Orchestrating complex data flows
Data-driven organisations run hundreds of interdependent operations daily: database extractions, transformations, data warehouse loads, model training, report generation. Managing these operations with cron jobs and bash scripts becomes unsustainable beyond a certain scale: no visibility into state, no structured error handling, implicit dependencies between tasks. Apache Airflow, born inside Airbnb from the work of Maxime Beauchemin, tackles the problem by modelling pipelines as Python code.
The project is donated to the Apache Software Foundation and in 2017 is in the incubation phase, the process through which projects demonstrate they can sustain an independent development community.
Pipelines as DAGs
The fundamental concept in Airflow is the DAG (Directed Acyclic Graph): a directed acyclic graph that describes dependencies between tasks. Each node in the graph is a task — a concrete operation such as running a SQL query, calling an API or moving a file — and edges define execution order. If task B depends on task A, Airflow ensures B runs only after A completes.
DAGs are defined in Python, not in configuration files. This means the developer can use loops, conditions, variables and any Python logic to dynamically generate the pipeline structure. A single script can create a DAG with hundreds of tasks based on a list of tables read from a database.
Scheduler, executor and operators
The scheduler monitors DAGs, determines which tasks are ready for execution based on dependencies and time scheduling, and dispatches them to the executor. The executor handles actual execution: the LocalExecutor runs tasks in local processes, the CeleryExecutor distributes them across remote workers via a message queue.
Operators are the building blocks of tasks: the BashOperator runs shell commands, the PythonOperator runs Python functions, the PostgresOperator executes SQL queries. Specialised operators handle interactions with cloud services, REST APIs, storage systems and data transfers.
Web UI and observability
The web UI provides a complete view of pipeline state: dependency graphs, status of each task (success, failure, running, waiting), detailed logs, execution history and the ability to manually rerun failed tasks. For data engineering teams, Airflow transforms fragile and opaque pipelines into observable and controllable flows.
Link: airflow.apache.org