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Log Processing at Edge

Overview​

Modern distributed systems generate massive volumes of logs across edge locations, data centers, and cloud environments. Traditional centralized logging approaches struggle with high data transfer costs, slow query performance, and compliance challenges. Processing logs at the edge - where they originate - addresses these fundamental challenges while maintaining complete visibility into system health.

Expanso's Approach to Log Processing​

Expanso Edge transforms log management by processing data at the source before sending it to centralized systems. Edge agents run directly on your infrastructure, applying intelligent filtering, aggregation, and redaction in real-time.

Key capabilities:

  • Intelligent Filtering: Remove debug-level noise and duplicate events at the source, typically reducing volume by 70-85% before transmission.
  • PII Redaction: Automatically detect and remove sensitive data (credit cards, SSNs, API keys) before logs leave the edge.
  • Pre-Aggregation: Calculate metrics and statistics locally, sending summaries instead of raw events.
  • Multi-Destination Routing: Send errors to search indexes, metrics to monitoring systems, and archives to cold storage - all from a single pipeline.
  • Real-Time Alerting: Detect critical patterns instantly at the edge, triggering alerts before logs reach centralized systems.

Benefits of Edge Log Processing​

Processing logs at the edge provides significant operational and cost advantages:

Cost Reduction

  • Reduce bandwidth usage by 70-90% through intelligent filtering and compression
  • Lower cloud ingestion costs by sending only relevant events
  • Minimize storage costs by archiving pre-filtered data
  • Typical savings: 80%+ reduction in total logging costs

Performance & Compliance

  • 10x faster queries by searching smaller, filtered datasets
  • Sub-second alerting on critical events vs. minutes with centralized processing
  • Automatic compliance with data protection regulations through source-level PII redaction
  • Regional data processing meets data sovereignty requirements

Operational Excellence

  • Centralized pipeline management across thousands of edge locations
  • Consistent processing rules deployed from a single interface
  • Real-time visibility into log volumes and processing metrics
  • Simplified troubleshooting with location-aware enrichment

Common Patterns​

Severity-Based Filtering Keep only warnings, errors, and critical logs while discarding verbose debug output. This single filter typically provides 70-85% volume reduction.

Sensitive Data Protection Apply regex-based redaction to remove PII, credentials, and other sensitive information before transmission. Ensures compliance with GDPR, HIPAA, and other data protection regulations.

Metric Extraction Calculate error rates, latency percentiles, and custom business metrics at the edge. Send aggregated metrics to monitoring systems while archiving detailed events to cold storage.

Conditional Routing Route different log types to appropriate destinations: errors to Elasticsearch for analysis, metrics to Prometheus, critical alerts to Slack/PagerDuty, and all logs to S3 for archival.

Context Enrichment Add location, environment, and deployment metadata at the edge. Enables filtering and grouping in centralized systems without complex correlation queries.

Example Use Cases​

  • Retail chains processing point-of-sale logs across hundreds of stores, filtering transaction noise while capturing all errors and compliance events
  • Manufacturing facilities monitoring equipment logs, aggregating metrics locally, and alerting on anomalies before sending summaries to centralized analytics
  • Financial services redacting PII from application logs, ensuring compliance while maintaining audit trails
  • SaaS platforms filtering high-volume debug logs from edge regions, routing errors for analysis while archiving filtered events

Next Steps​