Elastic Stack for Observability and Log Management 

Course & Training

Hands-on intensive course on the Elastic Stack. Learn how to efficiently manage and visualize logs, metrics, and traces with Elasticsearch, Logstash, Kibana, and Beats.

In today's IT landscape, the ability to collect, process, and analyze large amounts of logs, metrics, and traces is crucial for success. The Elastic Stack is a powerful, open-source toolset that masters these tasks. In this course, you will learn how to use the Elastic Stack to monitor systems, analyze log data, and create meaningful visualizations and dashboards. This course equips you with the tools to reliably monitor systems and quickly detect issues.

In-House Course:

We are happy to conduct tailored courses for your team - on-site, remotely or in our course rooms.

Request In-House Course

Public Course Schedule:

Content:


In this course, we dive deep into the world of the Elastic Stack and provide you with the knowledge to build a comprehensive observability solution. We cover the following topics:

– Architecture and Core Concepts:
... - The Importance of Observability: Logs, Metrics, and Traces
... - The Role of the Elastic Stack: Elasticsearch, Logstash, Kibana, and Beats
... - Core Data Concepts: Indices, Documents, and Clusters
... - Integration into modern environments like Kubernetes
– Data Processing and Collection:
... - Log processing with Logstash pipelines
... - Data collection with various Beats
– Analysis with Kibana Query Language (KQL):
... - Introduction to KQL for log analysis
... - Analysis of metrics with KQL
– Visualization and Dashboards:
... - Log visualization with Kibana
... - Creation of interactive dashboards
... - Effective use of KQL in Kibana
– Alerting and Notifications:
... - Introduction to the alerting framework
... - Creating alerts based on KQL queries

This course provides a hands-on introduction to the Elastic Stack, enabling you to proactively monitor your systems and identify issues faster.


Disclaimer: The actual course content may vary from the above, depending on the trainer, implementation, duration and constellation of participants.

Whether we call it training, course, workshop or seminar, we want to pick up participants at their point and equip them with the necessary practical knowledge so that they can apply the technology directly after the training and deepen it independently.

Goal:

Participants understand the architecture and components of the Elastic Stack. They can collect and process data with Beats and Logstash, analyze it with KQL, and create meaningful dashboards and targeted alerts in Kibana to implement an effective monitoring and log management solution.


Form:

Collaboratively working through an incremental sample project with brief explanations, assignments, solutions, and ongoing support from our top trainers.


Target Audience:

Software and System Engineers, DevOps specialists, and IT administrators responsible for monitoring, log analysis, and maintaining system health, as well as anyone evaluating or introducing modern monitoring solutions.


Requirements:

Basic understanding in the use of the command line.
Basic understanding of version control with GIT.
Basic knowledge of IT infrastructure is helpful but not mandatory.


Preparation:

After registration, each participant receives a questionnaire with installation instructions. Based on the answers, we provide individual feedback. If required, a mini-setup via remote session can be performed before the training (included in the training). Training laptops can be provided on request, but we recommend to work with your own device to be able to continue directly afterwards.

Request In-House Course:

In-House Kurs Anfragen

Waitinglist for public course:

Sign up for the waiting list for more public course dates. Once we have enough people on the waiting list, we will determine a date that suits everyone as much as possible and schedule a new session. If you want to participate directly with two colleagues, we can even plan a public course specifically for you.

Waiting List Request

(If you already have 3 or more participants, we will discuss your preferred date directly with you and announce the course.)

More about the Elastic Stack



The Elastic Stack, formerly known as the ELK Stack, is a comprehensive suite of open-source tools for searching, analyzing, and visualizing data in real-time. It consists of the core components Elasticsearch, Logstash, and Kibana, and is complemented by the Beats family for data collection.




History


The history of the Elastic Stack began in 2010 with the development of Elasticsearch by Shay Banon. Elasticsearch was designed as a scalable, distributed search and analytics engine. Shortly thereafter, Kibana was developed as a visualization frontend and Logstash for server-side data processing. These three products formed the original ELK Stack.


With the introduction of Beats , a family of lightweight data shippers, the stack was expanded to become the Elastic Stack. Beats enables the easy collection of data from edge machines and its forwarding to Elasticsearch or Logstash.


Today, the Elastic Stack is one of the leading platforms for observability (logs, metrics, traces), security, and enterprise search. It is used by companies worldwide to monitor complex data landscapes, detect security threats, and gain business-critical insights. Continuous development, including the integration of machine learning and AI features, secures the stack's position as a forward-looking technology.