside of this kind of data processing is cost. Building and maintaining in-house servers is very expensive.


Three additional types of data processing 

Depending on what your data is needed for you may also find background information on these additional types of electronic data processing helpful.

Commercial Data Processing: Commercial data processing focuses on managing business-related data, such as sales and inventory. It employs databases and ERP systems to streamline operations and support decision-making. The intention of commercial data processing is to enhance business operations and drive profitability.

Scientific Data Processing: Scientific data processing is used for research and experimental data analysis in fields like biology and physics. It handles complex computations, simulations, and modeling tasks using specialized software. The objective is to advance scientific knowledge and contribute to technological innovations.

Online Processing: Online processing involves immediate data processing, essential for applications like online banking. Data is continuously updated, ensuring users access the most recent information. The goal is to provide a seamless, interactive experience with timely data access.

Preparing Your Data for Data Processing

Before you can process and analyze data, you need to prepare it. One of the best ways to achieve this goal is to use ETL tools that extract, transform, and load data to a supported target destination for processing. The best ETL tools automate data preparation, streamlining the data processing cycle.

Here’s how the ETL process works:

  • You extract data from a data source such as a relational database, transactional database, customer relationship management (CRM) system, or SaaS tool and place it inside a staging area.

  • You transform the data into a readable format for analytics and carry out tasks such as data validation and cleansing.

  • You load the data into a supported target system.

Related Reading: Top 14 ETL Tools for 2023


How Can Help With Different Types of Data Processing

If you’re looking for the right tools to easily ETL data so you can process and analyze that data, can help. With’s ETL pipelines, the task of preparing your data for future analysis is simple. The no-code data pipeline platform’s simple graphical interface does away with the need to write complex code and provides integration support out of the box with more than 100 popular data connectors. And you can use APIs for quick customizations and flexibility.

With, you can spend less time processing your data, so you have more time for analyzing it. The platform is also capable of ELT, ReverseETL, CDC, data warehouse insights, and data observability. makes the different types of data processing less of a chore by preparing data for analysis. Schedule a demo now.  

Frequently Asked Questions

Why is it important to understand the different types of data processing?

Understanding the various types of data processing is crucial because it directly influences the efficiency and effectiveness with which an organization can process vast amounts of structured and unstructured data. This capability is essential for extracting valuable insights, making informed decisions, and driving innovation across industries. With 65% of companies believing they will become irrelevant without embracing big data, choosing the appropriate data processing method can significantly impact a business's relevance and competitiveness.

How does manual data processing differ from automatic data processing?

Manual data processing involves human intervention for entering and processing data, which can be slow and prone to errors, whereas automatic data processing utilizes computer systems and software to perform tasks without human intervention, enhancing speed, efficiency, and accuracy. This article focuses on automatic data processing types, such as transaction processing, distributed processing, and real-time processing, among others, which are designed to handle large volumes of data with high speed and reliability, a necessity in modern business environments.

What are the advantages of using transaction processing for businesses, especially in critical operations like stock exchanges?

Transaction processing offers the significant advantage of handling individual operations, such as data entry and retrieval, in real-time, which is paramount in mission-critical situations like stock exchanges. Its key strength lies in its emphasis on availability, supported by hardware redundancy and quick recovery software mechanisms. This ensures that business operations are not adversely affected by system failures, making it the preferred method for applications where uninterrupted availability is crucial.

Can you explain how distributed processing enhances data management and cost-efficiency for businesses?

Distributed processing improves data management by partitioning operations across multiple interconnected computers or servers, allowing for parallel processing. This method efficiently handles large datasets that are too big for a single machine, thereby enhancing the processing speed and reliability of service. Additionally, it offers high fault tolerance and cost savings, as businesses do not need to invest in expensive mainframe computers and their maintenance, making it a viable solution for improving data management and reducing operational costs.

Why might a company prefer batch processing over real-time processing, and what is the measure of its efficiency?

A company might prefer batch processing over real-time processing when accuracy is more important than immediate results. Batch processing involves analyzing large volumes of data collected over time together, which is suitable for tasks requiring in-depth analysis, such as deriving insights from sales figures. This method is more resource-efficient for handling vast amounts of data, as it saves computational resources by processing data in large batches. The efficiency of batch processing is measured in terms of throughput, which is the amount of data processed per unit of time, making it a preferred choice for detailed analytics where speed is less critical than precision.