Data Engineering: Apache Airflow #1

DATA ENGINEERING SERIES | KEEP IN TOUCH

Rahul S

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Apache Airflow is a powerful open-source platform designed for orchestrating, scheduling, and monitoring complex data workflows. In this article, we’ll delve into the key features, components, and use cases of Apache Airflow.

Apache Airflow, initially developed by Airbnb, is a platform for programmatically authoring, scheduling, and monitoring workflows. It allows users to define and manage workflows as code, making it highly flexible and adaptable to a wide range of use cases. Airflow’s core strength lies in its ability to automate, schedule, and manage complex data pipelines, enabling organizations to streamline data processing and analysis.

Key Components of Apache Airflow:

  1. Scheduler: The scheduler is the brain of Apache Airflow, responsible for orchestrating the execution of tasks on a trigger or schedule. It manages the allocation of resources, schedules task dependencies, and ensures that tasks are executed at the appropriate time.
  2. Work Queue: Airflow uses a message queuing system, such as Apache Celery, to distribute tasks to worker nodes. This ensures parallel and distributed execution of tasks, making it scalable and efficient.
  3. Metadata Database: The metadata…

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