AIRFLOW Apache Airflow: Orchestration and First Look
Welcome to the first tutorial in the Airflow series. Before writing any code, we start with why cron jobs fall short for real data pipelines, what orchestration solves, and how Apache Airflow fits in. You'll get familiar with the core concepts and everything after this builds on them.
Orchestration
A data pipeline usually has several steps, you need a way to run those steps in the right order and know when something fails.
Every pipeline is defined as a DAG, which stands for Directed Acyclic Graph. Directed means tasks flow in one direction. Acyclic means no loops. Graph is just a set of connected steps.
Airflow
Airflow is the most widely used orchestration tool in data engineering. Built at Airbnb in 2014 and now the industry standard. Pipelines are defined in Python.
Three concepts to know:
- DAG: the entire workflow.
- Task: a single unit of work — one box in the graph.
- Scheduler: runs the DAG on a schedule, tracks every run's status, duration, and logs.
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Running the Environment
Scroll down and press Start Airflow. Once it's up, open the Airflow UI link. Sign in with username airflow and password airflow.
The Airflow Homepage
The top of the homepage shows stats: failed, running, and active DAGs. Everything is at zero for now since no DAGs exist yet. The health panel confirms Airflow's components are running, and the history section lists recent DAG runs with their status and timestamps.
What's next
Start Airflow Suggested tutorials
Keep the momentum going, pick a related topic.