Airflow

Building Scalable Data Pipelines with Apache Airflow Introduction Building scalable data pipelines is crucial for modern data engineering. In this post, I’ll share my experience and best practices for creating maintainable and efficient data pipelines using Apache Airflow. Why Apache Airflow? Apache Airflow has become the de-facto standard for workflow orchestration in data engineering. Here’s why: Declarative DAGs: Write your workflows in Python Rich Ecosystem: Extensive collection of operators and hooks Scalability: Can handle complex workflows with thousands of tasks Monitoring: Built-in UI and logging capabilities Community: Large, active community and regular updates Best Practices 1. Modular DAG Design Keep your DAGs modular and reusable: ...

Data Pipeline Automation Overview This project implements a robust data pipeline automation system that processes and transforms data from multiple sources into a unified data warehouse. The system is designed to be scalable, maintainable, and easily extensible. Key Features Automated data extraction from various sources (APIs, databases, files) Data validation and quality checks Incremental data loading Error handling and retry mechanisms Comprehensive logging and monitoring Automated testing suite Architecture The system is built using a modern data stack: ...