Key Differences
| Factor | Real-Time | Batch |
|---|---|---|
| Latency | Seconds/Milliseconds | Hours/Days |
| Complexity | High | Low |
| Cost | 3-5x higher | Baseline |
| Infrastructure | Streaming platforms | Simple schedulers |
| Error Handling | Complex | Simple |
Use Real-Time Processing For:
- 🔴 Price monitoring: Immediate competitive response needed
- 🔴 Fraud detection: Instant alerts required
- 🔴 Stock trading: Time-sensitive decisions
- 🔴 Live dashboards: Up-to-the-second metrics
- 🔴 Alert systems: Immediate notifications
Use Batch Processing For:
- ✅ Reports: Daily/weekly analytics
- ✅ Data warehousing: Historical analysis
- ✅ ETL pipelines: Scheduled transformations
- ✅ Backups: Regular snapshots
- ✅ Cost optimization: Non-urgent processing
Cost Comparison
Processing 1M records/day:
- Batch (nightly): £500-1,500/month
- Real-time (streaming): £2,000-5,000/month
- 3-year difference: £54,000-126,000
Hybrid Approach (Best Practice)
Most businesses benefit from both:
- Real-time for critical business events
- Batch for analytics and reporting
- Lambda architecture combines both
- Start with batch, add real-time where needed
Implementation Complexity
Batch Processing
- Simple cron jobs or schedulers
- Easy to debug and retry
- 1-2 week implementation
- Lower skill requirements
Real-Time Processing
- Kafka, Kinesis, or Pub/Sub
- Complex error handling
- 4-8 week implementation
- Requires specialized skills
Our Recommendation
Start with batch processing unless you have a clear business case for real-time. Add real-time capabilities only when latency directly impacts revenue or customer experience.
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