Batch processing mode processes data how?

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Multiple Choice

Batch processing mode processes data how?

Explanation:
Batch processing handles data in groups at defined intervals rather than processing each item as it arrives. Data is collected over a time window or until a batch size is reached, and then a processing job runs on that entire set. This means results arrive after the batch window closes, introducing latency that depends on how long the window is. This approach is common for workloads like nightly reports or large ETL pipelines where throughput and resource planning matter. That’s why the idea that it processes data continuously with low latency isn’t correct for batch processing—continuous, low-latency handling is a hallmark of stream processing. Real-time streaming and the claim that batch is inherently faster than stream also don’t fit the typical characteristics, since batch prioritizes grouped processing over immediate, per-event handling.

Batch processing handles data in groups at defined intervals rather than processing each item as it arrives. Data is collected over a time window or until a batch size is reached, and then a processing job runs on that entire set. This means results arrive after the batch window closes, introducing latency that depends on how long the window is. This approach is common for workloads like nightly reports or large ETL pipelines where throughput and resource planning matter.

That’s why the idea that it processes data continuously with low latency isn’t correct for batch processing—continuous, low-latency handling is a hallmark of stream processing. Real-time streaming and the claim that batch is inherently faster than stream also don’t fit the typical characteristics, since batch prioritizes grouped processing over immediate, per-event handling.

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