Back to Works
Data Engineering
September 2025

Data Pipeline Architecture

Building scalable ETL pipelines with Apache Airflow and Python

Apache AirflowPythonETLPostgreSQLRedis

Data Pipeline Architecture


Overview

This project demonstrates a robust data pipeline architecture using Apache Airflow and Python for ETL operations. The system processes millions of records daily with comprehensive error handling and monitoring.


Key Features

  • Scalable ETL Processing: Handles millions of records daily with horizontal scaling
  • Error Handling: Comprehensive error handling and retry mechanisms with exponential backoff
  • Monitoring: Real-time pipeline monitoring and alerting with custom dashboards
  • Data Quality: Automated data validation and quality checks with configurable rules

    • Technology Stack

  • Orchestration: Apache Airflow 2.7+
  • Language: Python 3.9+
  • Database: PostgreSQL 14+
  • Caching: Redis 6+
  • Containerization: Docker & Docker Compose
  • Monitoring: Prometheus + Grafana

    • Architecture

    The pipeline follows a modular architecture with separate components for:


  • Data Extraction: Multi-source data ingestion with rate limiting
  • Transformation: Data cleaning, validation, and business logic application
  • Loading: Efficient data warehouse loading with upsert strategies
  • Quality Assurance: Automated testing and monitoring

    • Implementation Details

  • Custom Airflow operators for specific business logic
  • Data lineage tracking and documentation
  • Automated testing with pytest
  • CI/CD pipeline integration

    • Results

  • 40% reduction in data processing time
  • 99.9% data accuracy rate through automated validation
  • Scalable to handle 10x current volume
  • Zero data loss during pipeline failures