ELK Stack Integration Guide

The ELK stack is a developer-facing observability and search platform built from three tools:

Elasticsearch stores, indexes, searches, and aggregates JSON documents.
Logstash ingests events, transforms them, and routes them to Elasticsearch or other outputs.
Kibana explores indexed data through dashboards, saved searches, and operational views.

Use this stack when an application needs centralized logs, event analytics, audit search, troubleshooting dashboards, or operational monitoring.

Integration Flow

Emit structured events from the application. Prefer JSON logs with stable field names such as timestamp, level, service.name, trace.id, message, and domain-specific identifiers.
Normalize and enrich events in Logstash. Parse timestamps, convert numeric fields, redact secrets, and add environment metadata before indexing.
Design Elasticsearch indices around query patterns. Use predictable index names such as logs-web-api-dev-YYYY.MM.dd and define mappings for dates, keywords, numbers, and text fields.
Build Kibana views for developers and operators. Create data views, dashboards, and alerts around latency, error rate, throughput, and business events.

Run the stack locally with Docker Compose or a managed development environment.
Keep credentials and cloud endpoints in .env files or secret stores, never in committed docs or code.
Validate that one application request produces a searchable event in Elasticsearch.
Confirm Kibana dashboards use the same fields documented in application logging contracts.