#66 Airflow (part 1): Components, Architecture & Setup with Docker

Airflow enables you to manage your data pipelines by authoring workflows as Directed Acyclic Graphs (DAGs) of tasks. There’s no concept of data input or output — just flow. You manage task scheduling as code, and can visualize your data pipelines’ dependencies, progress, logs, code, trigger tasks, and success status.

Core components

An Airflow installation generally consists of the following components:

• A scheduler, which handles both triggering scheduled workflows, and submitting Tasks to the executor to run.
• An executor, which handles running tasks. In the default Airflow installation, this runs everything inside the scheduler, but most production-suitable executors actually push task execution out to workers.
• A webserver, which presents a handy user interface to inspect, trigger and debug the behaviour of DAGs and tasks.
• A folder of DAG files, read by the scheduler and executor (and any workers the executor has)
• A metadata database, used by the scheduler, executor and webserver to store state.