Apache Airflow: Workflow Orchestration for Data Pipelines
Modern organizations that work with data depend on pipelines that collect, transform, enhance, and transfer information from one place to another. These pipelines can include many individual stages, such as extracting raw data, cleaning it, training machine-learning models, creating dashboards, and more. These steps also need to run in a clearly defined sequence. Workflow orchestration tools such as Apache Airflow help make sure every stage runs at the correct time and in the correct order. They also make pipelines easier to observe, control, and maintain.
Airflow was originally developed at Airbnb in 2014 and has since become one of the most widely used open-source workflow orchestration platforms. Thousands of organizations use it to automate and monitor batch-based pipelines. In this article, Apache Airflow is explained from the basics. The topics covered include:
- What Airflow is, what it does, and why data teams rely on it.
- The main concepts and components of Airflow, and how they work together.
- How pipelines are represented as Directed Acyclic Graphs (DAGs), including a walkthrough of a simple DAG.
- Typical use cases such as ETL/ELT, MLOps, and other workflow scenarios.
This guide is intended to be a practical, example-based introduction to Apache Airflow for engineers, students, and teams beginning their journey with workflow orchestration.
Key Takeaways
- Apache Airflow is a Python-based workflow orchestration platform used to manage, schedule, and monitor complex data pipelines with DAGs, also known as Directed Acyclic Graphs.
- DAGs describe tasks and their dependencies, enabling Airflow to run each step in the correct order while offering built-in retries, logging, and visibility.
- Airflow’s architecture, including the Scheduler, Executor, Worker, Metadata Database, and Web UI, supports scalable distributed execution and real-time pipeline monitoring.
- Airflow is especially useful for batch workflows such as ETL/ELT jobs, machine-learning training pipelines, data validation checks, and scheduled analytics. However, it is not the best fit for real-time or extremely high-frequency workloads.
- Tools such as Prefect, Dagster, Luigi, Argo Workflows, and Mage AI may be more suitable depending on requirements such as event-driven processing, Kubernetes-native execution, or a more modern developer-focused interface.
What Is Apache Airflow?
Apache Airflow is an open-source platform for creating, scheduling, and monitoring workflows. In Airflow, a workflow is represented as a DAG, or directed acyclic graph. This means it is a collection of tasks with clearly defined relationships, also called dependencies, and no circular paths. In other words, a task cannot loop back to itself through an upstream dependency. Airflow follows the idea of “configuration as code.” Instead of building workflows through a drag-and-drop interface, workflows are written as Python scripts. This gives engineers a high level of flexibility: Airflow operators and Python libraries allow connections to many different technologies. It also makes it possible to apply software engineering practices such as version control, testing, and code review to pipeline definitions.
Why Workflow Orchestration Matters
The main reason workflow orchestration is important is that data workflows often contain several steps that must run in a specific order. A simple example is an ETL pipeline, which stands for Extract, Transform, and Load. As more tasks, dependencies, branching paths, and schedules are added, pipelines can quickly become difficult to manage. Manual execution or basic scheduling tools often become insufficient in these situations. Airflow solves this by acting as a central orchestration layer. It stores workflows as directed acyclic graphs where tasks are nodes and dependencies are edges. Airflow understands which task should run when, waits for upstream dependencies to finish, supports scheduled execution, handles errors through retries and alerts, and keeps detailed logs.
If a data pipeline is compared to a recipe, each task is one step in the preparation process, such as chopping, boiling, or plating. Airflow acts like the kitchen manager who understands the recipe and oversees every step in the kitchen, ensuring that everything happens in the proper order and at the right time.
Without workflow orchestration, you may have the chefs and ingredients ready, but there is no coordination. Airflow provides that coordination and helps produce the intended result, which is the completed workflow run.
Why Do Data Teams Use Airflow?
Airflow is widely used by data engineers, machine-learning engineers, and DevOps teams because it addresses many challenges that come with complex data pipelines. The following sections describe some of the main reasons teams choose Airflow.
Orchestrating Complex Workflows and Dependencies
Airflow was designed for workflows that contain many interdependent steps. It makes it straightforward to define upstream and downstream requirements so that tasks run in the correct order. If task C depends on tasks A and B, the Airflow scheduler understands that relationship and ensures A and B run, potentially in parallel, before C starts. Airflow can also handle complex dependency structures, branching logic, and similar workflow patterns.
