Modern IT teams face a growing volume of data generated daily by applications, systems, and infrastructure. Efficient processing, analysis, and visualization of this information are essential, which is why more and more organizations are turning to open-source solutions — with OpenSearch being one of the most popular tools today.

OpenSearch is an open-source platform developed, among others, by Amazon Web Services, enabling real-time processing of large datasets. Originating directly from the well-known Elasticsearch project, OpenSearch offers advanced analytics capabilities, flexible scalability, and full control over data — without the need to rely on closed, commercial solutions.

Importantly, OpenSearch is not just a search and indexing engine but a comprehensive ecosystem platform that integrates with a wide range of tools. Thanks to this flexibility, it is possible to build a complete, high-performance analytics system tailored to the specific needs of any organization.

OpenSearch in practice — how to build a modern analytics system

Success depends not only on the OpenSearch engine itself but also on how data is collected, processed, and visualized. In production environments, tools like Logstash or Fluentd have proven highly effective, allowing flexible data collection from various sources — application servers, operating systems, containers, or network devices. This data can be enriched, filtered, or transformed already at the ingestion stage, significantly reducing the load on the OpenSearch database and improving overall system performance.

Lightweight agents such as Filebeat or Metricbeat are also very popular within the OpenSearch ecosystem. These tools efficiently forward logs and metrics directly to the analytics system. In containerized environments, Fluent Bit is increasingly used, making it an excellent solution for monitoring distributed applications.

A critical element of any analytics system is the data visualization layer. OpenSearch Dashboards, developed in parallel with the OpenSearch engine, enable the creation of intuitive dashboards, interactive charts, and reports that help DevOps teams and data analysts quickly diagnose issues or track trends within the IT environment. Notably, the platform closely resembles Kibana, familiar from earlier Elasticsearch deployments, which simplifies migration or integration with existing systems.

Organizations with advanced monitoring environments based on Prometheus can also integrate these tools — for example, by exporting metrics from Prometheus to OpenSearch for centralized analysis.

Performance and optimization — what to keep in mind

Working with large datasets requires a well-thought-out system architecture. In the case of OpenSearch, it's essential to properly assign roles within the cluster — nodes serving as master, data, or coordinating nodes should be selected based on resources like RAM and high-speed SSD storage. Only with this approach can you maintain high performance, even when handling millions of documents daily.

Another key aspect is data lifecycle management. OpenSearch enables automatic creation of new indexes, archiving of old data, and deletion of unnecessary information according to retention policies. This not only improves performance but also helps control infrastructure costs.

In practice, applying precise data filtering already during the collection stage is highly recommended — sending only relevant information to the system reduces infrastructure load and significantly improves query response times.

Monitoring the OpenSearch cluster itself is also a crucial part of daily operations for DevOps teams and data analytics specialists. Built-in tools and APIs allow real-time tracking of system health, resource usage, and potential errors, making it easier to maintain stability and plan for scaling.

Conclusion

Building a flexible data analytics system with OpenSearch is a proven solution for organizations seeking effective ways to analyze large datasets, control costs, and maintain full technological independence. Thanks to the broad ecosystem of tools — including Logstash, Fluentd, Beats, and OpenSearch Dashboards — it's possible to create a scalable platform that supports both infrastructure monitoring and advanced operational or security analytics.

Deploying OpenSearch is not only a step toward greater IT system transparency but also a foundation for a modern, flexible data architecture designed to meet the evolving needs of today's analytics and operations teams.