Why is the method of data processing important?
The method of data processing you use will determine the answer time to the question and how reliable the output is. Therefore, the method needs to be chosen carefully. For example, in a situation where availability is critical, such as a stock exchange portal, transaction processing should be the preferred method.
- It is important to note the difference between data processing and data processing systems. Data processing is the process by which data is converted into useful information. A data processing system is an application that is suitable for a particular type of data processing. For example, a time-sharing system is designed to streamline time-sharing processing. It can also be used to run batch processing. However, this would not be very good for the job.
In that sense, when we talk about choosing the right type of data processing for your needs, we are referring to choosing the right system
1. Transaction processing
Transaction processing is deployed in critical situations of the mission. For example, processing a stock exchange transaction, as mentioned earlier. In transaction processing, availability is the most important factor. Availability may be affected by factors such as:
Hardware: The transaction processing system must have spare hardware. Hardware redundancy allows for partial failures, as redundant components can be made to automatically handle and run the system.
. Typically, transaction processing systems use a transaction summary to achieve this. Simply put, in case of failure, the unconditional transaction is canceled. This allows the system to restart faster.
2. Distributed processing.
Often, datasets are too large to fit a machine. Distributed data processing breaks down these large datasets and stores them on multiple machines or servers. It depends on the HDOP Distributed File System (HDFS). A distributed data processing system has a high error tolerance. If one server fails in the network, data processing tasks can be redistributed to other available servers.
Distributed processing can also save a lot of money. Businesses no longer need to build expensive mainframe computers and invest in their maintenance and upkeep.
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 real-time output is expected. However, the two differ in how they handle data loss. Real-time processing counts as fast as possible data. . GPS tracking applications are the most common example of real-time data processing.
Compare that with transaction processing. In the event of an error, such as a system failure, the transaction processing stops and resumes the ongoing processing. Real Time Processing Transaction processing is preferred in cases where approximate answers are sufficient.
. First popularized by Apache Storm, Stream Process analyzes data as it arrives. Google BigQuery and Snowflake are examples of cloud data platforms that use real-time processing.
4. Batch processing.
As the name suggests, batch processing occurs when fragments of data that have been stored over a period of time are analyzed simultaneously or in batches. Batch processing is required when analyzing large amounts of data for detailed insights. For example, a company’s sales statistics will typically undergo batch processing over a period of time. Because it contains a large amount of data, it will take time for the system to process it. By processing data in batches, it saves computational resources.
In addition, batch processing efficiency is measured in terms of throughput. .
Multiprocessing is a method of data processing where two or more processors work on the same dataset. This may sound like split processing, but there is a difference. In multi-processing, different processors reside within the same system. Thus, they are in the same geographical location. If there is a component failure,