Batch Processing System - Easy Guide
Batch processing is a method of running high-volume, repetitive data jobs automatically without requiring much human interaction. It is commonly used in industries such as finance, accounting, and scientific research, where large amounts of data need to be processed in a specific order. Batch processing systems are designed to execute a large number of similar jobs or tasks without user intervention. Users submit their jobs to the system, which are then added to a queue for processing. The system processes each job in turn, without any user intervention, until all jobs have been completed.
Batch processing is still critical in most organizations because many common business processes are amenable to batch processing. While online systems can also function when manual intervention is not desired, they are not typically optimized to perform high-volume, repetitive tasks. Batch processing allows companies to process jobs when computing or other resources are readily available, prioritize time-sensitive jobs, and schedule batch processes for those which are not as urgent.
Some common types of batch processing jobs include weekly/monthly billing, payroll, inventory processing, report generation, data conversion, subscription cycles, and supply chain fulfillment. Batch processing systems are used to process various types of data and requests.
The IBM mainframe computer z/OS operating system or platform has arguably the most highly refined and evolved set of batch processing facilities owing to its origins, long history, and continuing evolution. Other examples of batch processing operating system include Unisys MCP and Burroughs MCP/BCS.
How is Batch Processing Different From Real-Time Processing?
Batch processing and real-time processing are two distinct data processing approaches, each with its own strengths and applications. The main differences between batch processing and real-time processing are:
Batch Processing:
- Data is collected over a period of time and processed all at once.
- Jobs with similar requirements are batched together and run through the computer as a group.
- Processor only needs to be busy when work is assigned to it.
- Well-suited for analyzing large volumes of historical data
- Consistent processing of large amounts of data
- Latency could be minutes, hours, or days
- Most storage and processing resources requirement to process large batches of data
- Less storage required to process the current or recent set of data packets.
- Less computational requirements
- Examples of batch processing are transactions of credit cards, generation of bills, processing of input and output in the operating system, payroll, and billing systems.
Real-Time Processing:
- Data is processed as soon as it arrives.
- Processor needs to be very responsive and active all the time.
- Real-time processing requires quick transaction and characterized by supplying immediate response.
- Particularly well-suited for tasks requiring immediate data processing and subsequent response.
- Analyzing data as soon as it arrives.
- Immediate and constantly up-to-date.
- Latency needs to be in seconds or milliseconds.
- Less storage required to process the current or recent set of data packets.
- More processing resources required to "stay awake" in order to meet real-time processing guarantees.
- Examples of real-time processing are fraud detection, real-time monitoring, recommendation systems, and online personalization.
How Does Batch Processing Handle Data that Change Frequently?
Batch processing is designed to handle large volumes of data efficiently, allowing organizations to process data in parallel and take advantage of distributed computing resources. However, batch processing may not be the best approach for data that changes frequently. Here are some ways batch processing handles data that changes frequently:
Collecting and processing data at regular intervals: Batch processing collects data at regular intervals, such as daily or weekly, and processes it all at once. This means that any changes made to the data between intervals will not be reflected in the batch processing results until the next interval.
Updating data in batches: Batch processing can update data in batches, but this may not be ideal for data that changes frequently. For example, if a company updates its inventory levels every hour, batch processing may not be the best approach to update the inventory levels.
Real-time processing: Real-time processing is a better approach for data that changes frequently. Real-time processing processes data as soon as it arrives, ensuring that the data is up-to-date and accurate. Real-time processing is particularly well-suited for scenarios where data accumulates over time and can be processed in discrete chunks.