Components and Key Terminology in Apache Airflow

Components in Apache Airflow

Apache Airflow’s functionality relies on the interaction of several components, allowing flexible scaling from a single machine to a full cluster.

The scheduler, together with an executor, tracks and triggers stored workflows; since Airflow 2.0 multiple schedulers can be used to reduce latency for large numbers of tasks.

When a workflow starts, a worker executes the stored commands, and workers with specific environments can be selected for special RAM or GPU requirements.

The web server provides a graphical interface for user interaction; it can be omitted, though its monitoring features are popular.

The metadata database securely stores statistics about workflow runs and connection data to external databases.

Important terminology in Apache Airflow

A DAG (Directed Acyclic Graph) is the internal representation of a workflow; a DAG run corresponds to a workflow run, and DAG files are kept in the DAG bag, as illustrated by the ETL example.

In Python, tasks are combined into a DAG, which acts as a container for tasks, their order, and execution parameters such as schedule interval, start time, and retry policy; complex workflows can be modeled without cycles, and conditional branching is supported.

Tasks can be defined as operators (which execute commands) or sensors (which wait for an event). Operators range from simple BashOperator to specialized cloud operators like GoogleCloudStorageToBigQueryOperator, facilitating integration with AWS, GCP, Azure, and other services.

The web UI visualizes DAGs in graph and tree views, using color‑coded edges to indicate task status; logs can be accessed with a few clicks, making monitoring and troubleshooting straightforward.

Overall, Airflow reliably executes data processes and, combined with Python, makes it easy to define what runs in a workflow and how.