Batch Processing

A practical example

Example: your team updates 25,000 contact records with a new segmentation field every night at 2 a.m.

A batch job processes them in chunks of 500, logging failures per chunk so you can rerun only the failed groups in the morning.

What to evaluate before investing

• Ask what batch sizes and scheduling options the platform supports, and whether schedules can be event-triggered as well as time-based.
• Check how partial failures are handled: does the job retry failed records automatically or require manual reruns?
• Confirm whether batch operations respect API rate limits or consume a separate, higher quota.

Limitations and tradeoffs

Batch processing introduces latency: data can be hours stale between runs. Use cases needing immediate reaction, such as cart abandonment follow-ups, require event-driven or streaming approaches instead, which usually cost more to operate.