ETL

Python Watchdog YAML-Based ETL Pipeline for Azure Data Lake Project Overview Developed a robust, event-driven ETL pipeline that monitors filesystem events and automatically processes and uploads data to Azure Data Lake Storage Gen2. The system used YAML configuration files for pipeline definition, making it highly configurable and maintainable. Business Context The business needed a flexible solution to continuously monitor specific directories for new data files, process them according to predefined rules, and reliably upload the results to cloud storage. This enabled near real-time data processing without the complexity of a full streaming solution. ...

Migrating ETL Workflows to Azure Databricks: A Case Study In this post, I’ll share my experience leading the migration of ETL workflows from legacy systems to Azure Databricks at Zürich Insurance. This project presented unique challenges and opportunities for modernizing our data infrastructure. Project Overview The goal was to migrate existing ETL workflows from legacy systems to Azure Databricks, improving scalability, maintainability, and performance. The migration involved multiple data sources and complex transformations. ...

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: ...