airflow-manual/airflow-docker/educational-setup-plan.md

Educational Airflow Setup Plan for Beginners

Current Issues Identified

1. Docker Compose Structure Problems

• Duplicate services: sections in docker-compose.yml
• Missing Greenplum service (referenced in dependencies but not defined)
• Inconsistent container naming

2. Missing Directory Structure

• No airflow/dags/ directory
• No data/ directory for sample files
• No initialization scripts

Proposed Simple Educational Setup

Core Components

1. Fixed Docker Compose Structure

services:
  # PostgreSQL for Airflow metadata
  postgres-metadata:
    image: postgres:16
    environment:
      POSTGRES_USER: airflow
      POSTGRES_PASSWORD: airflow
      POSTGRES_DB: airflow
    ports:
      - "5433:5432"
    volumes:
      - pgmeta:/var/lib/postgresql/data

  # PostgreSQL for training exercises
  postgres-training:
    image: postgres:16
    environment:
      POSTGRES_USER: student
      POSTGRES_PASSWORD: student
      POSTGRES_DB: training
    ports:
      - "5432:5432"
    volumes:
      - pg_data:/var/lib/postgresql/data

  # Airflow services
  airflow-webserver:
    image: apache/airflow:2.9.2
    environment:
      AIRFLOW__CORE__LOAD_EXAMPLES: "False"
      AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres-metadata:5432/airflow
    ports:
      - "8080:8080"
    volumes:
      - ./dags:/opt/airflow/dags
      - ./data:/opt/airflow/data

  airflow-scheduler:
    image: apache/airflow:2.9.2
    environment:
      AIRFLOW__CORE__LOAD_EXAMPLES: "False"
      AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres-metadata:5432/airflow
    volumes:
      - ./dags:/opt/airflow/dags
      - ./data:/opt/airflow/data

  airflow-init:
    image: apache/airflow:2.9.2
    environment:
      AIRFLOW__CORE__LOAD_EXAMPLES: "False"
      AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres-metadata:5432/airflow

2. Hardcoded Environment Variables

Create .env file with all variables hardcoded:

• Airflow admin: admin/admin
• PostgreSQL metadata: airflow/airflow
• PostgreSQL training: student/student

3. Educational DAG Examples

Level 1: Basic Concepts

• hello_world_dag.py - Simple print statements
• sql_basic_dag.py - Basic SQL operations
• file_operations_dag.py - CSV file processing

Level 2: Intermediate

• data_processing_dag.py - ETL pipeline with multiple steps
• branching_dag.py - Conditional task execution

4. Sample Data Structure

data/
├── input/
│   ├── customers.csv
│   ├── orders.csv
│   └── products.csv
├── output/
└── logs/

5. Learning Progression

Week 1: Airflow Basics

• DAG structure and syntax
• Basic operators (PythonOperator, BashOperator)
• Task dependencies

Week 2: SQL Integration

• PostgreSQL connections
• SQL execution in tasks
• Data transformation

Week 3: Real-world Scenarios

• Error handling and retries
• Parameter passing
• Scheduling

Implementation Steps

1. Fix Docker Compose - Remove duplicate sections, add missing services
2. Create .env file - Hardcode all environment variables
3. Setup directories - Create dags/, data/input/, data/output/
4. Create sample DAGs - From simple to complex
5. Add sample data - CSV files for practical exercises
6. Create requirements.txt - Essential Python packages
7. Update documentation - Clear step-by-step instructions
8. Test setup - Ensure everything works end-to-end

Key Educational Principles

• Simplicity First - Start with minimal configuration
• Progressive Complexity - Build skills step by step
• Practical Focus - Real data processing tasks
• Immediate Feedback - Students see results quickly

Sample Exercise Structure

Each DAG will include:

• Clear comments explaining each component
• Step-by-step task definitions
• Expected output descriptions
• Common pitfalls and solutions

Technical Requirements

• Docker and Docker Compose
• Basic Python knowledge
• Basic SQL knowledge
• Web browser for Airflow UI