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Containers & DevOpsBeginner → IntermediateNEW
Docker for Data Engineers
Master Docker for data engineering — Dockerfile best practices, multi-stage builds for Python, docker-compose dev stacks (Airflow, Kafka, PostgreSQL), and production hardening with GitHub Actions CI/CD.
4.8rating0 students3h 30m total3 lessons
What you'll learn
Build optimised Docker images for Python pipelines with multi-stage builds
Manage bind mounts, named volumes, and .dockerignore correctly
Compose a full Airflow CeleryExecutor stack with health checks
Run a Kafka + Schema Registry + AKHQ dev environment in one command
Harden production images: non-root user, secrets injection, health checks
Scan images with Trivy and push to ECR via GitHub Actions OIDC
Final Project
Package a Python data pipeline in Docker, compose a local Airflow + PostgreSQL + Kafka dev stack, and deploy via a hardened GitHub Actions CI/CD pipeline
Curriculum
3 lessons · 3h 30mCourse Info
Lessons3 lessons
Total time3h 30m
LevelBeginner → Intermediate
Students0
Rating4.8 / 5.0