Batch Processing in Data Engineering

Batch processing is a method that computers use to run high-volume repetitive data jobs.

• It allows users to process data when computing resources are available, and with little or no user interaction. Jobs are software programs. Batch size is the number of work units to be processed within one batch operation.
• Users collect and store data, then process the data during an event known as a batch window. Apart from submitting jobs and collecting the data, no other interaction is required to process batch as they run automatically at scheduled times and based on the availability of resources.
• Large amounts of data can be efficiently managed using the batch processing specifically those, that need frequent or repetitive tasks.

Some examples of Batch processing jobs are:

• Data conversion
• Supply chain fulfillment
• Report generation
• Billing and payroll
• Inventory processing
• Maintaining subscription cycles

Batch processing can be used in cases like financial service providing organizations, Research and scientific work and software as a service.

Advantages of Batch Processing

• Increases efficiency as it is ideal for processing large volumes of data in batches rather than doing it individually
• Can be done during less-busy designated time independently
• Is cost effective

Disadvantages of Batch Processing

• Sometimes the one time process in batch processing can be very slow.
• There is time delay between collection of data(receiving the transaction) and getting result(the output in master file) immediately after that.

In this article, we will learn about two fundamental methods that govern the flow of information and understand how data gets processed in the digital world. We start with simple definitions of batch processing and real-time processing, and gradually cover the unique characteristics and differences.