Advanced Problem and Anomaly Detection with Zabbix
This course is completely focused on problem detection. It covers areas ranging from creating simple triggers to using new long-term analytical functions.
Information about the course
| Designed for the product: | Zabbix 7.0 |
|---|---|
| Group size: | Small groups (up to 10 students) |
| Requirements: | None |
| Course language: | English or Czech |
| Available online: | Yes |
| Course length: | 1 DAY |
| Course price: | € 490 excluding VAT |
Course content
Day 1
- Detecting problems in Zabbix
- Detecting problems by combining multiple metrics
- Most used trigger functions
- Time-based trigger functions
- Predictive trigger functions
- Long-term analysis using trend data
- Preventing trigger flapping
- Detecting problems using log files
- Dependencies between problems
- Reacting to problems using actions
- Event correlation rules
Tomáš Heřmánek
CEO & Zabbix Certified TrainerTomáš is a huge fan of OpenSource technologies, including Zabbix. He specializes in application servers, automation, and monitoring. Over the last ten years, he has been involved in several large-scale projects that have received extremely positive feedback. Tomáš holds a unique Zabbix Certified Trainer certificate from Zabbix SIA. Additionally, he enjoys sharing his expertise through training sessions and community events.
Additional information
No previous training required.
The course includes advanced practical exercises.
Each participant will be provided with a PDF of the presentations and practical exercises for the training.
Certificate
- Each participant will obtain a certificate of Zabbix 7.0 course completion
Course certificates and gifts
For all attendees
Acquired skills and knowledge
Prevent alert storms and false alarms
by utilizing a variety of Zabbix trigger functions
React only to root cause problems
with trigger dependencies and event correlation
Configure dynamic problem detection thresholds
with baseline monitoring and anomaly detection features
Detect problems proactively
by configuring predictive problem detection