This article aims to provide a comprehensive understanding of the definitions and examples of realtime, near realtime, and batch processing.

Realtime systems are characterized by their synchronous or asynchronous nature and their close connection to the physical world. They ensure completion within a specified delay after receiving a signal and often involve transactional implications. Examples of realtime systems include stock market data feeds and real-time sensors in industrial settings.

Near realtime systems, on the other hand, operate within human perception tolerances and strive to display data reasonably quickly after a change. While they can keep up with real-world inputs, they may experience latency. Messaging systems and event-driven systems are typical examples of near realtime systems.

Lastly, batch processing involves processing large blocks of computing tasks at scheduled times, tolerating longer response times, and analyzing incoming data later. Generating reports from a large dataset or processing transactions in a banking system overnight are examples of batch processing.

The choice between these approaches depends on the specific requirements of the system, such as the need for real-time data updates or the tolerance for latency.

Realtime vs Near Realtime vs Batch

Realtime, near realtime, and batch are different processing approaches with varying time constraints and characteristics.

Realtime systems are synchronous or asynchronous web services that guarantee completion within a specified delay.

Near realtime systems aim to display data reasonably quickly after a change, albeit with occasional delays.

In contrast, batch processing occurs when a system wakes up and involves saving and analyzing incoming data later, making it more tolerant of long response times.

Realtime systems are strongly coupled to the physical world and can handle bursts of rapid input.

Near realtime systems have time constraints within human perception tolerances and may experience latency.

Batch processing, on the other hand, can be computed at any time and place, and involves grouping transactions together for processing at a later time.

Examples and Attributes

Attributes such as transaction grouping, time-driven or event-driven scheduling, and data movement protocols are crucial factors to consider when defining systems for processing tasks in a synchronized, time-sensitive manner.

Realtime systems ensure completion within a specified delay after receiving a signal and are strongly coupled to the physical world. Examples include synchronous or asynchronous web services.

Near realtime systems have time constraints within human perception tolerances and aim to display data reasonably quickly after a change. They can handle bursts of rapid input but may experience latency. Examples include JMS, messaging systems, and event-driven systems.

Batch processing involves processing large blocks of computing tasks with macroscopic deadlines. It can be computed at any time and place and is more tolerant of long response times. Examples include saving and analyzing incoming data later or grouping transactions together.

Synchronized and time-sensitive systems require careful consideration of transaction grouping, scheduling, and data movement protocols to ensure efficient processing.

Realtime systems are characterized by their strong coupling to the physical world and guarantee completion with a specified delay after receiving a signal. Examples include synchronous or asynchronous web services.

Near realtime systems, on the other hand, have time constraints within human perception tolerances and aim to display data reasonably quickly after a change. They can handle bursts of rapid input but may experience latency. Examples include messaging systems and event-driven systems.

Batch processing, in contrast, involves timed processing when the system wakes up and can be computed at any time and place. It is more tolerant of long response times and involves saving and analyzing incoming data later. Examples include grouping transactions together for analysis.