The ELK stack is an acronym used to describe a stack that comprises three popular open-source projects: Elasticsearch, Logstash, and Kibana. Often mentioned as Elasticsearch, the ELK stack gives you the power to aggregate logs from all of your systems and applications, analyze these logs, and make visualizations for application and infrastructure monitoring, faster troubleshooting, security analytics, and more.

ELK Stack Components

As we mentioned earlier, the ELK Stack is made up of three components: 1) Elasticsearch, 2) Logstash and 3) Kibana.

What is Elasticsearch?

Elasticsearch is a NoSQL database. It is claimed that Elasticsearch is based on the Lucene search engine, and it is built with RESTful APIS. Out of the many offerings of Elasticsearch, simple deployment, maximum reliability, and easy management are some of them. One of the reasons behind its popularity is that it offers advanced queries to perform detailed analysis and stores all the data centrally. It helps execute a quick search of the documents.

Elasticsearch also gives you the ability to store, search and analyse a big volume of data. It is mostly used as the underlying engine to powers applications that complete search requirements. It is quite popular in search engine platforms for modern web and mobile applications.

What is Logstash?

Logstash is the data collection pipeline tool. It collects data inputs and feeds them into the Elasticsearch. It gathers all types of data from a different source and makes it available for further use.

Logstash can unify data from disparate sources and normalize the data into your desired destinations. It gives you the ability to cleanse and democratize all your data for analytics and visualization of use cases.

What is Kibana?

Kibana is a data visualization that completes the ELK stack. This tool is used for visualizing the Elasticsearch documents and helps developers to have a quick insight into them. Kibana dashboard offers various interactive diagrams, geospatial data, and graphs to visualize complex choirs.

Kibana is mostly used to view, search and interact with data stored in Elasticsearch directories. It also helps you in performing advanced data analysis and visualizing your data in a variety of tables, charts, and maps.

ELK Stack Architecture

The different components of the ELK Stack provide a simple yet powerful solution for log management and analytics.

All the components present in the ELK Stack were designed carefully to interact and play nicely with each other without too much extra configuration.

For a small-sized development environment, the classic architecture will look as follows:

Beats (Data Collection) → Logstash (Data Aggression and Processing) → Elasticsearch (Indexing and Storage) ← Kibana (Analysis and Visualization)

However, to handle more complex pipelines built for handling large amounts of data in production, additional components are likely to be added into your logging architecture:

Beats (Data Collection) → Redis/Kafka/Rabbit MQ (Buffering) →Logstash (Data Aggression and Processing) → Elasticsearch (Indexing and Storage) ← Kibana (Analysis and Visualization)

The illustration shown above is a very simplified explanation for the sake of understanding. A full production-grade architecture will consist of multiple Elasticsearch nodes, perhaps multiple Logstash instances, an archiving mechanism, an alerting plugin, and a full replication across regions or segments of your data center for high availability.


We can conclude without doubt that the ELK Stack offers a powerful data analytics platform at an affordable price point. Till now we have discussed everything about the role that each of these components plays in the data pipeline. With the information shared, you’ll be better prepared to make an informed decision about the Elastic Stack and how it can serve your organization’s needs.

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