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Elasticsearch Tutorial - What is ELK stack (Elastic stack)

Updated: Oct 25, 2019



In this Elasticsearch tutorial, you will learn - What is ELK stack (Elastic stack)? We will go through ELK stack examples, load data into Elasticsearch stack and create Kibana dashboard.


Navigation Menu:

  1. Introduction to ELK Stack

  2. Installation

  3. Load data into Elasticsearch stack with Logstash

  4. Create Kibana Dashboard Example

  5. Kibana GeoIP Dashboard Example



What is ELK stack (now called Elastic Stack)?


ELK stack is an acronym for three open-source products - Elasticsearch, Logstash & Kibana. However all three components are maintained by Elastic.


ELK stack started as a Log Analytics solution but later it evolved into enterprise search and analytics platform.

  • Elasticsearch is based on Lucene search engine and you can consider it as a NoSQL database which has capability to index (for text search) and store the data.

  • Logstash is basically a data pipeline technique that can connect to various sources based on various plugins, apply transformations and loads data into various targets including Elasticsearch. In short, Logstash collects and transforms the data and sometimes used for data shipping as well.

  • Kibana is a data visualization platform where you will create dashboards.

  • Another tool called Filebeat is one of the Beats member which can also perform similar tasks like Logstash.



ELK Stack Architecture


Here is the basic architecture of elastic stack. Notice I haven't mentioned the source in below diagram. Usually data source for ELK stack are various log files, for example application log, server logs, database log, network switch log, router log etc.

These log files are consumed using filebeat. Filebeat acts like data collector which collects various types of log files (when we have more than one type of log file). Now-a-days, Kafka is used as another layer which distributes files collected by filebeat to various queue from where logstash transform it and stores in elasticsearch for visualization.


So complete flow would look like - [application log, server logs, database log, network switch log, router log etc] => Filebeat => Kafka => ELK Stack. Please note this could be changed based on architecture needed for a project. If there are limited types of log files, sometimes you might even not consider using filebeat or kafka and directly dump logs into ELK stack.


Fun Fact: ELK stack Google Trend


Elasticsearch is most famous amongst the stack. Refer the Google Trend shown below.


Why is ELK stack is so popular worldwide, basically due to 3 major reasons.

  • First of all, price - Its open source tool, easy to learn and free of cost. If you consider other visualization tools like QlikView and Tableau - Kibana provides you similar capabilities without any hidden cost. Elasticsearch is used by many big companies for example Wikipedia & GitHub.

  • Second, its elegant user interface. You can spend time exploring and reviewing data not trying to figure out how to navigate the interface.

  • And last but not the least, its extensible. Elasticsearch is schema-free NoSQL database and can scale horizontally. It is also used for real time analytics.



Next: ELK Installation


Navigation Menu:

  1. Introduction to ELK Stack

  2. Installation

  3. Load data into Elasticsearch stack with Logstash

  4. Create Kibana Dashboard Example

  5. Kibana GeoIP Dashboard Example


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