Back to top

ELK Stack (Elasticsearch, Logstash, Kibana)

Duration: 2 days | Price: € 900,00

30% discount for several people from the same company


Learners must have a laptop equipped with Linux or Windows 7+, at least 5GB of free space, Java version 1.7u55 or newer and Google Chrome.


This course provides an introduction to using the ELK (Elasticsearch, Logstash, Kibana) stack for reading, normalizing, processing, indexing, and viewing data and time series. The fundamental components of the Elastic suite will be presented with a practical approach and exercises, deepening the applications and examining real use cases that exemplify the configuration and functionality for each component.

Elasticsearch, the suite’s core product, is a professional search engine that can effectively manage Big Data in any application / website. To date it is the most popular search engine in the world. Elasticsearch natively supports clustering and distributed architectures, providing full-text search functionality with a RESTful interface, therefore independent of the programming language with which it is consumed, using JSON for data representation and HTTP as a communication protocol.

Elasticsearch can be used to search for any type of document and provides a scalable, near-real-time search system with multitenancy support. Kibana is the suite tool that allows you to browse and view the data contained in the Elasticsearch indexes. By exploiting the capabilities and speed of research and data aggregation offered by Elasticsearch, Kibana allows you to create graphs and dashboards for Big Data analysis in a simple and intuitive way. Logstash is the component of the stack that takes care of retrieving, filtering, normalizing and sending data from heterogeneous sources to Elasticsearch. Its plugin architecture allows you to work with different data sources with minimal effort.


  • Elastic Stack (ELK) overview
  • What it is and when to use it
  • Terminology: Documents, Indices, Shards, Nodes, Clusters
  • Configuration and Installation
  • Distribute data across multiple nodes
  • Backup
  • What it is and when to use it
  • Configuration
  • Input, Filter and Output
  • Installation and configuration
  • Backup and restore
  • How to automatically import data from a relational database
  • How to import data from a near-real-time log
  • Best practices
  • Configuration settings
  • Searches and filters
  • Discover, Visualize and Dashboard views
  • Installation and configuration
  • Backup and restore
  • Integrate Kibana views into web and desktop applications
  • Best Practices

Who is it for?

The course is aimed at developers and software architects who need to build real-time research systems and analysis solutions, including big-data.

Request informations