Elastic Stack Tutorial for Beginners:ELK (Elasticsearch, Logstash, Kibana, Beats)

In this article we are going to cover Elastic Stack Tutorial for Beginners: A Complete Guide to ELK (Elasticsearch, Logstash, Kibana, Beats).

The Elastic Stack, also known as the ELK Stack, is a powerful set of open-source tools for real-time data ingestion, analysis, and visualization. It’s widely used for centralized logging, monitoring, and observability across applications and infrastructure.

What Is the Elastic Stack?

The Elastic Stack is a suite of tools developed by Elastic to manage, search, and visualize log data from various sources in real time. It includes:

Elasticsearch – A distributed search and analytics engine
Logstash – A data collection and processing pipeline
Kibana – A dashboard and visualization tool
Beats – Lightweight shippers that send data from edge systems

Why Use the Elastic Stack?

Real-Time Analysis: Quickly ingest and analyze large volumes of data
Scalability: Handle high-throughput systems with horizontal scaling
Flexibility: Works with structured, unstructured, and time-series data
Security & Observability: Full-stack visibility with Elastic Security and APM
Open Source: Freely available with enterprise-grade capabilities

Core Components of the Elastic Stack

Elasticsearch

Elasticsearch is the core engine that stores and indexes data.

Features:

Near real-time search and analytics
Scalable (shards/replicas)
Full-text search capabilities
RESTful API

Use Cases:

E-commerce product search
Log and metrics analytics
Business dashboards

Logstash

Logstash collects, transforms, and forwards data from diverse sources.

Key Features:

Input Plugins (files, Kafka, DBs)
Filter Plugins (e.g., grok, mutate)
Output Plugins (e.g., Elasticsearch, S3)

Kibana

Kibana lets you visualize Elasticsearch data through intuitive dashboards and charts.

Features:

Discover tab for searching raw data
Visualizations (Lens, TSVB)
Dashboards for custom reports
Developer tools for querying
Optional: Machine Learning and Maps for advanced analytics

Beats

Beats are small agents that ship data to Logstash or Elasticsearch.

Types of Beats:

Filebeat: Sends log files
Metricbeat: Collects metrics from OS/apps
Packetbeat: Analyzes network traffic
Winlogbeat: Gathers Windows event logs
Heartbeat: Monitors service uptime
Auditbeat: Captures audit and security logs
Functionbeat: Designed for cloud services (e.g., AWS Lambda)

Advantages:

Lightweight and efficient (written in Go)
Deployable on edge or remote systems

Elastic Stack Architecture

Data Sources ─▶ Beats ─▶ [Logstash] ─▶ Elasticsearch ─▶ Kibana

Elastic Stack Tutorial for Beginners:ELK (Elasticsearch, Logstash, Kibana, Beats) 1

Simple data: Beats → Elasticsearch
Complex data: Beats → Logstash → Elasticsearch

Kibana visualizes data stored in Elasticsearch, allowing users to analyze trends, discover anomalies, and build dashboards.

Elastic Stack (ELK) Architecture Explanation

Data Sources & Beats:

Beats collect and ship data from various sources:

Simple, structured data is sent directly to Elasticsearch.
Complex, unstructured, or raw data is first sent to Logstash.

Logstash:

Logstash processes, filters, and transforms raw data before sending it to Elasticsearch.

Elasticsearch:

Elasticsearch stores and indexes the processed data.

Kibana:

Kibana visualizes the stored data for analysis and insights.

Types of Beats and Their Roles:

Filebeat: Collects and forwards log files from applications, services, and system logs.
Packetbeat: Captures network traffic and provides insights into network activity.
Winlogbeat: Specifically designed to collect Windows event logs.
Metricbeat: Gathers system- and service-level performance metrics (CPU, memory, disk usage, etc.).
Heartbeat: Checks the availability and response time of services and endpoints (uptime monitoring).
Auditbeat: Monitors audit logs for security and compliance insights.
Functionbeat: Collects logs and metrics from cloud-based services like AWS Lambda.

Use Cases of the Elastic Stack

Log and Event Monitoring: Centralize application and system logs
Security Analytics: Detect intrusions, analyze behavior, SIEM
Business Intelligence: Monitor KPIs and business metrics
APM (Application Performance Monitoring): Observe app behavior
Infrastructure Monitoring: Track servers, containers, and cloud services
Search and Recommendation Engines: Power real-time product search

Key Benefits

Real-time data access and visualization
Horizontal scaling for high volumes
Full-text and structured search capabilities
Custom dashboards for deep insights
Machine learning integration for anomaly detection
Secure, open-source, and cloud-ready

Competitors in the Market

Tool	Key Focus	Limitation
Splunk	Enterprise logging & analytics	Expensive licensing
Datadog	SaaS observability & APM	Less control over deployment
Graylog	Open-source logging	Smaller community than Elastic
Sumo Logic	Cloud-native log analytics	Proprietary features
Loki (Grafana)	Log aggregation + Grafana	Limited full-text search
New Relic	APM and observability	Primarily app monitoring, less logs

Elastic Stack in the Market

Market Share: 33.65% in hosted search (2023)
Over 100M+ downloads
Top 3 SIEM tool for security analytics
Elastic Cloud SaaS growing rapidly (50% YoY)
Strong Dev & Community Support: REST APIs, Kibana UI, GitHub stars, forums

Elastic Stack Tutorial for Beginners

We have categorized below Basic Practical Elastic Stack Tutorial for Beginners which will help to those who want to start learning Elastic Stack.

Getting Started with Installation

Whether you’re using Ubuntu, CentOS, Amazon Linux, or Windows, here are detailed setup guides to get your Elastic Stack running:

Log Ingestion Examples and Use Cases

Here are practical guides for forwarding logs from various platforms and services into the Elastic Stack:

Kubernetes and Elastic Stack Integration

Use Elastic Stack to manage container logs in Kubernetes environments:

Kubernetes Logging using ELK Stack and Filebeat

Observability and Dashboards

Explore advanced observability features and dashboards in Elastic Stack:

Conclusion:

The Elastic Stack—comprising Elasticsearch, Logstash, Kibana, and Beats—offers a powerful, flexible, and scalable solution for real-time data ingestion, analysis, and visualization. Whether you’re just starting with log management or looking to build a robust observability pipeline, mastering the ELK Stack equips you with the tools needed to gain valuable insights from your data. With its wide range of use cases, from monitoring infrastructure to analyzing application performance, Elastic Stack remains a go-to choice for developers, DevOps engineers, and data analysts alike. As you continue exploring its capabilities, you’ll find endless opportunities to optimize system visibility, troubleshoot issues faster, and make informed decisions backed by data.

Reference:

Elastic Stack Official page