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Kibana and Logstash Fundamentals

Learn to Build an ELK Stack


Our Elasticsearch training classes have a 4.67/5 rating based on 15 reviews

This 2-hour online course is the fastest way to get started with Kibana and Logstash, so you can build an ELK stack. You will learn all the important information about ELK functionality, from parsing logs to building dashboards. This Elasticsearch online course is taught by Radu Gheorghe‌‌, a seasoned Elasticsearch instructor and consultant from Sematext, author of “Elasticsearch in Action”, and frequent conference speaker.

Why attend?

  • Small, interactive, instructor-led classes
  • Lots of hands-on exercises
  • Customized learning experience
  • More flexible – no need to travel
  • Certificate of Completion included

What’s Included

  • 2-hour online training
  • A digital copy of the training material
  • Docker Compose files, configs, scripts, etc.
  • Certificate of Completion

Next Class: May 7 See Upcoming Classes

$200.00 -10% Early Bird Register Now

Full day classes available upon request

Looking for a more general and extended knowledge-based Elasticsearch training?

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Who should attend?

This Elasticsearch online course is designed for anyone who:

  • Wants to understand how Logstash and Kibana works
  • Wants to set up an in-house ELK stack

What attendees say

Sematext was an ideal training partner for We had just recently adopted Elasticsearch on a new project, and they gave us two days of solid training that was tailored to our team’s needs. The material was built atop strong foundations and moved quickly into advanced areas around querying, Lucene internals, and cluster performance. It was clear that it was all informed by real-world experience operating these systems at scale.

Andrew Montalenti CTO/Founder –

Course Outline

Basic setup: a faster, distributed grep on top of Elasticsearch
  • Setting up Kibana
  • Index patterns and saved fields
  • Kibana’s Discover pane: running full text search on your logs
  • Saved searches
  • Setting up Logstash: which options you have in terms of packaging and configuration
  • Configuring Logstash to read data and send it to Elasticsearch
  • Lab
    • Indexing data with Logstash
    • Searching through the indexed data with Kibana
    • Search syntax basics
Using structured logging for more precise searches and meaningful visualizations
  • Reading data from files or over the network
  • Using Kafka as a central buffer to scale multiple Logstash instances
  • Using Logstash to reindex Elasticsearch documents
  • Using grok or dissect to parse unstructured data. Advantages & disadvantages of both
  • Enriching data with GeoIP information or from external sources
  • Using the mutate filter to change fields of your structured event
  • Parsing various date formats
  • Using conditionals in Logstash configuration
  • Visualizations in Kibana: line, area and pie charts, metrics, etc
  • When to use which visualization to derive insights from your data
  • Building dashboards with saved visualizations
  • Lab
    • Parse and enrich Apache logs with Logstash
    • Searching in specific fields with Logstash
    • Saved searches and visualizations
    • Building custom dashboards

Main Topics

  • Setting up Kibana and Logstash
  • Parsing Unstructured Data with Logstash
  • Kibana Visualizations
  • Building Kibana Dashboards
  • Using Kafka to buffer logs

Elasticsearch Training

Upcoming Classes

Pick from a wide range of short (2h), use case focused classes to match your exact needs

May 7, 2019Kibana and Logstash Fundamentals$200 / person Only $180 / person before 6th AprRegister Now
Sept 17, 2019Kibana and Logstash Fundamentals$200 / person Only $180 / person before 20 JulyRegister Now
Dec 3, 2019Kibana and Logstash Fundamentals$200 / person Only $180 / person before 30 SeptRegister Now
Radu Gheorghe

About the trainer

Radu Gheorghe

Your trainer is an active Elasticsearch consultant. Radu has worked with clients from 20+ different industries and is the author of Elasticsearch in Action. Here are some problems that Radu solved for Sematext clients recently:

  • Improved search relevancy using Learning to Rank
  • Optimized multiple petabyte-scale clusters. Some up to 400 nodes.
  • Designed Elasticsearch index and cluster architecture for dozens of clients
  • Optimized log ingestion pipelines to parse and enrich 100K+ events/second
  • Helped clients reduce production Elasticsearch and ingestion pipeline costs by as much as 10x

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