what is apache airflow
--- Apache Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow’s extensible Python framework enables you to build workflows connecting with virtually any technology. A web-based UI helps you visualize, manage, and debug your workflows. You can run Airflow in a variety of configurations — from a single process on your laptop to a distributed system capable of handling massive workloads.
With its core features like pipeline automation, dependency management, scalability, makes it a vital tool for data engineers.
core concepts of airflow
DAGS - A Directed Acyclic Graph(DAG), according to the official workflow documentation, is a model that encapsulates everything needed to execute a workflow.
Schedule: When the workflow should run.
Tasks: tasks are discrete units of work that are run on workers.
Task Dependencies: The order and conditions under which tasks execute.
Callbacks: Actions to take when the entire workflow completes.
common uses of airflow
- Automation of ETL pipelines
- Data validation and transformation tasks
- schedule data analytics reports
- machine learning, model training and deployment.
advantages of airflow
- It is Python-based based enabling writing of workflows as code.
- Its web-based UI provides real-time monitoring and debugging capabilities.
- Separation of the web server and scheduler components allows for better resource allocation.
- Airflow is modular and extensible, enabling creation of custom operators and plugins. -Airflow's scalability supports distributed execution.
disadvantages of airflow
- It has a steep learning curve.
- Airflow isn't built for streaming data.
- Airflow can be complex to set up for beginners.
- Windows users can't use Airflow locally, unless on WSL.
- Debugging on airflow can betime-consumingg.
Despite the several disadvantages, airflow still proves to be a vital tool for data engineer,s especially when paired with other tools such aApache Kafkaka.
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