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Edge Processing

Process data where it's generated instead of sending everything to the cloud.

Traditional vs Edge-First​

Traditional Approach​

Edge Device → All Data to Cloud → Process → Store
             ↑
         High cost, High latency, Privacy concerns

Problems:

  • Send ALL data to cloud (expensive)
  • Round-trip latency for results
  • Network dependency (offline = broken)
  • Sensitive data leaves premises

Edge-First Approach​

Edge Device → Process Locally → Filtered/Aggregated to Cloud
                    ↑
            Low cost, Low latency, Privacy preserved

Benefits:

  • Filter and aggregate at source (cheaper)
  • Immediate local results (faster)
  • Continue processing offline (resilient)
  • Keep sensitive data on-premises (compliant)

Key Benefits​

1. Reduced Bandwidth Costs​

Example: IoT sensors generate 1GB/day of raw data:

  • Traditional: Send 1GB/day = 30GB/month
  • Edge: Filter 95%, send 50MB/day = 1.5GB/month

Savings: 95% reduction in bandwidth costs

2. Lower Latency​

Example: Edge analytics for retail:

  • Traditional: Edge → Cloud → Process → Results = 200ms+ round-trip
  • Edge: Edge → Process → Results = under 10ms local

Improvement: 20x faster response time

3. Offline Resilience​

Example: Factory floor processing:

  • Traditional: Network down = No processing
  • Edge: Network down = Continue processing, sync when reconnected

Result: 100% uptime for critical processing

4. Privacy & Compliance​

Example: Healthcare data:

  • Traditional: PHI leaves premises (compliance risk)
  • Edge: Redact PHI at source, only send anonymized data

Result: Maintain compliance, reduce risk

Common Patterns​

Pattern 1: Edge Filtering​

Filter out noise, send only valuable data:

# Keep only ERROR-level logs
pipeline:
  processors:
    - mapping: |
        root = if this.level != "ERROR" { deleted() }

Use case: Log aggregation (reduce volume 90%+)

Pattern 2: Regional Aggregation​

Aggregate locally, send summaries:

# Aggregate metrics every hour
pipeline:
  processors:
    - window:
        period: 1h
    - mapping: |
        root.avg = this.values.sum() / this.values.length()

Use case: Multi-location analytics

Pattern 3: Local Enrichment​

Enrich with local data before sending:

pipeline:
  processors:
    - http:
        url: "http://local-service/enrich"
        verb: POST

Use case: Add local context (device info, location)

Pattern 4: PII Redaction​

Remove sensitive data at the edge:

pipeline:
  processors:
    - mapping: |
        root = this
        root.email = this.email.hash("sha256")
        root.ssn = deleted()

Use case: Compliance (GDPR, HIPAA)

When to Use Edge Processing​

Good fit:

  • High data volume from edge sources
  • Latency-sensitive applications
  • Privacy/compliance requirements
  • Unreliable network connectivity
  • High bandwidth costs

Not ideal:

  • Low data volume
  • Cloud-native sources (already in cloud)
  • Centralized processing required
  • Edge resources constrained

What's Next?​

👉 Quickstart - Deploy edge pipelines in 10 minutes

👉 Use Cases - See edge processing in action

👉 Examples - Real pipeline configurations