Live Online Kubernetes Course for Data Engineering

Live Course Module: Kubernetes Course for Data Engineering

Total Duration: 40 Hours (5 Weeks)

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Week 1: Introduction to Kubernetes & Container Orchestration (Beginner)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Introduction to Container Orchestration

  • Why orchestration is needed in Data Engineering

  • Kubernetes vs Docker Swarm vs traditional methods

• 0:45 – 1:30: Kubernetes Architecture Overview

  • Master node, worker nodes

  • Components: API Server, Controller Manager, Scheduler, etcd, kubelet, kube-proxy

• 1:30 – 2:15: Installing & Configuring Kubernetes

  • Minikube / Kind / K3s setup

  • kubectl installation and configuration

• 2:15 – 3:00: Kubernetes Objects Basics

  • Pods, ReplicaSets, Deployments

  • Labels and annotations

• 3:00 – 4:00: Hands-on Lab

  • Deploy first Pod and Deployment

  • Explore kubectl commands

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Week 2: Pods, Services, ConfigMaps & Secrets (Beginner → Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Understanding Pods in Depth

  • Pod lifecycle, multi-container pods

  • Init containers and sidecar containers

• 0:45 – 1:30: Services and Networking

  • ClusterIP, NodePort, LoadBalancer, Ingress basics

  • Service discovery and DNS

• 1:30 – 2:15: ConfigMaps and Secrets

  • Externalizing configuration

  • Managing sensitive data securely

• 2:15 – 3:00: Persistent Storage

  • Volumes, PersistentVolume (PV) and PersistentVolumeClaim (PVC)

  • Storage classes for dynamic provisioning

• 3:00 – 4:00: Hands-on Lab

  • Deploy a Pod with ConfigMaps and Secrets

  • Connect Pod to persistent storage

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Week 3: Deployments, Scaling & Scheduling (Intermediate)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Deployments & ReplicaSets

  • Rolling updates, rollbacks, scaling strategies

• 0:45 – 1:30: Horizontal & Vertical Pod Autoscaling

  • HPA, VPA basics

  • Resource requests & limits

• 1:30 – 2:15: Namespaces & Resource Quotas

  • Organizing clusters for multiple teams

  • Limiting resource usage

• 2:15 – 3:00: Scheduling & Affinity Rules

  • NodeSelector, Node Affinity, Pod Affinity/Anti-affinity

  • Taints and Tolerations

• 3:00 – 4:00: Hands-on Lab

  • Deploy a scalable multi-Pod application

  • Implement HPA and resource limits

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Week 4: Helm, Config Management & Monitoring (Intermediate → Advanced)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Introduction to Helm

  • Helm charts, releases, repositories

  • Deploying applications via Helm

• 0:45 – 1:30: Advanced Configuration Management

  • Secrets management with KMS or HashiCorp Vault

  • Using ConfigMaps for dynamic configuration

• 1:30 – 2:15: Monitoring & Logging

  • Prometheus & Grafana for Kubernetes

  • EFK/PLG stack for logs

• 2:15 – 3:00: Security Best Practices

  • RBAC, Service Accounts

  • Pod Security Policies, Network Policies

• 3:00 – 4:00: Hands-on Lab

  • Deploy a Helm chart for a data application (e.g., Spark)

  • Implement monitoring and RBAC

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Week 5: Kubernetes for Data Engineering Pipelines (Advanced)

Sessions: 2 × 3–4 hours

• 0:00 – 0:45: Kubernetes for Data Engineering Workloads

  • Running Spark, Flink, Kafka on Kubernetes

  • StatefulSets for databases

• 0:45 – 1:30: CI/CD Pipelines with Kubernetes

  • Integrate with Jenkins, GitHub Actions

  • Deploy containerized ETL pipelines

• 1:30 – 2:15: Real-World Data Engineering Project

  • Containerized Spark ETL with PostgreSQL or MinIO

  • Deploy using Deployments, Services, PVCs

• 2:15 – 3:00: Scaling & High Availability

  • Cluster autoscaling

  • Multi-node deployments for fault tolerance

• 3:00 – 4:00: Capstone Project Review & Q&A

  • Presentations and feedback

  • Best practices recap

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Key Learning Outcomes

1. Understand Kubernetes architecture and key components.

2. Deploy, scale, and manage containerized data applications.

3. Manage configuration, secrets, and persistent storage.

4. Implement monitoring, logging, and security best practices.

5. Deploy real-world data engineering pipelines (Spark, Kafka, PostgreSQL, MinIO) on Kubernetes.

6. Integrate Kubernetes workloads into CI/CD pipelines and prepare for production deployment.