What is Apache Airflow? Beginners Guide to Data Engineering (2024)

What is Airflow?

Apache Airflow is an open-source workflow authoring, scheduling, and monitoring application. It's one of the most reliable systems for orchestrating processes or pipelines that Data Engineers employ. You can quickly see the dependencies, progress, logs, code, trigger tasks, and success status of your data pipelines.

Airflow allows users to create workflows as DAGs (Directed Acyclic Graphs) of jobs. The robust user interface of Airflow makes it simple to see pipelines in production, track progress, and resolve issues as needed. It links to a variety of data sources and can send an email or Slack notice when a task is completed or failed. Because Airflow is distributed, scalable, and adaptable, it's ideal for orchestrating complicated business logic.

Components of Airflow

• Dag: The Directed Acyclic Graph (DAG) is a compilation of all the tasks you wish to do that is ordered and displays the relationships between them. In a python script, it's defined.
• Web Server : The user interface for Flask is called a web server. It enables us to keep track of the DAGs' state and initiate actions.

• Metadata Database : Airflow uses a metadata database to keep track of the status of all jobs and to perform all read/write operations on a workflow.
• Scheduler: This component is in charge of scheduling the execution of DAGs, as its name suggests. The task's status in the database is retrieved and updated.

Where to use Airflow?

Apache Airflow can be used to schedule the following tasks:

• ETL pipelines which are data extraction pipelines that run Spark tasks or other data transformations on data from many sources.
• Training for machine learning models.
• Report generation (Automated)
• Backups and other DevOps responsibilities.

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