Live Online Docker Course for Data Engineering

 

Format: Live online sessions using Google meet or MS Teams with hands-on coding, mini-projects, and a capstone project by an industry expert.
Target Audience: College Students, Professionals in Finance, HR, Marketing, Operations, Analysts, and Entrepreneurs
Tools Required: Laptop with internet
Trainer: Industry professional with hands on expertiseLive Online Docker Course for Data Engineering

Week 1: Introduction to Containers & Docker Basics (Beginner)

Sessions: 2 × 3–4 hours

• Introduction to Containerization
  • Virtualization vs. containerization
  • Benefits for data engineering pipelines
• Docker Architecture Overview
  • Docker Engine, CLI, Daemon
  • Images, Containers, Registries
• Installing Docker
  • Docker Desktop / Docker Engine
  • Basic commands: docker run, docker ps, docker stop, docker rm
• Running Your First Container
  • Explore container lifecycle
  • Hello World, Python container examples
• Hands-on Lab:
  • Run multiple containers, inspect logs, and clean up containers

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Week 2: Docker Images, Dockerfile & Basic Pipelines (Beginner → Intermediate)

Sessions: 2 × 3–4 hours

• Docker Images Basics
  • Pull, tag, inspect, remove images
  • Docker Hub and public/private images
• Dockerfile Fundamentals
  • Commands: FROM, RUN, COPY, CMD, EXPOSE
  • Build reproducible environments
• Building Custom Images
  • Python, Pandas, Spark images for data pipelines
• Image Optimization
  • Layering, caching, reducing image size
• Hands-on Lab:
  • Build a custom Python ETL image
  • Run data ingestion script inside container

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Week 3: Networking, Volumes & Docker Compose (Intermediate)

Sessions: 2 × 3–4 hours

• Docker Networking Basics
  • Bridge, Host, None networks
  • Container-to-container communication
• Persistent Storage
  • Volumes vs. bind mounts
  • Sharing and persisting data across containers
• Docker Compose Fundamentals
  • Multi-container orchestration with docker-compose.yml
  • Environment variables & secrets management
• Data Engineering Pipelines with Compose
  • Example: Kafka → Spark → PostgreSQL
  • Scaling services
• Hands-on Lab:
  • Deploy a mini pipeline using Docker Compose

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Week 4: Logging, Monitoring, Security & Private Registries (Intermediate → Advanced)

Sessions: 2 × 3–4 hours

• Container Logging
  • Log drivers, logging best practices
  • Collecting logs for ETL processes
• Monitoring Containers
  • Introduction to Prometheus and Grafana
  • Monitoring resource usage of containers
• Security Best Practices
  • Secure images, scan vulnerabilities
  • User permissions, secrets, and environment management
• Private Registries
  • Push/pull images to AWS ECR, Azure ACR, Docker Hub private
• Hands-on Lab:
  • Secure and monitor Spark + PostgreSQL container setup

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Week 5: CI/CD, Kubernetes Intro & Capstone Project (Advanced)

Sessions: 2 × 3–4 hours

• Docker in CI/CD Pipelines
  • Integrate Docker with Jenkins, GitHub Actions, Airflow
• Introduction to Kubernetes for Data Engineers
  • Pods, Deployments, Scaling containers
  • When to move from Docker Compose to Kubernetes
• Capstone Project: Containerized ETL Pipeline
  • Airflow + Spark + PostgreSQL + MinIO
  • Multi-stage deployment using Docker images
• Project Review & Presentations
  • Peer review and instructor feedback
  • Best practices recap, Q&A

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Key Learning Outcomes After 5 Weeks

1. Master Docker architecture, containers, images, and Dockerfiles.
2. Build and manage multi-container data pipelines using Docker Compose.
3. Implement persistent storage, networking, logging, and monitoring.
4. Apply container security best practices.
5. Integrate Docker with CI/CD pipelines.
6. Gain a foundational understanding of Kubernetes for scaling data workflows.
7. Deploy a real-world containerized data engineering pipeline as a capstone project.

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