Before you can make use of any structured and unstructured you collect, that data must be processed.

The simplest example of data processing is data visualization. For example, most CRMs can spit out data analysis reports in the form of graphs. To get to that point, however, data undergoes a series of conversion operations.

There are several different types of data processing, which differ in terms of availability, atomicity, and concurrency, among other factors.

Table of Contents

1. Why Does the Data Processing Method Matter?

2. Transaction processing

5. Distributed processing

3. Real-time processing

4. Batch processing

6. Multiprocessing

7. Preparing Your Data for Processing

Why Does the Data Processing Method Matter?

The method of data processing you employ will determine the response time to a query and how reliable the output is. Thus, the method needs to be chosen 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 is the rules by which data is converted into useful information. A data processing system is an application that is optimized for a certain type of data processing. For instance, a timesharing system is designed to run timesharing processing optimally. It can be used to run batch processing, too. However, it won't scale very well for the job.

In that sense, when we talk about choosing the right data processing type for your needs, we are referring to choosing the right system. The following are the most common types of data processing and their applications.

1. Transaction Processing

Transaction processing is deployed in mission-critical situations. These are situations, which, 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 can be influenced by factors such as:

  • Hardware: A transaction processing system should have redundant hardware. Hardware redundancy allows for partial failures, since redundant components can be automated to take over and keep the system running.
  • Software: The software of a transaction processing system should be designed to 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 the system to reboot quickly.

2. Distributed Processing

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. 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, the data processing tasks can be reallocated to other available servers.

Distributed processing can also be immensely cost-saving. Businesses don't need to build expensive mainframe computers 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.

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3. Real-time Processing

Real-time processing is similar to transaction processing, in that it is used in situations where output is expected in real-time. However, the two differ in terms of 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 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. Real-time processing is preferred 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.

4. 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 a large volume of data needs to be analyzed for detailed insights. For example, sales figures of a company over a period of time will typically undergo batch processing. Since there is a large volume of data involved, the system will take time to process it. By processing the data in batches, it saves on computational resources.

Batch processing is preferred over real-time processing when accuracy is more important than speed. Additionally, the efficiency of batch processing is also measured in terms of throughput. Throughput is the amount of data processed per unit time.

5. Multiprocessing

Multiprocessing is the method of data processing where two or more than two 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.

However, in the context of this article, multiprocessing can be seen as having an on-premise data processing system. Typically, companies that handle 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.

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Preparing your Data for Data Processing

Before data can be processed and analyzed, it needs to be prepared, so it can be read by algorithms. Raw data needs to undergo ETL - extract, transform, load - to get to your data warehouse for processing. Xplenty simplifies the task of preparing your data for analysis. With our cloud platform, you can build ETL data pipelines within minutes. The simple graphical interface does away with the need to write complex code. There is integration support right out of the box for more than 100 popular data warehouses and SaaS applications. And you can use APIs for quick customizations and flexibility.

With Xplenty, you can spend less time processing your data, so you have more time for analyzing it. Learn more by scheduling a demo and experiencing our low-code platform for yourself.