From Python to Production Pipeline :A Practical guide to Apache Airflow
The article provides a practical guide to using Apache Airflow for orchestrating data pipelines. It explains the core concepts of Airflow, including DAGs, tasks, and operators, and how to set it up locally. By the end of the tutorial, readers will be able to create a working data pipeline that fetches, cleans, and saves weather data.
- ▪Apache Airflow is a workflow orchestrator that schedules and monitors tasks but does not move data itself.
- ▪A Directed Acyclic Graph (DAG) defines the order of tasks in a pipeline, while tasks represent individual units of work.
- ▪The tutorial guides users through setting up Airflow locally and creating a data pipeline that fetches and processes weather data.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3935287) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Varun Joshi Posted on May 24 From Python to Production Pipeline :A Practical guide to Apache Airflow #python #airflow #dataengineering #tutorial You have been using python and you have written scripts that pull data, clean it and load it somewhere, May be you even have a while True loop running on the server somewhere or a cron job you are afraid to touch.
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