![]() Metadata Database: Airflow supports a variety of databases for its metadata store.We will cover the details later in this blog. ![]() ![]() People usually select the executor that suits their use case best. There are various types of executors that come with Airflow, such as SequentialExecutor, LocalExecutor, CeleryExecutor, and the KubernetesExecutor. Executor: While the Scheduler orchestrates the tasks, the executors are the components that actually execute tasks.Scheduler: This is the most important part of Airflow, which orchestrates various DAGs and their tasks, taking care of their interdependencies, limiting the number of runs of each DAG so that one DAG doesn’t overwhelm the entire system, and making it easy for users to schedule and run DAGs on Airflow.The Web Server also provides the ability to manage users, roles, and different configurations for the Airflow setup. Web Server: This is the UI of Airflow, that can be used to get an overview of the overall health of different Directed Acyclic Graphs (DAG) and also help in visualizing different components and states of each DAG.Understanding the components and modular architecture of Airflow allows you to understand how its various components interact with each other and seamlessly orchestrate data pipelines. Airflow Architecture diagram for Celery Executor-based Configurationīefore we start using Apache Airflow to build and manage pipelines, it is important to understand how Airflow works.
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