Docker Use Cases
Local Development Environment
Problem
Different developers have different setups (OS, Python versions, dependencies), leading to "works on my machine" issues.
Solution
Standardized development environment using Docker.
Project Structure:
Dockerfile:
docker-compose.yml:
Usage:
Benefits:
- Every developer has identical environment
- New developer onboarding:
git clone && docker-compose up - No Python/Postgres/Redis installation needed on host
- Easy to switch between project versions
CI/CD Pipeline
Problem
Need to build, test, and deploy application automatically on every commit.
Solution
Docker-based CI/CD pipeline.
GitHub Actions Workflow (.github/workflows/ci.yml):
Dockerfile.test:
Impact:
- Automated testing on every PR
- Consistent build environment
- Fast feedback (cached layers)
- Secure: No dependencies installed on CI runner
Microservices Architecture
Problem
Monolithic application is hard to scale and deploy.
Solution
Break into microservices using Docker.
Architecture:
docker-compose.yml:
nginx.conf:
Benefits:
- Independent scaling (scale worker without scaling API)
- Independent deployment (deploy API without affecting web)
- Technology diversity (API in Python, web in Node.js)
- Fault isolation (API crash doesn't affect web)
Data Pipeline with Apache Airflow
Problem
Need to orchestrate complex data pipelines with dependencies.
Solution
Airflow running in Docker with isolated task execution.
docker-compose.yml:
Example DAG (dags/etl_pipeline.py):
Usage:
Benefits:
- Reproducible pipelines
- Easy to scale workers
- Isolated from host system
- Version-controlled infrastructure
Testing Across Multiple Versions
Problem
Need to test application against multiple Python/database/dependency versions.
Solution
Matrix testing with Docker.
Script (test-matrix.sh):
Dockerfile.test:
GitHub Actions Matrix:
Benefits:
- Test all combinations automatically
- Catch version-specific bugs
- Ensure broad compatibility
- No need to install multiple versions on host
Machine Learning Model Training
Problem
ML training requires specific GPU setup, libraries, and dependencies.
Solution
GPU-enabled Docker containers for training.
Dockerfile:
docker-compose.yml:
Usage:
Benefits:
- Reproducible training environment
- Easy to switch GPU types
- Share environment with team
- Works same on workstation and cloud
Database Migration and Seeding
Problem
Need consistent database setup across environments.
Solution
Dockerized database with init scripts.
docker-compose.yml:
init-scripts/01-schema.sql:
init-scripts/02-seed.sql:
Advanced: With Flyway Migrations:
migrations/V1__initial_schema.sql:
migrations/V2__add_timestamps.sql:
Usage:
Blue-Green Deployment
Problem
Need zero-downtime deployments.
Solution
Blue-green deployment with Docker and nginx.
docker-compose.yml:
nginx.conf (initially pointing to blue):
Deployment script:
Benefits:
- Zero downtime
- Instant rollback (switch nginx back)
- Test new version before cutover
- Gradual migration (canary deployment)
Ready to try these patterns? Check out Tutorials for hands-on projects!