Elastic Analyst Course Overview

Elastic Analyst Course Overview

The Elastic Analyst course is designed for those looking to deepen their understanding of the Elastic Stack with a focus on searching, visualizing, and analyzing data within Kibana. This course prepares learners for the Elastic Certified Analyst exam, equipping them with the skills needed to effectively work with Elastic products. Throughout the course, participants will gain hands-on experience defining index patterns, utilizing the Kibana Query Language (KQL), creating powerful visualizations, and constructing dashboards that provide insights into their data. Additionally, learners will explore advanced analysis techniques including machine learning jobs. This training is not only beneficial for those aiming to achieve the Elastic Certified Engineer Certification but also for professionals who wish to proficiently analyze large datasets and transform them into actionable intelligence. By the end of the course, attendees will have a solid foundation to leverage the full capabilities of the Elastic Stack in real-world scenarios.

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  • Live Online Training (Duration : 24 Hours)
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  • Live Online Training (Duration : 24 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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  • Power Packed 12 Hours (Edited from 24 hours of Live Training)
  • 6 Months Access to Videos
  • Access via Laptop, Tab, Mobile, and Smart TV
  • Certificate of Completion
  • Hands-on labs




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Course Prerequisites

To ensure a productive learning experience in the Elastic Analyst course, potential students are recommended to possess the following prerequisites:

  • Basic understanding of data structures and common data formats such as JSON
  • Familiarity with the concepts of databases and data storage
  • Awareness of search engine concepts and principles
  • Some exposure to analytics and data visualization
  • Basic knowledge of command-line interfaces and operating systems (Windows, Linux, or macOS)
  • An elementary understanding of networking principles, including TCP/IP
  • Willingness to learn new querying languages, specifically the Kibana Query Language (KQL)

These prerequisites are designed to ensure that students have a foundational understanding that will allow them to grasp the concepts taught in the Elastic Analyst course effectively. Please note that while having experience with Elasticsearch and Kibana is beneficial, it is not mandatory for this entry-level course.

Target Audience for Elastic Analyst

The Elastic Analyst course by Koenig Solutions is designed for professionals seeking expertise in Elastic Stack data searching, visualization, and analysis.

  • Data Analysts
  • Business Intelligence Professionals
  • IT Analysts
  • Search and Log Analysis Engineers
  • Data Scientists
  • Security Analysts (particularly those interested in SIEM)
  • DevOps Engineers (involved in monitoring and observability)
  • Kibana Users and Dashboard Creators
  • Elasticsearch Developers
  • Software Engineers who implement Elastic Stack solutions
  • System Administrators involved in data indexing and searching
  • Network Administrators focusing on real-time data analysis

Learning Objectives - What you will Learn in this Elastic Analyst?

Introduction to the Elastic Analyst Course Learning Outcomes

In the Elastic Analyst course, students will master the essentials of searching, visualizing, and analyzing data using the Elastic Stack, particularly focusing on Kibana's capabilities.

Learning Objectives and Outcomes

  • Define and manage index patterns in Kibana, with and without applying a Time Filter field, to organize and access data efficiently.
  • Apply the Kibana Query Language (KQL) to filter and retrieve specific data sets matching various criteria, enhancing search precision.
  • Create, customize, and pin filters to refine search results and boost the relevance of data analysis.
  • Construct various visualizations including Metric, Gauge, Lens, and others, to represent data in an easily digestible and actionable format.
  • Perform advanced visualizations like geo-mapping and Time Series Visual Builder (TSVB) to gain insights from spatial data and time-based patterns.
  • Utilize sub-bucket aggregations to split visualizations, allowing for more granular analysis of data segments.
  • Implement calculations and aggregations such as moving averages and derivatives within visualizations to identify trends and anomalies.
  • Develop comprehensive dashboards that aggregate multiple visualizations into a single view, providing a cohesive analysis experience.
  • Explore Machine Learning features within Kibana to set up jobs for anomaly detection and insights into data behavior.
  • Create and leverage scripted fields and Space functionalities to extend Kibana's analytical capabilities and personalize the analysis environment.