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Learn By Doing: Deploying and Managing the EFK Stack on Kubernetes

Vijin Palazhi
HashiCorp and CNCF Trainer
DevOps Pre-Requisite Course
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Learn the intricate details of setting up an efficient, robust and scalable observability stack with ‘Deploying and Managing the EFK Stack on Kubernetes: A Practical Guide’! The course provides a deep dive into setting up and configuring the EFK stack within a Kubernetes environment to efficiently handle log aggregation, analysis, and visualization.


The ‘Deploying and Managing the EFK Stack on Kubernetes: A Practical Guide’ hands-on course is designed for DevOps engineers, system administrators, and cloud professionals looking to deploy and manage the Elasticsearch, Fluentd, and Kibana (EFK) stack on Kubernetes. Through a combination of theoretical explanations and practical labs, participants will learn how to leverage Kubernetes resources such as Deployments, Services, and Persistent Volumes to deploy each component of the EFK stack. The course also covers advanced topics like scaling the EFK stack, monitoring and alerting configurations, and securing the stack within a Kubernetes cluster. 

Labs Overview:

Introduction to Kubernetes and the EFK Stack: Dive into the world of the EFK (Elasticsearch, Fluentd and Kibana) observability stack. Learn the basics of each component and analyze how it fits to an organization’s use case.

Deploying Elasticsearch on Kubernetes: Learn how to deploy the backend in the EFK stack -  Elasticsearch, within a Kubernetes cluster. Walk through setting up a StatefulSet for Elasticsearch to ensure stable, unique network identifiers and storage, and configuring Persistent Volumes for data storage.

Fluentd Integration with Kubernetes: Next head over to the very important component of our stack - the log shipper Fluentd. Learn how to deploy Fluentd on Kubernetes as a DaemonSet to collect logs from Kubernetes nodes and pods, and forward them to Elasticsearch.

Setting Up Kibana on Kubernetes: How can monotonous log data be made interesting? The answer - Kibana - a popular data visualization tool and the final part of our EFK stack. In this lab, learn how to deploy Kibana on Kubernetes to visualize and analyze logs stored in Elasticsearch, and expose it through a Kubernetes Service for access.

Advanced Configuration of the EFK Stack on Kubernetes: At times tailoring the EFK stack becomes essential to meet specific logging and monitoring requirements. Gain practical knowledge about advanced configurations for optimizing the EFK stack on Kubernetes, including custom Fluentd plugins for log enrichment, and Elasticsearch index management for performance.

Monitoring and Alerting for the EFK Stack on Kubernetes: Explore setting up monitoring and alerting for the EFK stack components on Kubernetes. Learn integrating monitoring solutions like Prometheus and Grafana for metrics collection and visualization  with Kubernetes, and configuring alerting based on log data and performance metrics.

Scaling the EFK Stack on Kubernetes: How to cope when the going gets tough with increasing logs volumes and query loads? In this lab, learn implementing various strategies for scaling the EFK stack. Get enlightened about horizontal scaling of Elasticsearch and Fluentd, and the use of Kubernetes autoscaling features like Horizontal Pod Autoscaler.

Securing the EFK Stack on Kubernetes: As a final step, focus on security best practices for the EFK stack on Kubernetes, including securing inter-component communications, restricting access to Kibana with authentication and authorization, and using Kubernetes network policies to control traffic flow between stack components.


The Deploying and Managing the EFK Stack on Kubernetes: A Practical Guide course provides a comprehensive learning journey through deploying and managing the EFK stack on Kubernetes, from basic setup to advanced configurations, monitoring, scaling, and securing the stack. By the end of this course, learners will have a solid understanding of deploying and managing the EFK stack on Kubernetes, enabling them to improve the observability and operational intelligence of applications running in Kubernetes.

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What our students say

About the instructor

Vijin Palazhi is the Chief Technology Officer at KodeKloud, with over a decade of experience in IT infrastructure and expertise in systems engineering. 

His skills encompass storage and backup solutions, Oracle Engineered Systems Stack, Oracle Middleware, virtualization, containerization (Kubernetes and Docker Swarm), and automation. 

Vijin has specialized in Oracle Stack, particularly in Exalogic ODA Exadata and Oracle Virtual Machine Storage and Backup. 

He also has extensive experience with storage technologies, CI/CD, cloud platforms (AWS/Oracle Cloud), data center operations, and server management.

About the collaborator

Harshita is a DevOps Lab Engineer at KodeKloud. Her interest lies in DevOps, automation and observability.

She is particularly interested in logging and application monitoring, and has worked on and configured various observability stacks.

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