Airflow has built-in support using schedulers.Airflow is versatile in nature and can be used across various domains, including growth analytics, data warehousing, engagement analytics, anomaly detection, email targeting, among others.One of the main advantages of using a workflow system like Airflow is that all is code, which makes the workflows maintainable, versionable, testable, and collaborative.Some of the intuitive features of AIrflow are mentioned below: Metadata database: Metadata database powers how the other components interact, stores the Airflow states and all the processes read and write from here.Executor: Executor is the mechanism that gets the tasks done.It uses the DAGb object to decide what tasks need to be run, when and where. Scheduler: Scheduler is a multithreaded Python process and is responsible for scheduling jobs.Web server: It is the GUI, which remains under the hood of a Flask app where you can track the status of your jobs and read logs from a remote file store.There are four key components of Airflow, which are: It is a workflow engine that performs several tasks, such as managing scheduling and running jobs and data pipelines, managing the allocation of scarce resources, provides mechanisms for tracking the state of jobs and recovering from failure and more. The platform is a flexible, scalable workflow automation and scheduling system for authoring and managing Big Data processing pipelines of hundreds of petabytes.

Behind the BasicsĬreated by Airbnb, Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. Your newsletter subscriptions are subject to AIM Privacy Policy and Terms and Conditions.

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