How does “real-time data processing” differ from batch processing?

Real-time data processing is characterized by its ability to handle and process information immediately as it is inputted. This means that as soon as data is received, it is analyzed and results are generated without delay, allowing for instantaneous decision-making and responses. This process is essential in scenarios where timely information is critical, such as in stock trading, emergency response systems, or live monitoring applications.

In contrast, the batch processing method involves collecting and processing data in bulk at scheduled intervals. This approach is suitable for situations where immediate processing is not necessary, and it allows for the efficient handling of large volumes of data. However, it does not provide the immediacy that real-time processing offers.

The nature of real-time processing often requires robust computing resources to ensure that data can be processed without lag. Saying that it requires less computing power isn't accurate as real-time systems generally need more advanced architecture to manage the high volume of transactions and to maintain speed.

Furthermore, real-time processing can and often does utilize databases effectively to store, retrieve, and manage the data being processed, contradicting any notion that it cannot utilize databases. Thus, the defining characteristic of real-time data processing is its immediate response to data input.