Understanding Apache Airflow: DAGs, Tasks, and Operators Explained Simply

2026-04-11 · Source: Machine Learning on Medium · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Novice, short

Summary

Apache Airflow uses three core concepts—DAGs, Tasks, and Operators—to define and execute data workflows. A DAG (Directed Acyclic Graph) acts as the workflow's blueprint, specifying what runs and in what order, ensuring tasks flow unidirectionally without loops. Tasks are individual units of work within a DAG, such as extracting or transforming data. Operators are the building blocks that define how these tasks are executed, with Airflow providing various built-in operators like `PythonOperator` for Python code or `BashOperator` for shell scripts. Dependencies between tasks can be defined using bitshift operators (e.g., `task_a >> task_b`) or explicit methods, enabling parallel execution and complex branching logic. Airflow automatically detects DAGs placed in the `dags/` folder as `.py` files.

Key takeaway

For Data Engineers designing ETL pipelines, understanding Airflow's DAGs, Tasks, and Operators is crucial for building scalable and debuggable workflows. You should define your workflow's structure with a DAG, break down work into specific tasks, and implement task logic using appropriate operators like `PythonOperator` or `BashOperator` to ensure clear execution flow and maintainability.

Key insights

Airflow orchestrates workflows using DAGs as blueprints, Tasks as work units, and Operators as execution templates.

Principles

• Workflows must be directed and acyclic.
• Tasks are defined by Operators.

Method

Define a DAG in a `.py` file, specify tasks using operators like `BashOperator` or `PythonOperator`, and establish task execution order with bitshift operators or explicit dependency methods.

In practice

• Use `BashOperator` for shell scripts.
• Use `PythonOperator` for Python functions.
• Place DAGs in the `dags/` folder.

Topics

• Apache Airflow
• Directed Acyclic Graph
• Airflow Tasks
• Airflow Operators
• Workflow Orchestration

Best for: Data Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.