What Is Apache Airflow?
Apache Airflow is an open-source platform for authoring, scheduling, and monitoring data pipelines. Created at Airbnb and now part of the Apache Software Foundation, it lets data teams define workflows as code, so a pipeline is version-controlled, testable, and repeatable like any other software. Airflow does not move or transform data itself; it orchestrates the steps that do, deciding what runs, in what order, and what happens when something fails.
DAGs: Pipelines as Code
The core concept in Airflow is the DAG, a directed acyclic graph. A DAG describes a set of tasks and the dependencies between them: task B runs after task A, tasks C and D run in parallel, task E waits for both. “Acyclic” means the graph never loops back on itself, so there is always a clear start and finish. Each DAG is written in Python, which is what makes a pipeline programmable rather than click-configured.
How Airflow Works
Airflow has a few moving parts that work together:
- The scheduler decides when each task should run, based on the schedule and dependencies.
- Executors and workers run the actual tasks.
- The metadata database tracks the state of every run.
- The web interface shows pipeline status, history, and logs.
When a task fails, Airflow can retry it, alert the team, and pick up where it left off, which is what makes it dependable for production data work.
What Airflow Is Used For
Airflow is most often the orchestration layer over a data platform. It schedules the nightly load from source systems, triggers transformation jobs, kicks off model training, and coordinates the many steps that move data from an ERP or application into a warehouse or lakehouse. It is the conductor, not the instrument: it calls the tools that extract, load, and transform while keeping the whole sequence ordered and observable.
Airflow and ETL/ELT
Airflow pairs naturally with ETL and ELT. The extract, load, and transform steps are the work; Airflow is the schedule and dependency manager that runs them in the right order and reports on them. In a modern stack it commonly orchestrates ingestion tools and transformation frameworks rather than replacing them.
Orchestration in an ERP Reporting Foundation
Moving ERP data into governed reporting is a multi-step, recurring job: pull from the source, land it, transform it, refresh the model. Those steps need to run on a schedule, in order, with monitoring and recovery when a source is late or a load fails. That orchestration is part of what a data foundation provides. QuickLaunch builds and runs the pipelines that keep JD Edwards, Vista, NetSuite, and OneStream data flowing into a governed model, so teams get dependable refreshes without standing up and babysitting the orchestration themselves.
Frequently Asked Questions
What is Apache Airflow used for?
Scheduling, orchestrating, and monitoring data pipelines. It coordinates the steps that move and transform data between systems, deciding what runs, in what order, and what happens when a task fails.
What is a DAG in Airflow?
A directed acyclic graph: a set of tasks and the dependencies between them, written in Python. It defines the order tasks run in and never loops back on itself, so each pipeline has a clear start and finish.
Is Airflow an ETL tool?
Not exactly. Airflow orchestrates ETL and ELT rather than performing the extract and transform itself. It schedules and manages the tools that do the actual data work.
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