Apache Airflow is a powerful platform for teams to automate and manage workflows programmatically. Its underlying technology transforms sequences of tasks into Directed Acyclic Graphs (DAGs). To automate these workflows, Airflow includes a scheduler that automatically delegate tasks to multiple workers. A robust set of command line utilities ensures fast execution. Additionally, Airflow includes a graphical user interface (GUI) to visualize pipelines and monitor production and progress. Using the API, it is easy to create and configure pipelines and monitor production progress and problems.
As a developer, Airflow can help you customize your environment to meet your specific needs. Its DDLs and service offerings make it possible to tailor your installation to suit your needs. For example, you can define a DAG to use as a starting point for a pipeline. The DAGs in Airflow are stored in the DAGs directory. The Airflow Scheduler looks for files named dag and “airflow” in their file names. It then parses these files and updates the metadata database.
The AWS managed workflows for Apache Airflow allow you to build ETL pipelines quickly and easily on AWS. Airflow workflows can retrieve input from Amazon S3, perform transformations on Amazon EMR clusters, and train machine learning models on Amazon SageMaker. And because workflows are written in the Python programming language, they are highly secure. With these benefits, you can use Airflow to build complex ETL pipelines.
In addition to the managed services, Apache Airflow supports integration with AWS. With managed services, teams can use Apache Airflow to automate workflows. These services are useful for production analytics. The managed services offer limited flexibility but reduce the cost of scaling and operation. As an alternative to managed services, MWAA also supports on-premises resources. The MWAA API is compatible with Apache Airflow. The two services make it easier for enterprises to automate their data pipelines.
Among its popular components are Airflow Docker and the Astronomer Registry. The Astronomer registry is a curated repository of Apache Airflow integrations. The AFCTL tool automates Airflow tasks and ensures best practices are followed. Other airflow components include a DockerOperator and an ECR plugin. They both support DockerOperator and a Deferrable Operator. There are a variety of other components available on the Astronomer Registry.
Many organizations use Apache Airflow for batch data pipelines. With its built-in frameworks, it makes it easier to set up ETL jobs. Airflow is a fundamental component for creating Machine Learning Models. This software streamlines data processing and analysis by enabling users to build Machine Learning models with minimal manual intervention. Custom Airflow sensors poll pipeline status and advance pipelines if they complete successfully, or resubmit if they fail.
Airflow also has APIs. For instance, an API that uses Amazon S3 is called S3KeySensor. The S3 KeySensor listens to the S3 key for specific files or directories. Hooks are a crucial piece of Apache Airflow, because they provide a uniform interface for developers and cloud providers. Hooks are building blocks of an operator. An instance of an operator is called a task and each instance of that operator is represented as a node of the DAG.