Course Introduction

Welcome to this learn-by-doing lesson: “Deploying and Managing the EFK Stack on Kubernetes.” I’m Vijin Palazhi, and along with Harshita Joshi, we’ll guide you through hands-on labs that teach how to deploy, configure, scale, monitor, and secure the EFK stack (Elasticsearch, Fluentd, Kibana) on Kubernetes. This course is designed for DevOps engineers, system administrators, and cloud professionals who want practical, repeatable workflows for log aggregation, analysis, and visualization in Kubernetes environments. You will get step-by-step exercises, live cluster access, and verification checks so you can validate each hands-on task as you progress.

The image illustrates a diagram showing three roles—DevOps Engineers, System Administrators, and Cloud Professionals—connected to the task of "Deploy & Manage" in the center.

What you’ll practice

Deploy Elasticsearch on Kubernetes to store and index logs.
Collect and forward logs using Fluentd as the aggregator/log shipper.
Visualize and explore logs with Kibana dashboards.
Configure persistence, resource requests/limits, and cluster scaling.
Monitor cluster health and secure the EFK stack for production use.

Each lab builds on the previous one so you move from fundamentals to production-ready patterns. How the labs work

Open the terminal in the lab by clicking the toggle icon. This connects directly to a live Kubernetes cluster provisioned for the exercise.
Use the Hint and Solution tabs if you get stuck; they’re designed to guide rather than replace the learning.
After you complete a step, click the Check button to validate the task and unlock the next step.

Use the terminal to run Kubernetes commands against the live cluster. If a task asks you to create or switch to a namespace, follow the exact command shown in the Tasks tab and then use the Check button to validate. Example namespace creation and switch:

kubectl create namespace elastic-stack
kubectl config set-context --current --namespace=elastic-stack

Lab navigation and UI

Overview tab: read the conceptual context and objectives for the module.
Tasks tab: follow workbook-style instructions with exact commands and validations.
Terminal: run commands directly against the sandbox cluster.
Toggle Panel Size: expand or collapse the terminal panel for better visibility.

The image is a screenshot from a lab course titled "Introduction to Kubernetes and the EFK Stack," detailing navigation steps for a learning environment with tabs for "Overview" and "Task." It includes instructions on using the "Toggle Panel Size" feature.

Course scope and progression This lesson begins with Kubernetes and EFK fundamentals, then moves on to practical deployment and operational tasks:

Module focus	What you’ll learn	Example outcome
Core concepts	Basics of Kubernetes and the EFK components	Understand how Elasticsearch, Fluentd, and Kibana interact
Elasticsearch deployment	Stateful workloads, persistence, and scaling	Run a resilient Elasticsearch cluster on Kubernetes
Fluentd integration	DaemonSet configuration and log forwarding	Collect node and application logs reliably
Kibana setup	Dashboards, visualizations, and index patterns	Explore and visualize logs stored in Elasticsearch
Production readiness	Resource limits, persistence, and monitoring	Harden and scale the EFK stack for real workloads
Security & monitoring	TLS, authentication, and observability best practices	Secure access and monitor cluster health and logs

The image shows a tutorial interface with a task focused on deploying Elasticsearch on Kubernetes, instructing users to create and switch to a namespace called "elastic-stack." There is a simple code terminal on the right side.

Best practices emphasized in this course

Use namespaced resources (for example, use elastic-stack as the deployment namespace).
Define resource requests and limits to ensure stable performance.
Persist Elasticsearch data using appropriate StorageClasses and PersistentVolumeClaims.
Run Fluentd as a DaemonSet to collect logs across nodes.
Secure Kibana and Elasticsearch endpoints for production access.

This course uses a live sandbox Kubernetes cluster for labs. Do not use production credentials or expose sensitive data while completing exercises. Always follow your organization’s security policies when applying these patterns to production.

The image showcases an overview of course topics related to Kubernetes and the EFK (Elasticsearch, Fluentd, Kibana) Stack, with eight specific topics listed.

Next steps Ready to begin? Open the first lab, read the Overview tab, then follow the Tasks tab. Start by creating the elastic-stack namespace and proceed through the deployment, configuration, and validation steps. Links and references

Course Introduction

Watch Video

Practice Lab