Centralized Scheduling
Airflow includes a scheduler that can execute tasks according to a defined timetable. You can define dozens or hundreds of pipelines that run at different intervals, such as hourly, daily, or monthly. Airflow understands task relationships and can trigger tasks intelligently. This centralized scheduler removes the need to maintain separate scheduling scripts across multiple servers.
Scalability and Distributed Execution
Airflow can grow along with your workflows. It can be configured to run tasks in parallel across multiple worker processes or machines. It also offers several executors, described later in this article, that can execute tasks on distributed systems such as Celery or Kubernetes across a cluster of nodes. This allows resource-intensive jobs, such as large-scale data processing, to be parallelized while multiple pipelines run at the same time.
Monitoring, Logging, and Alerting
Airflow includes a powerful web interface that gives real-time visibility into workflows. You can see which tasks succeeded, which failed, and inspect log output for each task. Tasks can also be triggered or retried manually through the web interface.
Key Components of Airflow
The following table gives a brief overview of the core building blocks of Apache Airflow. It can be used as a quick reference for understanding what each component is and what it does.
| Component | What it is | Core responsibilities |
|---|---|---|
| DAGs (Directed Acyclic Graphs) | The workflow blueprint, written in Python, represented as a graph of tasks with dependencies and no cycles. | Defines which tasks run and in what order; stores schedule and metadata; enables visualization in the UI. |
| Tasks | The smallest unit of work, represented as a node in the DAG graph and created from operators or TaskFlow functions. | Runs a specific action, such as executing SQL, calling Python, triggering an API, or moving files; reports success or failure. |
| Scheduler | A long-running service that continuously evaluates DAGs and task states. | Decides when each task should run based on schedules and dependencies; creates task instances; manages retries and backfills. |
| Executor and Workers | The execution backend and the processes that actually run tasks. | Starts task instances on the selected backend, such as a local process or thread, Celery workers, or Kubernetes pods; returns results to the Scheduler. |
| Web Server (Web UI) | The operational interface served by Airflow’s webserver. | Provides observability and control: view DAGs, trigger or clear tasks, inspect logs, pause DAGs, and manage connections and variables. |
| Metadata Database | The persistent system of record, typically PostgreSQL or MySQL. | Stores DAG runs, task instances and states, configurations, log metadata, and operational history. |
How Airflow Works: Architecture
The following is a step-by-step overview of how Airflow processes a workflow, also known as a DAG.
Authoring the DAG
The DAG author writes a Python script that defines the DAG. This script contains information such as the schedule, for example “run daily at 9 AM,” or manual trigger settings. It also contains task definitions through operators or @task functions, along with task dependencies such as task1 >> task2, meaning task2 waits for task1 to finish. The Python file is placed in Airflow’s DAGs folder so Airflow can discover it. Airflow periodically reads and parses this file to load the DAG definition.
Scheduling and DAG Parsing
The Airflow Scheduler is a process that runs continuously in the background. It checks whether each DAG should be executed based on its schedule or triggers. For instance, a DAG scheduled to run daily can be detected by the scheduler, which then creates a new run at midnight. The scheduler also checks whether tasks inside DAGs are ready to run, meaning their upstream dependencies have completed and their scheduled time, also called the logical date, has arrived.
Executing Tasks
When a task is ready to run, the Scheduler hands it over to the Executor. The Executor decides where and how the task should run. It may execute locally or be sent to a worker queue. In a distributed architecture, the task is placed into a queue and then picked up by Worker processes that execute the task.
Updating State and Handling Errors
As each task instance finishes, it reports its status, such as success or failure, back to the scheduler. The scheduler then updates the metadata database with the task state.
This state tracking allows the scheduler to decide what should happen next. For example, if Task A finishes successfully, the scheduler knows it can now schedule Task B. If Task A fails but has retries remaining, the scheduler schedules another attempt. If it fails and no retries are left, the DAG run is marked as failed. When a task fails, Airflow can automatically retry it if retries are configured. If it continues to fail after all retries, Airflow can send alerts, such as email or Slack notifications. These behaviors can be configured as DAG or task parameters, such as retry count, retry delay, or alert recipients.
Monitoring and Intervention
The Airflow Web UI provides a live view of this process. You can see a DAG run appear, for example as a new row in the Tree view for the current run, and watch task statuses change as execution progresses. The Graph view visualizes the DAG tasks and shows which tasks completed successfully and which encountered errors.
