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 database stores credentials, connections, history, and configuration. It allows for tracking the status and metadata of all tasks in the system.
  4. Web Interface: Airflow provides a web-based user interface that offers visibility into the DAGs (Directed Acyclic Graphs) and their associated tasks. Users can monitor task logs, perform ad-hoc queries, and manage DAGs through this interface.
  5. Executor: The executor determines how tasks are executed. Airflow supports various executors, including the Sequential, Local, Celery, and Dask executors, allowing users to choose the one that best suits their infrastructure and needs.
  6. Operators: Operators define the logic of what each task performs. Airflow provides a wide range of built-in operators for common operations like Python functions, SQL scripts, data transfer, and more. Users can also create custom operators to suit specific use cases.

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