ELK Master Class - Elasticsearch, Beats, Logstash and Kibana Course Overview

To ensure that participants are able to fully engage with and benefit from the ELK Master Class - Elasticsearch, Beats, Logstash, and Kibana course, the following prerequisites are recommended:

• Basic understanding of Linux or Unix-like operating systems, including familiarity with the command-line interface.
• Familiarity with basic concepts of networking and data transfer protocols such as HTTP.
• Fundamental knowledge of JSON (JavaScript Object Notation) format, as it is commonly used for data representation in Elasticsearch.
• Awareness of the basics of system administration, including software installation and configuration.
• Prior experience with any programming or scripting language (e.g., Python, Ruby, or Shell scripting) is helpful but not mandatory.
• Understanding of data logging, monitoring, and analysis concepts can be beneficial.
• Basic knowledge of database concepts and data structures, which will aid in understanding Elasticsearch's indexing and storage mechanisms.

While prior knowledge in these areas will be advantageous, the course is designed to guide learners through the fundamental concepts and practical applications of the ELK stack. Enthusiasm to learn and a willingness to explore new technologies are as important as the technical prerequisites mentioned above.

Target Audience for ELK Master Class - Elasticsearch, Beats, Logstash and Kibana

The ELK Master Class at Koenig Solutions is designed for professionals seeking expertise in Elasticsearch, Beats, Logstash, and Kibana for data analysis and visualization.

• Data Engineers
• DevOps Engineers
• System Administrators
• IT Operations Staff
• Search and Analytics Engineers
• Security and Incident Response Analysts
• Software Developers
• Data Scientists
• Business Intelligence (BI) Professionals
• Technical Architects
• Cloud Infrastructure Engineers
• Monitoring and Observability Personnel

Learning Objectives - What you will Learn in this ELK Master Class - Elasticsearch, Beats, Logstash and Kibana?

Introduction to the ELK Master Class Learning Outcomes:

In the ELK Master Class, participants will learn to deploy and manage the Elastic Stack, effectively utilizing Elasticsearch, Beats, Logstash, and Kibana for real-time data processing and visualization.

Learning Objectives and Outcomes:

• Understand the core components and architecture of the Elastic Stack, and the role each element plays in data analysis.
• Install and configure the Elastic Stack components, ensuring a fully operational environment for data ingestion and visualization.
• Gain proficiency in Elasticsearch fundamentals, including cluster management, REST APIs, and the Query DSL for advanced data retrieval.
• Learn to create and manage documents, indices, and searches in Elasticsearch to extract actionable insights from data.
• Master Kibana for data exploration, visualization, and dashboard creation, enhancing the ability to interpret and present data effectively.
• Develop Logstash pipelines for efficient data processing and transformation, leveraging input, filter, and output plugins.
• Implement Beats for data shipment, focusing on Filebeat, to streamline log data transfer from various sources to the Elastic Stack.
• Acquire skills to monitor, troubleshoot, and optimize the performance of Elasticsearch clusters, ensuring reliability and scalability.
• Explore various use cases of the Elastic Stack, recognizing its advantages and potential limitations in different scenarios.
• Apply alerting and monitoring techniques within Kibana to maintain oversight of data and system health.