ELK Stack : Get Started with Elasticsearch, Logstash, Kibana, & Beats

Introduction
Elastic Stack, formerly known as the ELK stack, is a popular suite of tools for ingesting, viewing, and managing log files. As open-source software, you can download and use it for free (though fee-based and cloud-hosted versions are also available).
Prerequisites
- A system with Elasticsearch installed
What is ELK Stack?
ELK stands for Elasticsearch, Logstash, and Kibana. In previous versions, the core components of the ELK Stack were:
- Elasticsearch – The core component of ELK. It works as a searchable database for log files.
- Logstash – A pipeline to retrieve data. It can be configured to retrieve data from many different sources and then send it to Elasticsearch.
- Kibana – A visualization tool. It uses a web browser interface to organize and display data.

Additional software packages called Beats are a newer addition. These are smaller data collection applications, specialized for individual tasks. There are many different Beats applications for different purposes. For example, Filebeat is used to collect log files, while Packetbeat is used to analyze network traffic.
Due to the ELK acronym quickly growing, the Elastic Stack became the more satisfactory and scalable option for the name. However, ELK and Elastic Stack are used interchangeably.
Why Use ELK Stack?
The ELK stack creates a flexible and reliable data parsing environment. Organizations, especially ones with cloud-based infrastructures, benefit from implementing the Elastic stack to address the following issues:
- Working on various servers and applications creates large amounts of log data, which is not human-readable. The ELK stack serves as a powerful centralized platform for collecting and managing unstructured information, turning it into useful assets in the decision-making process.
- The ELK stack with basic features is open source, which makes it a cost-efficient solution for startups and established businesses alike.
- The Elastic stack provides a robust platform for performance and security monitoring, ensuring maximal uptime and regulation compliance.
The Elastic stack addresses the industry gap with log data. The software can reliably parse data from multiple sources into a scalable centralized database, allowing both historic and real-time analysis.
How Does Elastic Stack Work?
The Elastic stack follows certain logical steps, all of which are configurable.
1. A computer or server creates log files. All computers have log files that document events on the system in a hard-to-read format. Some systems, such as server clusters, generate massive amounts of log files.

However, Elastic Stack is designed to help manage scalable amounts of data.
2. The various available information files are collected by a Beats application. Different Beats reach out to different parts of the server, read the files, and ship them out.

Some users may skip Beats altogether and use Logstash directly. Others may connect Beats directly to Elasticsearch.
3. Logstash is configured to reach out and collect data from the different Beats applications (or directly from various sources).

In larger configurations, Logstash can filter data from multiple systems, and collect the information into one location.
4. Elasticsearch is used as a scalable, searchable database to store data. Elasticsearch is the warehouse where Logstash or Beats pipe all the data.

5. Finally, Kibana provides a user-friendly interface for you to review the data that’s been collected.

It is highly configurable, so you can adjust the metrics to fit your needs. Kibana also provides graphs and other tools to visualize and interpret patterns in the data.
ELK Stack Supporting Applications
Additional third-party applications enhance the Elastic Stack, providing wider use-case possibilities. Some external applications supported by the ELK stack are:
- Apache Kafka
Kafka is a real-time streaming distribution platform. That means that it can read multiple data sources at once. Kafka acts as a data buffer and helps prevent data loss or interruption while streaming files quickly
- Redis
Redis is a NoSQL key-value database with incredible read/write speeds and various data types. When added to the Elastic stack, Redis often serves as a buffer for data stream spikes, ensuring no data is lost.
- Hadoop
Hadoop is a massive batch-processing data storage system. Indexing data from Hadoop into the real-time Elasticsearch engine creates an interactive bi-directional data discovery and visualization platform.
The Hadoop support comes through the Elasticsearch-Hadoop Connector, offering full support for Spark, Streaming, Hive, Storm, MapReduce, and other tools.
- RabbitMQ
RabbitMQ is a messaging platform. Elastic Stack users use this software to build a stable, buffered queue of log files.
- Nginx
Nginx is best known as a web server that can also be set up as a reverse proxy. It can be used to manage network traffic or to create a security buffer between your server and the internet.
ELK Stack Advantages and Disadvantages
The Elastic stack comes with certain benefits and drawbacks.
Advantages
- The Elastic stack and the components are free to try out and use.
- ELK offers numerous hosting options, whether on-premises or deployed as a managed service.
- The capability to centralize logging from complex cloud environments allows advanced searches and creating correlations from multiple sources on a single platform.
- Real-time analysis and visualization decrease the time taken to discover insights, enabling continual monitoring.
- Client support for multiple programming languages, including JavaScript, Python, Perl, Go, etc.
Disadvantages
- Deploying the stack is a complex process and depends on the requirements. Check out our tutorial for deploying the Elastic stack on Kubernetes.
- Growing and maintaining the ELK stack is costly and requires computing and data storage based on the data volume and storage time.
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