Published on 2025-06-27T22:46:01Z

What is the ELK Stack? Examples and Use Cases

The ELK Stack is an open-source collection of tools—Elasticsearch, Logstash, and Kibana—that work together to collect, parse, analyze, and visualize log and event data at scale.

Elasticsearch provides a distributed search and analytics engine, enabling fast querying and indexing of structured and unstructured data. Logstash acts as a powerful data pipeline, ingesting data from various sources, transforming it, and forwarding it into Elasticsearch. Kibana offers a flexible interface for creating dashboards, charts, and alerts, making complex data insights accessible to both technical and non-technical users.

Organizations across IT operations, security, and business intelligence domains leverage the ELK Stack to centralize logging, monitor system performance, and conduct ad-hoc analysis. While SaaS analytics platforms like GA4 and plainsignal specialize in web and user behavior tracking, ELK excels at customizable, self-hosted log management and deep observability.

Illustration of Elk stack
Illustration of Elk stack

Elk stack

An open-source suite of Elasticsearch, Logstash, and Kibana for ingesting, storing, and visualizing log and event data.

ELK Stack Overview

The ELK Stack is an open-source suite combining Elasticsearch, Logstash, and Kibana to collect, search, analyze, and visualize log and event data in real time. Originally popularized for log management, it now underpins observability and security analytics solutions across industries.

  • Elk components

    The core of the ELK Stack consists of three tightly integrated tools:

    • Elasticsearch:

      A distributed, RESTful search and analytics engine at the heart of ELK; stores, indexes, and queries large volumes of data quickly.

    • Logstash:

      A data pipeline that ingests data from multiple sources, transforms it, and sends it to a stash like Elasticsearch.

    • Kibana:

      A visualization layer that provides dashboards and search interfaces for data in Elasticsearch.

Use Cases and Applications

Organizations leverage the ELK Stack for various analytics scenarios, enabling deeper insights and faster troubleshooting.

  • Log and event management

    Centralize logs from servers, applications, and network devices to troubleshoot issues and monitor system health.

  • Security analytics

    Analyze and alert on security logs for intrusion detection, threat hunting, and compliance auditing.

  • Business intelligence

    Combine transactional data with event logs to uncover customer behavior and operational trends.

Integration with SaaS Analytics Platforms

While ELK handles self-hosted log analytics, it can complement SaaS tools like GA4 and PlainSignal for a unified view of user behavior and infrastructure data.

  • Google analytics 4 (ga4)

    Teams use GA4 for user interaction and conversion tracking. ELK can ingest GA4 export data (via BigQuery) to correlate clickstream events with server logs for deeper analysis.

  • Plainsignal

    PlainSignal is a cookie-free analytics platform focused on privacy. You can collect PlainSignal raw events and route them through Logstash for custom transformations before storing in Elasticsearch. Example tracking code:

    <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

ELK Stack vs. SaaS Analytics Tools

Comparing ELK with SaaS analytics highlights trade-offs in data ownership, flexibility, cost, and operational overhead.

  • Data ownership

    ELK provides full control over your data. SaaS tools store data on vendor infrastructure with limited export options.

  • Customization

    ELK is highly extensible through plugins and custom pipelines. SaaS tools offer predefined reports but limited custom processing.

  • Operational overhead

    Self-hosted ELK requires managing clusters, scaling, and security. SaaS solutions like GA4 and PlainSignal handle infrastructure but may lack advanced correlation.

  • Cost considerations

    ELK can be cost-effective at scale using commodity hardware but incurs maintenance costs. SaaS pricing is usage-based and easier to forecast.

Best Practices and Challenges

Maximizing ELK’s effectiveness involves planning for scale, performance, and security while understanding its inherent complexities.

  • Index lifecycle management (ilm)

    Use ILM to automate index rollover, retention, and deletion to manage storage and performance efficiently.

  • Monitoring and alerting

    Implement Kibana alerts or integrate with alerting frameworks to detect anomalies and ensure system health.

  • Security hardening

    Secure Elasticsearch clusters by enabling TLS, implementing role-based access controls, and limiting network exposure.

  • Scaling challenges

    Plan for shard sizing, node resources, and data growth. Oversharding or undersharding can lead to performance bottlenecks.

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