Apache Airflow is a versatile, platform-independent tool for workflow orchestration, offering extensive capabilities for creating and monitoring both streaming and batch pipelines. With its comprehensive features, even the most complex processes can be implemented seamlessly. Airflow is supported by key platforms and tools in the Data Engineering world, such as AWS and Google Cloud.
Airflow not only provides scheduling and management of processes but also enables real-time tracking of job execution, allowing for swift identification and resolution of errors.
In brief: Airflow is currently one of the most in-demand and "hyped" tools in pipeline orchestration. It is widely adopted by companies globally, and knowledge of Airflow is fast becoming an essential skill for data engineers, particularly for students beginning their career in this field.
Basic Concepts of Airflow
This section introduces you to the fundamentals of working with Airflow. You will learn how DAGs (Directed Acyclic Graphs) are created, what they consist of (operators, tasks), and how the architecture of Airflow is structured, including the database, scheduler, and web interface. We will also examine examples of event-driven pipelines that can be implemented using Airflow.
Installation and Environment Setup
In this practical module, you will work on a project involving weather data processing. The DAG will fetch data from a weather API, transform it, and store it in a Postgres database. You will gain skills in:
- Configuring the environment using Docker;
- Verifying the web interface and container operations;
- Configuring the API and creating the necessary tables in the database.
Practice: Creating DAGs
In this hands-on practice session, you will delve into the Airflow interface and learn to monitor task statuses effectively. You will:
- Create DAGs based on Airflow 2.0 that retrieve and process data;
- Master the Taskflow API—a modern approach to building DAGs with more convenient syntax;
- Implement parallel task execution (fanout) to run multiple processes simultaneously.
