1. Introduction

The ELK stack combines three open-source products: Elasticsearch, Logstash, and Kibana. All these elements work together to provide a robust integrated system, offering full-suite analytics for real-time insight into managing massive amounts of data. It is also known as Elasticsearch, which retrieves logs from all your systems and applications, analyzes and creates visualizations for security analysis, application and infrastructure monitoring, and requirement analysis to speed up troubleshooting. Each element of the stack has a crucial role:

• Elasticsearch - Elasticsearch is a core of the stack, developed on top of Apache Lucene technology, a distributed search and analytics engine. It is an ideal choice for various log analytics and searches because of its excellent performance, support in multiple languages, and leverages schema-free data in JSON format.
• Logstash - Logstash is a small, lightweight open-source server-side tool to process data that allows one to access and modify data across multiple sources and redirect them to any other location. It is used as a data pipeline for Elasticsearch since it has amazing capabilities in processing logs and has more than 200 open-source plugins for indexing your data.
• Kibana - Kibana is a user interface that represents the stack. It is a valuable tool for viewing and navigating data involving logs and time series analytics or applications and monitoring or operational intelligence. It provides the ability to design and view dashboards with attributes like heat maps and pie charts, line and bar graphs, histograms, and even built-in geo capabilities.

2. ELK Stack Architecture

Above diagram represents a typical ELK Stack architecture, used for log management, data processing and visualization. Here is a breakdown of the components:

i. Log (Input Data)

• The system collects logs or other data from various sources, such as application logs, system metrics or monitoring tools.
• These raw logs are unstructured and need processing to make them usable for analysis.

ii. Logstash (Data Processing):

• Logstash is data processing tool. It collects log from multiple source, processes them (e.g., filtering, transforming, enriching), and sends the output to a storage system like Elasticsearch.
• It acts as a pipeline, allowing to format the data before it’s indexed.

iii. Elasticsearch (Storage):

• Elasticsearch is a search and analytics engine where all the processed data from Logstash is stored.
• It indexes the data for fast querying and allows for efficient search and analysis of logs.
• This is where data is stored in a structured format.

iv. Kibana (Visualization):