Implement centralized logging with ELK Stack, Loki, or Splunk for log collection, parsing, storage, and analysis across infrastructure.

Log Aggregation Table of Contents [Overview]( overview) [When to Use]( when to use) [Quick Start]( quick start) [Reference Guides]( reference guides) [Best Practices]( best practices) Overview Build comprehensive log aggregation systems to collect, parse, and analyze logs from multiple sources, enabling centralized monitoring, debugging, and compliance auditing. When to Use Centralized log collection Distributed system debugging Compliance and audit logging Security event monitoring Application performance analysis Error tracking and alerting Historical log retention Real time log searching Quick Start Minimal working example: Reference Guides Detailed implementations in the references/ directory: Guide Contents [ELK Stack Configuration](references/elk stack configuration.md) ELK Stack Configuration [Logstash Pipeline Configuration](references/logstash pipeline configuration.md) Logstash Pipeline Configuration [Filebeat Configuration](references/filebeat configuration.md) Filebeat Configuration [Kibana Dashboard and Alerts](references/kibana dashboard and alerts.md) Kibana Dashboard and Alerts [Loki Configuration (Kubernetes)](references/loki configuration kubernetes.md) Loki Configuration (Kubernetes) [Log Aggregation Deployment Script](references/log aggregation deployment script.md) Log Aggregation Deployment Script Best Practices ✅ DO Parse and structure log data Use appropriate log levels Add contextual information Implement log retention policies Set up log based alerting Index important fields Use consistent timestamp formats Implement access controls ❌ DON'T Store sensitive data in logs Log at DEBUG level in production Send raw unstructured logs Ignore storage costs Skip log parsing Lack monitoring of log systems Store logs forever Log PII without encryption

log-aggregation