Cleanup and Next Run
After a DAG run finishes, meaning all tasks either succeeded, were skipped, or failed, Airflow moves on to the next scheduled run. Metadata about previous runs remains stored in the database, so historical information can be reviewed at any time. The scheduler continues to trigger new runs according to the schedule or manual triggers. This cycle continues until the Airflow process is stopped.
Understanding DAGs in Airflow
A DAG in Airflow defines the schedule, tasks, and dependencies required to run a workflow. A DAG does not need to understand the internal details of each task. It only needs to specify when tasks should run and in what order.
Tasks, Operators, and Sensors
Tasks are the basic work units in Airflow. In Airflow, tasks are usually instances of operators, which are templates for predefined actions. Airflow includes several core operators. The BashOperator can run shell commands, while the PythonOperator can call Python functions. Airflow also provides a @task decorator, which turns a regular Python function into an Airflow task.
Airflow also offers sensors. Sensors are tasks that wait until a condition or event is satisfied before they succeed. They can be used to wait for something like a file appearing or a database table being populated.
Task Dependencies
Dependencies between tasks can be defined using the >> and << operators, which are recommended, or methods such as set_upstream and set_downstream. It is also possible to chain tasks, create cross-downstream dependencies, and dynamically generate lists of tasks. Without dependencies, a DAG would simply consist of independent tasks.
Best Practices for DAG Design
The following table summarizes several guidelines to consider when designing DAGs.
| Best Practice | Description | Practical Tips / Examples |
|---|---|---|
| Ensure DAGs are truly acyclic | DAGs must not contain cycles. In complex workflows, indirect loops can easily be created, such as A → B → C → A. Airflow detects cycles automatically and refuses to run DAGs that contain them. | Review dependencies regularly. Use Graph View to visually confirm there are no loops. Refactor complicated DAGs to prevent accidental cycles. |
| Keep tasks idempotent and small | Each task should handle one logical step and be safe to rerun without damaging or duplicating data. Idempotent tasks help ensure retries do not create inconsistent results. | Use “insert or replace” patterns. Write to temporary files before committing results. Break large scripts into several tasks. |
| Use descriptive IDs and documentation | Clear names and documentation improve readability and maintainability in both the codebase and the Airflow UI. | Choose meaningful DAG IDs and task IDs. Add documentation with dag.doc_md or task.doc_md. Match names to business logic. |
| Leverage Airflow features | Use built-in Airflow capabilities for communication, credential management, and pipeline coordination instead of custom implementations. | Use XComs for small pieces of shared data. Use Variables and Connections for configuration and secrets. Prefer built-in operators and hooks when possible. |
| Test and version control the DAG code | DAGs are code and should be managed using proper software engineering practices, including version control, testing, and CI/CD. | Store DAGs in Git. Write tests for custom operators and logic. Use local or test environments before deploying to production. |
| Avoid overloading the scheduler | Very large numbers of DAGs or heavy computations inside DAG files can slow down the scheduler. DAG files should be lightweight to parse. | Monitor scheduler performance. Split very large DAGs into smaller ones. Avoid API calls or expensive computation during DAG import. |
| Define a clear failure handling strategy | Plan how failures should be handled. Not every error should be retried, and long-running tasks may require SLAs or alerts. | Use retries only for temporary failures. Use Sensors and triggers for event-based dependencies. Set SLAs for long-running or critical tasks. |
Example: A Simple Airflow DAG
Concepts become easier to understand with an example. The following simple Airflow DAG creates a small workflow with two tasks that run in sequence:
from datetime import datetime
from airflow import DAG
from airflow.operators.bash import BashOperator
# Define the DAG and its schedule
with DAG(
dag_id="example_dag",
description="A simple example DAG",
start_date=datetime(2025, 1, 1),
schedule_interval="@daily", # runs daily
catchup=False
) as dag:
# Task 1: Print current date
task1 = BashOperator(
task_id="print_date",
bash_command="date"
)
# Task 2: Echo a message
task2 = BashOperator(
task_id="echo_hello",
bash_command="echo 'Hello, Airflow!'"
)
# Define dependency: task1 must run before task2
task1 >> task2
The following sections explain what is happening in this example:
The required classes are imported first: DAG, which is used to define the DAG, and BashOperator, which is used to run shell or Bash commands as tasks. The datetime class is also imported so the DAG start date can be specified.
