Elasticsearch laboratory

This laboratory aims to practice working with Elasticsearch through writing queries and aggregations and try out some visualization tools provided by Kibana.

Pre-requisites

You need the following tools to complete this laboratory:

• Windows, Linux, or Mac: Every component you need is platform-independent.
• Elasticsearch and Kibana version 7.15.x
  • Either run them using Docker, if you have Docker on your machine
  • Or you can download the binaries (see in Exercise 2)
    • You can also use a VM at https://cloud.bme.hu, see details here.
• PowerShell
  • Included in Windows
  • Install PowerShell Core on Linux
  • Install PowerShell Core on MacOS
• A GitHub account and a git tool

Material to review before the laboratory

• The expected mode of submitting your work, as detailed here.
• The material covered in course Business intelligence related to the topic, including, but not limited to
  • The demo material covered during the semester https://github.com/peekler/Business-Intelligence-Demos/tree/master/ELK
• Elasticsearch documentation for reference
  • https://www.elastic.co/guide/en/elasticsearch/reference/current/search.html
  • https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html

Grading

Exercises 1 & 2 & 3 help you prepare the environment. Starting with exercise 4, each exercise is worth one grade when completed successfully. For an exercise to count as successful, you must finish all its subtasks successfully. (E.g. if you complete exercises 4 and 5 successfully, but there is an error in exercise 6, and you did not complete exercise 7, the resulting grade will be 3.)