The ELK Stack—comprising Elasticsearch, Logstash, and Kibana—is one of the most popular log management and data analytics stacks today. Developed and maintained by Elastic.co, it enables organizations to collect, process, analyze, and visualize data from multiple sources in real-time.
In recent years, the ELK Stack has evolved into the Elastic Stack, which also includes Beats—lightweight data shippers. Together, they provide a powerful end-to-end solution for centralized logging, monitoring, security analytics, and business intelligence.
Table of Contents
Elasticsearch
Elasticsearch is a distributed, RESTful search engine built on top of Apache Lucene. It stores the processed data and provides real-time search and analytics capabilities.
Purpose: Storage, Search & Analytics Engine
Features:
- Full-text search: Blazing-fast search on massive datasets
- Distributed and scalable: Supports clustering and sharding
- RESTful APIs: Easy integration with any stack
- Schema-less: Flexible document indexing with JSON
- Analytics: Powerful aggregations for metrics and insights
Example
GET /logs/_search
{
"query": {
"match": {
"status": "error"
}
}
}Logstash
Logstash is a server-side data processing pipeline that ingests data from multiple sources, transforms it, and sends it to a “stash” like Elasticsearch.
Purpose: Data Collection, Transformation, and Forwarding
Features:
- Input-Filter-Output pipeline
- Extensible via plugins (200+ available)
- Supports complex transformations
- Real-time ingestion
Basic Pipeline:
input {
file {
path => "/var/log/syslog"
}
}
filter {
grok {
match => { "message" => "%{SYSLOGPATTERN}" }
}
}
output {
elasticsearch {
hosts => ["localhost:9200"]
index => "syslog"
}
}
Kibana
Kibana is a browser-based interface for interacting with Elasticsearch. It allows users to visualize data, create dashboards, and explore logs interactively.
Purpose: Visualization and Data Exploration
Features:
- Powerful visualizations (bar charts, pie charts, maps)
- Custom dashboards for real-time monitoring
- Saved searches and alerts
- Machine Learning features (anomaly detection, forecasting)
Dashboards Example:
- CPU & Memory Usage
- Web Traffic Trends
- Application Errors by Type
- Real-time Logs by Host
Beats
Beats are single-purpose agents installed on edge hosts that send data directly to Logstash or Elasticsearch. Lightweight Data Shippers
Popular Beats:
- Filebeat – Ships log files (e.g., system, application, nginx logs)
- Metricbeat – Collects system and service metrics (CPU, memory, Docker, etc.)
- Packetbeat – Monitors network traffic and protocols (HTTP, DNS, etc.)
- Winlogbeat – Collects Windows Event Logs
- Auditbeat – Monitors Linux audit framework and file integrity
- Heartbeat – Sends uptime/ping monitoring data
- Functionbeat – Sends data from serverless environments (e.g., AWS Lambda)
Why are Beats preferred over Logstash for log collection?
Beats are lightweight, efficient, and easy to deploy on edge nodes, making them ideal for collecting and shipping logs from multiple sources. Unlike Logstash, which is heavier and designed for complex data processing, Beats focus on simple, fast log forwarding, offering better performance and scalability in distributed environments.
So it prefer to have beats on all nodes rather than Logstash
If Beats are on all nodes, how do they interact with Logstash?
In this setup, Beats collect logs locally on each node and send the data to a centralized Logstash instance. Logstash then processes, filters, and enriches the data before forwarding it to Elasticsearch. This allows for lightweight data collection at the edge (via Beats) and powerful centralized processing (via Logstash), giving you the best of both worlds.
Conclusion
The ELK Stack is a battle-tested solution used by startups and enterprises alike. Whether you’re building a monitoring system for your servers or analyzing customer behavior across your app, ELK gives you the flexibility, power, and speed to do it in real time.