Stream processing ingests and analyzes data in real time, event by event. In contrast, batch processing collects data in groups and processes it on a schedule (e.g., hourly or daily)

• Stream processing (used by Snowplow) is ideal for real-time analytics, personalized content, and fraud detection.
• Batch processing works better for historical reporting and workloads where immediacy isn’t required.

Snowplow supports both models but excels in real-time data delivery via streaming pipelines. 

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Batch processing vs real-time streaming: when should each be used?Batch processing is suitable for large-scale data that doesn’t require immediate analysis. It works well for:

• Historical reporting.
• Analyzing large datasets.
• Situations where data freshness is not critical (e.g., monthly or weekly reports).

Real-time streaming is necessary when data must be processed and acted upon immediately. Key use cases include: 

• Real-time personalization.
• Fraud detection.
• Recommendation engines (where decisions must be made within seconds of receiving data).

Snowplow’s streaming pipeline supports such applications by providing enriched event data in real-time.