Building Pipelines with Apache Airflow

Apache Airflow has become the de facto standard for building, monitoring, and maintaining data pipelines. As data volumes and complexity grow, the need for robust and scalable orchestration is paramount. In this chapter, we will cover the fundamentals of Airflow – installing it locally, exploring its architecture, and developing your first Directed Acyclic Graphs (DAGs).

We will start by spinning up Airflow using Docker and the Astro CLI. This will allow you to get hands-on without the overhead of a full production installation. Next, we’ll get to know Airflow’s architecture and its key components, such as the scheduler, workers, and metadata database.

Moving on, you’ll create your first DAG – the core building block of any Airflow workflow. Here, you’ll get exposed to operators – the tasks that comprise your pipelines. We’ll cover the most common operators used in data engineering...