Batch vs. Stream Processing – Choosing the Right Data Handling Method
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Batch Processing deals with large volumes of data all at once, often on a scheduled basis. Key features:
Large-Scale Tasks : Great for massive datasets, like aggregating logs or running heavy analytics.
Scheduled Intervals : Typically runs hourly, nightly, or weekly—when you don’t need instant results.
High Latency :Results are only available after the entire batch finishes.
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Stream Processing handles data in real time as it arrives—no waiting for a scheduled batch. Key points:
Real-Time Data : Processes events or records within milliseconds or seconds.
Minimal Latency : Ideal for applications needing immediate responses, like alerting or dashboards.
Ongoing, Continuous : Data flows in a steady stream, and the system updates results on the fly.
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