ELK Use Cases

Real-world use cases and integrations for the ELK Stack

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Available Use Cases

1. MongoDB to Elasticsearch Pipeline

Scenario: Sync MongoDB collections to Elasticsearch for full-text search and analytics. Components:
  • MongoDB (source database)
  • Logstash with JDBC input
  • Elasticsearch (search index)
  • Kibana (visualization)
Learn More: MongoDB Pipeline

2. S3 Data Lake Integration

Scenario: Analyze log files stored in AWS S3 without moving them. Components:
  • AWS S3 (storage)
  • Logstash S3 input
  • Elasticsearch (analysis engine)
Learn More: S3 Pipeline

3. Log Analytics

Scenario: Centralize and analyze application logs from multiple servers. Components:
  • Filebeat (log shipper)
  • Logstash (parser)
  • Elasticsearch (storage)
  • Kibana (dashboards)

4. Metrics Monitoring

Scenario: Monitor system and application metrics in real-time. Components:
  • Metricbeat (metrics collector)
  • Elasticsearch (time-series DB)
  • Kibana (visualization)

Common Patterns

Data Flow Pattern

Source → Logstash → Elasticsearch → Kibana

Multi-Source Aggregation

┌──────────┐
│ Database │──┐
└──────────┘  │
┌──────────┐  │    ┌──────────┐    ┌──────────┐
│   Files  │──┼───▶│ Logstash │───▶│    ES    │
└──────────┘  │    └──────────┘    └──────────┘
┌──────────┐  │
│  Kafka   │──┘
└──────────┘

Best Practices

  1. 1 Use Beats for lightweight data shipping
  2. 2 Pipeline idempotency - handle retries gracefully
  3. 3 Monitor pipeline health - check for bottlenecks
  4. 4 Version control - store configurations in Git

Next Steps

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