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Apache Airflow

Apache Airflow is an open-source platform for orchestrating complex workflows. It allows the scheduling and monitoring of data pipelines, offering flexibility and extensibility for managing tasks in a distributed and scalable manner.

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Frequently asked questions
  • What is Apache Airflow?

    Apache Airflow is an open-source platform for orchestrating complex workflows and data pipelines. It allows users to schedule, monitor, and manage workflows as directed acyclic graphs (DAGs). Airflow is widely used for automating and orchestrating tasks in data engineering, machine learning, and other domains.

  • How does Apache Airflow define workflows?

    Workflows in Apache Airflow are defined as directed acyclic graphs (DAGs), where nodes represent tasks and edges define the sequence of task execution. Each task is a unit of work, and dependencies between tasks are explicitly defined, allowing for flexible and dynamic workflow execution.

  • What are the key components of Apache Airflow?

    Key components include the Airflow Scheduler (for managing task scheduling and execution), the Airflow Web Server (providing a user interface for DAG management), the Metadata Database (storing metadata and configuration), and Executors (defining how tasks are executed).

  • How does Apache Airflow support dynamic and parameterized workflows?

    Apache Airflow supports dynamic workflows by allowing the definition of dynamic DAGs and tasks at runtime. Workflows can also be parameterized using Jinja templating, enabling the dynamic configuration of tasks based on parameters or external variables.

  • How is Apache Airflow different from cron jobs or other schedulers?

    Unlike traditional schedulers, Apache Airflow provides a more expressive and dynamic way to define, schedule, and monitor workflows. It allows for complex dependencies between tasks, retries on failure, dynamic workflow generation, and a centralized web interface for monitoring and managing workflows.

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