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Welcome to this quick 3-minute tutorial on the fundamentals of Airflow for Data Engineers!
Airflow is an open-source platform that helps manage, schedule, and monitor data pipelines. It provides a way to define workflows as directed acyclic graphs (DAGs) and execute them in a reliable, scalable, and maintainable way.
At its core, Airflow consists of three main components:
DAGs: A DAG is a collection of tasks with dependencies between them. Each task represents a unit of work, and the dependencies between tasks determine the order in which they should be executed.
Operators: An operator is a Python class that represents a single task in a DAG. There are many built-in operators in Airflow, such as BashOperator, PythonOperator, and SQLOperator, but you can also create your own custom operators.
Scheduler: The scheduler is responsible for triggering tasks based on their dependencies and the defined schedule. It manages the state of each task and ensures that they are executed in the correct order.
To use Airflow, you'll typically start by defining a DAG in a Python script. You'll then create tasks by instantiating operator classes and specifying their dependencies. Once you've defined your DAG, you can run it by starting the Airflow scheduler and worker processes.
Airflow provides a rich set of features that make it a popular choice for managing data pipelines, including:
- A web-based user interface for monitoring and managing DAGs
- Built-in support for task retries, logging, and alerting
- Integration with popular data storage and processing systems like Hadoop, Spark, and Kubernetes
An active community of contributors and plugins that extend its functionality.
Overall, Airflow is a powerful tool that helps Data Engineers manage complex data pipelines with ease. I hope this quick tutorial has given you a good overview of its fundamentals.
Thanks for watching!