Containerization
Docker and Kubernetes for Data Platforms
Overview
Containerization packages applications and their dependencies into isolated environments. For data platforms, containers ensure reproducibility across development, testing, and production.
Tools
| Tool | Purpose | When to Use |
|---|---|---|
| Docker | Container engine | Local development, testing |
| Kubernetes | Container orchestration | Production, scaling |
Guides
| Document | Description | Key Topics |
|---|---|---|
| Docker Guide | Containerization basics | Dockerfile, Compose, optimization |
| Kubernetes Guide | Container orchestration | Spark, Airflow, Jupyter on K8s |
Typical Workflow
Containerization for Data
Benefits
- Reproducibility: Same environment everywhere
- Isolation: No dependency conflicts
- Portability: Run anywhere
- Scalability: Easy to scale with Kubernetes
- Resource Efficiency: Better resource utilization
Challenges
- Complexity: Kubernetes has learning curve
- Networking: Container networking can be complex
- Storage: Persistent storage requires configuration
- Monitoring: Need observability stack
Learning Path
- Start with: Docker Guide - Learn containerization basics
- Then: Kubernetes Guide - Learn orchestration
- Practice: Build and deploy data applications
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