Kibana Data Analyst - Virtual
Instructor-led Virtual Training
|Location||Nov 2019||Dec 2019||Jan 2020||Feb 2020||Mar 2020||Apr 2020||May 2020||Jun 2020||Jul 2020||Aug 2020||Sep 2020|
|US CENTRAL TIMEZONE||
Dec 16 – Dec 19|
|US PACIFIC TIMEZONE||
Nov 18 – Nov 21|
|CENTRAL EUROPEAN TIMEZONE||
Nov 25 – Nov 28||
Dec 9 – Dec 12||
Jan 6 – Jan 9||
Feb 3 – Feb 6|
|APJ AUSTRALIAN TIME||
Nov 25 – Nov 28|
|APJ SINGAPORE TIMEZONE||
Nov 25 – Nov 28|
Classes in bold are guaranteed to run!
This On-Demand course focuses on using Kibana to analyze data in Elasticsearch. You will learn how to create visualizations and dashboards across a variety of data sets, as well as how to manage Kibana by handling saved objects and creating spaces.
Note: Data Analysts working with time series and infrastructure data should attend the Kibana Data and Ops Analyst course.
Starting with the fundamentals, you will learn the core concepts of data analysis using Kibana — from simple aggregation-based charts to complex time series visualizations — through lectures, labs, and Q&A sessions. By the end of this course, you will be able to easily find answers and anomalies in your data sets.
Note: This course is also available in a classroom environment.Topics Covered
- Kibana fundamentals
- Kibana search
- Kibana visualizations
- Kibana dashboards
- Kibana visual builder
- Kibana management
Any technical or non-technical users, including Data Analysts, Security Analysts, Operations Analysts, DevOps, and various Business Professionals
- Laptop with Wi-Fi connectivity
- Mac, Linux, or Windows
- Latest version of Chrome or Firefox (other browsers not supported)
- Disable any ad blockers and restart your browser before class
Virtual Classroom Information
This virtual training is taught over 4 consecutive days for 4 hours per day. Classes are offered in the morning and afternoon across different time zones. Open a specific offering (listed at the top of the page) to see the hours in which it will be held.
We encourage participants to set up their systems prior to logging into the training environment the first day of the class. We also encourage being logged in 15 minutes prior to the start of class each day.