february 13, 2026
ELK Stack
Today, company IT systems generate vast volumes of logs and events on a daily basis — ranging from user requests and application errors to infrastructure metrics and network activity. These data are critical for monitoring, troubleshooting, and ensuring the stable operation of services, but without the right tools they quickly turn into a chaotic stream of information.
Traditional approaches to log storage include files on servers or relational databases. However, these solutions scale poorly, are not suitable for full-text search, and complicate rapid incident analysis. At the same time, ready-made commercial monitoring systems can be expensive and insufficiently flexible for specific requirements. At the intersection of the need for flexibility, scalability, and fast analysis, the ELK Stack ecosystem emerged — a set of tools for the centralised collection, storage, search, and visualisation of logs and events.
The ELK Stack is a collection of open-source tools designed for the centralised ingestion, processing, storage, and visualisation of logs and events.
The name of the stack is derived from the initial letters of its core components: Elasticsearch, Logstash, and Kibana, each of which performs a specific role in the data-processing pipeline.
- Elasticsearch is a distributed search and analytics engine responsible for data storage and fast full-text search. It is this component that delivers high performance even when working with millions of events and enables complex analytical queries to be executed almost in real time.
- Logstash is a tool for data ingestion, processing, and transformation. It receives logs from various sources, parses them, normalises the data, enriches it with additional fields, and forwards it to Elasticsearch in a format suitable for analysis.
- Kibana is a web-based interface for data visualisation. It allows users to build dashboards, charts, tables, and clear monitoring panels that help quickly understand what is happening within the system.
In practice, the ELK Stack is rarely limited to just three components. To enable convenient and lightweight collection of metrics and events, specialised agents known as Beats are used. These are lightweight services installed on servers and workstations that send data directly to Elasticsearch or via Logstash. Individual Beats are responsible for different types of data: log collection, system metrics, network traffic, and operating system events. Thanks to these agents, it is possible to monitor CPU and memory utilisation, disk health, network activity, as well as Windows and other operating system events in real time — all of which are conveniently displayed on ready-made dashboards in Kibana.
As part of testing the capabilities of the ELK Stack, two of the most practical agents were used — Metricbeat and Winlogbeat.
- Metricbeat is responsible for collecting system performance metrics, including CPU load, memory usage, disk status, network activity, and other key indicators. These data make it possible to monitor host health in real time and quickly identify bottlenecks in infrastructure performance.
- Winlogbeat is designed for collecting Windows events. It reads security, system, and application logs and forwards them to Elasticsearch for further analysis. This enables centralised monitoring of errors, warnings, login attempts, and other critical events, while Kibana allows these data to be visualised through convenient dashboards.
In addition to visual dashboards, the ELK Stack allows you to configure an alerting system that automatically responds to anomalies and critical events. You can define conditions under which the system sends notifications — for example, in the event of a sudden spike in CPU load, disk saturation, a high number of errors in logs, or suspicious activity in system logs.
Such alerts make it possible not only to observe the system, but to respond to issues before they become critical for users. Notifications can be sent via email, messaging platforms, or corporate systems, making the ELK Stack a valuable tool not only for analytics but also for real-time infrastructure monitoring.
The machine learning capabilities within the Elastic ecosystem also deserve special mention. In the paid editions of the stack, a set of ML features is available that enables automatic detection of anomalies in system behaviour — without the need to define rigid thresholds in advance.
This approach allows the model to learn what normal system behaviour looks like and then highlight deviations: unusual load spikes, atypical user activity, unexpected patterns in logs, or a sharp increase in error rates. Instead of the classic rule-based approach such as “if CPU usage exceeds 90%, send an alert”, a more flexible scenario emerges in which the system detects true anomalies rather than simple breaches of static thresholds.
While these capabilities are available only under commercial licences, they clearly illustrate the direction in which the ELK Stack is evolving — from basic data collection and visualisation towards smarter, more proactive monitoring.
When you see how far the functionality of the ELK Stack has progressed, it is natural to ask how it compares with other popular solutions. For most teams, the choice is not between “observability or nothing”, but between specific systems — such as Zabbix, Grafana, and other platforms.
To better understand where the ELK Stack performs best, it makes sense to compare it not in terms of “which is better”, but in terms of the tasks these tools are typically used for. In practice, these represent different monitoring philosophies: some solutions are historically focused on infrastructure monitoring, others on metric visualisation, while others — like ELK — originated in the world of logs and events.
For this reason, it is more accurate to view them not as direct competitors, but as tools that address different problems, even though their functionality may overlap.
Zabbix is primarily a classic infrastructure monitoring tool: it is well suited for tracking the state of servers, network equipment, and services, but is less convenient for working with large volumes of logs and performing complex searches over them.
Grafana is a powerful visualisation tool most commonly used as a presentation layer for metrics from other systems. It excels at building clear and attractive charts, but on its own it is not a full-fledged storage solution for logs and events.
The ELK Stack stands out due to its versatility: it combines storage, search, and visualisation of logs and metrics within a single stack. Its main strength lies in working with events and text-based logs at scale, as well as in the ability to flexibly correlate them with metrics and alerts. At the same time, it requires more resources and initial configuration effort than tools such as Grafana or Zabbix.
Conclusion
Overall, the ELK Stack is well suited for teams that need more than just visually appealing charts and require a flexible tool for working with logs, events, and metrics in a single environment. It is particularly valuable in scenarios where rapid incident root-cause analysis is needed, different types of data must be correlated, and issues must be addressed proactively through alerts.
Yes, this versatility comes at a cost — in terms of configuration effort, resource consumption, and a higher entry threshold. However, this is precisely why many teams choose to adopt the ELK Stack: it provides greater control and transparency compared to traditional monitoring systems, which tend to focus either solely on metrics or only on infrastructure.
As a project grows and observability requirements become more complex, the ELK Stack can evolve from an interesting experiment into a solid foundation for a comprehensive monitoring and analytics system.
