Apache Airflow ETL Pipeline

Create a production-ready ETL pipeline using Apache Airflow.

Pipeline Stages

1. Extract

• Data source: [Source: API/Database/S3]
• Handle pagination for APIs
• Incremental data extraction

2. Transform

• Use pandas for data manipulation:
  • Data cleaning (null handling, duplicates)
  • Aggregations and calculations
  • Joining multiple datasets
• Data validation and quality checks

3. Load

• Destination: [Destination: Data Warehouse/Database]
• Batch inserts for performance
• Upsert logic for updates

DAG Configuration

Task Dependencies

• Clear dependency graph
• Use >> operator for readability

Retry Logic

• Exponential backoff strategy
• Maximum retry attempts
• Retry delay configuration

SLA Monitoring

• Set task SLAs
• Monitor pipeline execution time

Alerts

• Email notifications on failure
• Custom alert callbacks
• Slack/PagerDuty integration

TaskGroups

• Organize related tasks
• Improve DAG visualization
• Logical grouping of operations

Data Quality Checks

Validation Tasks

1. Row count validation - Ensure expected data volume
2. Schema validation - Verify column names and types
3. Null checks - Critical fields not null
4. Data range checks - Values within expected ranges

Best Practices

Idempotency

• Safe to re-run tasks
• Deterministic outcomes
• Handle duplicates gracefully

Logging

• Comprehensive logging at each stage
• Log data quality metrics
• Audit trail for debugging

Requirements

• Complete DAG implementation
• Production-ready error handling
• Performance optimized
• Well-documented code