{"id":304,"date":"2021-08-16T00:00:00","date_gmt":"2021-08-16T00:00:00","guid":{"rendered":"https:\/\/tac.debuzzify.com\/?p=304"},"modified":"2023-06-23T00:15:05","modified_gmt":"2023-06-23T00:15:05","slug":"the-prefect-way-to-automate-orchestrate-data-pipelines","status":"publish","type":"post","link":"https:\/\/www.the-analytics.club\/the-prefect-way-to-automate-orchestrate-data-pipelines\/","title":{"rendered":"Python Workflow Automation With Prefect (A Better Airflow)"},"content":{"rendered":"\n\n\n

I was a big fan of Apache Airflow. Even today, I don’t have many complaints about it. But the new technology, Prefect, amazed me in many ways, and I can’t help but migrate everything to it.<\/p>\n\n\n\n

Prefect (and Airflow) is a workflow automation tool. You can orchestrate individual tasks to do more complex work. You could manage task dependencies, retry tasks when they fail, schedule them, etc.<\/p>\n\n\n\n

Workflow management is the\u00a0backbone of every data science project. Even small projects can have remarkable benefits with a tool like Prefect. It eliminates a significant part of repetitive tasks. With Prefect you can start automating tasks inside your Jupyter notebook. You can’t do airflow tasks in Jupyter Notebooks.<\/p>\n\n\n\n

Related: <\/b>11 Advantages of Cloud Databases Over On-Premise Databases.<\/i><\/b><\/a><\/p>\n\n\n\n

This article covers some of the frequent questions about Prefect. It includes<\/p>\n\n\n\n