Streaming Data vs Batching: Performance Tradeoffs

Streaming and batching are not religious. The right answer is per-workload.

Streaming basics

Streaming: process each event as it arrives.

Batching: collect events; process in groups.

Different latency; different throughput.

Batching basics

• Latency: streaming wins (sub-second); batching loses (minutes-hours).
• Throughput: batching wins (amortized overhead); streaming loses (per-event overhead).
• Operations: batching simpler; streaming more nuanced.

Four-criteria split

1. Latency requirement.

2. Volume.

3. Order requirements.

4. Cost sensitivity.

Hybrid pattern

Many systems: streaming for hot data; batching for analytics. Lambda architecture (kind of).

Each does what it does best; complementary.

Antipatterns

• Streaming for analytics. Higher cost without latency benefit.
• Batching for user-facing. Latency unacceptable.
• Both for the same workload without distinction. Operational confusion.

What to do this week

Three moves. (1) Apply this pattern to your slowest production endpoint. (2) Measure p99 before/after. (3) Document the win and ship the runbook so the team can reproduce.