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ELK Stack (Elasticsearch, Logstash, Kibana)

Duration: 2 days | Price: € 900,00

30% discount for more people from the same company

Prerequisites

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

Description.

This course provides an introduction to the use of the ELK stack (Elasticsearch, Logstash, Kibana) for reading, normalizing, processing, indexing and visualizing 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 for each component exemplify the configuration and functionality. Elasticsearch, the main product of the suite, is a professional search engine able to 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, thus independent of the programming language with which it is consumed, using JSON for data representation and HTTP as the communication protocol. Elasticsearch can be used to search any type of document and provides a scalable, near-real-time search system with multitenancy support. Kibana is the suite’s tool for browsing and visualizing the data contained in Elasticsearch indexes. Leveraging the capabilities and speed of search and data aggregation offered by Elasticsearch, Kibana allows you to easily and intuitively create charts and dashboards for Big Data analysis. Logstash is the stack component that is responsible for retrieving, filtering, normalizing and sending data from heterogeneous sources to Elasticsearch. Its plugin architecture allows working with different data sources with minimal effort.

Contents

Introduction
  • Elastic Stack (ELK) Overview
Elasticsearch
  • What it is and when to use it
  • Terminology: Documents, Indexes, Shards, Nodes, Clusters
  • Configuration and Installation
  • Distribute data across multiple nodes
  • Backing up
Logstash
  • What is it and when to use it
  • Logstash Configuration
  • Input, Filter and Output
  • Installation and Configuration
  • Backup and restore
  • How to automatically import data from a relational database
  • Importing data from a near-real-time log
  • Best practices
Kibana
  • Configuration Settings
  • Searches and Filters
  • Discover, Visualize and Dashboard Views
  • Installation and Configuration
  • Backup and restore
  • Integrating 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 search systems and analysis solutions, including big-data.

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