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Centralized Logging with ELK Course

Learn the Best Practices in Building a Logging Pipeline and Analyzing Data with Timelion


Our Elasticsearch training classes have a 4.74/5 rating based on 20 reviews

This Elastic logging online course goes through the advanced visualization capabilities of Kibana, and looks deep into the patterns for shipping time-series data to Elasticsearch efficiently and reliably with Timelion. It covers shipping with Logstash, Filebeat and Logagent, as well as using Kafka for buffering and general best practices in building a logging pipeline. This Elasticsearch online class 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, tutorials and training materials
  • Customized learning experience
  • More flexibility – no need to travel
  • Real-world use cases and scenarios
  • 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: TBA 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 with basic understanding of what Elasticsearch, Logstash and Kibana are and how they work
  • Anyone looking to extend their knowledge, in order to build and run large-scale log centralization setups.

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

Advanced visualizations
  • Timelion charts and sheets
  • Cumulative metrics
  • Working with multiple time series
  • Customizing Timelion charts
  • Dealing with missing data points
  • Removing noise
  • Lab
    • Chart average and moving average
    • Chart occurrences of multiple values of a field
Tuning the ingestion pipeline
  • Pipeline patterns for reliability, scalability and performance
  • Logstash tunables
  • Filebeat installation and configuration
  • Filebeat tunables
  • Installing and configuring Logagent
  • Parsing files with Logagent
  • Lab
    • Tune Logstash for throughput
    • Set up a Logstash → Kafka → Logstash → Elasticsearch pipeline
    • Set up a Filebeat → Elasticsearch pipeline
    • Setup a Logagent → Elasticsearch pipeline
Scaling the ingestion pipeline
  • Sending logs directly to Elasticsearch
  • Using Logstash as an aggregator
  • Using Logagent as an aggregator
  • General decision points and tradeoffs
  • Lab
    • Set up a Filebeat → Ingest → Elasticsearch pipeline
    • Set up a Filebeat → Logstash → Elasticsearch pipeline
    • Set up a Filebeat → Kafka → Logstash → Elasticsearch pipeline
    • Set up a rsyslog → Logagent → Elasticsearch pipeline

Main Topics

  • Analyzing time-series data with Timelion
  • Using Kafka as a central buffer
  • Tuning Logstash, Filebeat and Logagent for performance
  • Pipeline patterns and their trade-offs

Elasticsearch Training

Upcoming Classes

Pick from a wide range of short (2h), use case focused classes to match your exact needs. Delivery method: Live Online. Time: 11:00 AM to 1:00 PM ET.

To be announced
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 issues 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 and DevOps teams reduce production Elasticsearch and ingestion pipeline costs by as much as 10x

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