Let’s Learn Elastic Stack (Part 1)— Introduction

Isanka Rajapaksha
4 min readMay 10, 2022

The fastest-growing companies tend to be those that pull the most value from the widest range of relevant data. Along with huge opportunities, though, data analysis can pose huge challenges.The sheer volume of data required to give companies a strategic edge can also demand significant investments in new infrastructure and personnel; not every business strikes the right balance between investment and return.

To achieve maximum ROI from data-related investments, companies need solutions that: improve data visibility to knowledge workers and provide business insights from data. This is where the Elastic Stack comes in.

The Elastic Stack is popular because it fulfills a need in the log management and analytics space. Monitoring modern applications and the IT infrastructure they are deployed on requires a log management and analytics solution that enables engineers to overcome the challenge of monitoring what are highly distributed, dynamic and noisy environments.

The Elastic Stack helps by providing users with a powerful platform that collects and processes data from multiple data sources, stores that data in one centralized data store that can scale as data grows, and that provides a set of tools to analyze the data.

What exactly is Elastic Stack? Why is this software stack seeing such widespread interest and adoption? How do the different components in the stack interact?

In this guide, we will take a comprehensive look at the different components comprising the stack. We will help you understand what role they play in your data pipelines, how to install and configure them, and how best to avoid some common pitfalls along the way with a series of articles.

What is the Elastic Stack?

The Elastic Stack is a group of open source products from Elastic designed to help users take data from any type of source and in any format, and search, analyze and visualize that data in real time. The product group was formerly known as the ELK Stack for the core products in the group — Elasticsearch, Logstash and Kibana — but has been rebranded as the Elastic Stack. A fourth product, Beats, was subsequently added to the stack. The Elastic Stack can be deployed on premises or made available as software as a service (SaaS). Elasticsearch supports Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure.

  • ElasticSearch — Used for deep search and data analytics. It’s an open source distributed NoSQL database, built in Java and based on Apache Lucene. Lucene takes care of storing disk data, indexing, and document scanning while ElasticSearch keeps document updates, APIs, and document distribution between ElasticSearch instances in the same cluster.
  • Logstash — Used for centralized logging, log enrichment, and parsing. It’s an ETL (Extract, Transfer, Load) tool that transforms and stores logs within ElasticSearch.
  • Kibana — Used for powerful and beautiful data visualizations. It’s a web-based tool through which ElasticSearch databases visualize and analyze previously stored data.
  • Beats are data shippers that are installed on servers as agents used to send different types of operational data to Elasticsearch either directly or through Logstash, where the data might be enhanced or archived.

Why Use Elastic Stack?

The Elastic Stack meets the needs of growing businesses with an efficient, integrated toolset designed to deliver actionable real-time insights from large sets of search data. Its highly active community and years of successful implementations offer an unmatched combination of maturity and future-proof development.

There are scores of reasons to consider the Elastic Stack. Here are four of the most important.

Enterprise Search
Empower users to search for everything from anywhere. Elastic can unify your content platforms into a highly personalized and relevant search experience. By unifying content platforms at the search level, the Elastic Stack empowers users to search across enterprise systems and data silos, giving them a comprehensive yet highly personalized search experience.

The Elastic Stack brings real-time metrics, logs, and APM traces into a single easily consulted view. Companies can spot opportunities as they arise and challenges as they begin to develop, allowing for a quick and profitable response.

Elastic deploys at scale, regardless of a company’s technical infrastructure. Public and private cloud implementations, bare-metal or containerized, even as a SaaS solution, Elastic is built to work with any company’s systems and to adapt as companies grow.

On top of the platform’s internal security (index encryption, field-level security on documents) the SIEM app collects security information across the enterprise and provides richly detailed dashboards that allow close scrutiny of security operations.

Wrap up

Hopefully by the end of this article, you are familiar with Elastic Stack. I’ve written about the architecture of each ELK stack component. You can refer to the my folowing blogs to improve your knowledge more on ELK stack.

  1. Let’s Learn Elastic Stack(Part 2) — Elasticsearch Architecture
  2. Let’s Learn Elastic Stack (Part 3) — Logstash Architecture
  3. Let’s Learn Elastic Stack (Part 4) — Kibana Architecture
  4. Let’s Learn Elastic Stack(Part 5) — Filebeat Architecture



[1] https://www.elastic.co/

[2] https://www.techtarget.com/searchitoperations/definition/Elastic-Stack

[3] https://logz.io/learn/complete-guide-elk-stack/