A DAG object is created with a context manager using with DAG(…) as dag:. The DAG receives a unique ID, example_dag, which must be unique in an Airflow environment. It also receives an optional description, a start date, and a schedule interval. In this example, the built-in schedule preset @daily is used, and the start date is set to datetime(2025, 1, 1). The parameter catchup=False prevents Airflow from backfilling missed runs between the start date and the current date. This means the DAG is intended to run only from now onward on a daily schedule. The full block creates a DAG that Airflow can discover and run once per day at the beginning of the day.
Two tasks are then defined inside the DAG context. The first task, task1, uses BashOperator to run the Bash command date. Its task_id is print_date, which identifies the task in the UI and logs. When the task runs, it prints the current date and time to its task log.
The second task, task2, also uses BashOperator. Its task_id is echo_hello, and its command is echo 'Hello, Airflow!'. This prints a simple message. At this stage, both tasks are defined but not yet connected.
The final line, task1 >> task2, defines the dependency between the tasks. It means task1 must run and complete successfully before task2 can be scheduled. In the DAG graph, this appears as an arrow pointing from task1 to task2.
Several details are worth noting in this example:
- BashOperator is used only to keep the example simple. A DAG can combine different operator types. For example, PythonOperator could call a Python function, or SimpleHttpOperator could call an API.
- The
with DAG(…) as dag:context pattern is a common way to create a DAG instance. Tasks created inside it are automatically linked to that DAG. catchup=Falseis frequently used in DAGs with a start date in the past when old scheduled runs should not be executed. Ifcatchup=True, which is the default, and the current date were January 10, 2025, Airflow would try to run the DAG for January 1, 2, 3, and so on through January 10 to catch up on missed runs. It is disabled here for simplicity.- This example uses the relative import path for BashOperator. In Airflow 2, many operators were moved under
airflow.providers. The example assumes the relevant providers are installed, while BashOperator itself is part of the Airflow core.
This simple example can be extended easily. Additional tasks could be added, such as calling an API, loading results into a database, and then sending a notification. More complex logic can also be introduced, such as branching. Airflow supports if/else-style branching through operators such as BranchOperator.
As DAGs grow, the Airflow UI and logging features become extremely useful for understanding what is happening. In this example, the log for print_date would contain the system date that was printed, while the log for echo_hello would contain “Hello, Airflow!”. If print_date failed, for example because the date command could not be found in a hypothetical situation, then echo_hello would not run because of the dependency. The DAG run would be marked as failed. You could then inspect the log, fix the problem, and rerun the task or the entire DAG.
Common Use Cases for Airflow
The table below summarizes common Apache Airflow use cases. It explains what each use case involves and how Airflow provides value in that context.
| Use Case | Description (What It Involves & Typical Steps) | How Airflow Helps / Benefits |
|---|---|---|
| ETL/ELT Data Pipelines | Moving data from multiple sources into a data warehouse or data lake using ETL, meaning Extract–Transform–Load, or ELT patterns. | DAGs coordinate extraction → transformation → load in the proper order. Airflow integrates with many databases and storage systems. If one step fails, such as an unavailable API, downstream tasks are stopped and alerts can be sent, which improves reliability for batch and incremental synchronization. |
| Data Warehousing & BI Reporting | Preparing and refreshing analytics data used in dashboards and reports. | Schedules daily or periodic jobs so reports are based on fresh data. Coordinates SQL workloads, quality checks, and reporting stages. Provides monitoring and notifications so failures, such as broken aggregations, are visible instead of silently damaging BI results. |
| Machine Learning Pipelines | Automating complete machine-learning workflows from data preparation through deployment. | Models each ML stage as a task in a DAG and enforces the correct execution order. Can persist artifacts between steps, such as preprocessed data or model binaries. Integrates with ML frameworks and Kubernetes operators, and supports scheduled retraining and experiment orchestration during off-peak times. |
| Data Quality Checks & Validation | Running automated checks to confirm that data is complete, consistent, and trustworthy. | Schedules recurring data quality DAGs, such as daily or weekly checks. Coordinates validations across databases and QA scripts, alerts teams about anomalies, and can trigger downstream correction steps as part of data reliability engineering. |
| Transactional DB Maintenance & Backups | Automating regular operational tasks for production databases and infrastructure. | Centralizes scheduling and monitoring of maintenance jobs. Ensures backups and housekeeping tasks run consistently at defined times, reduces manual effort and human error, and provides logs and history so teams can verify that critical maintenance work completed successfully. |
| Integration / Workflow Automation | Coordinating business or system workflows that span multiple services and APIs. | Acts as a flexible “glue” layer that connects services. DAGs describe complex branching and conditional logic. Operators and Python tasks enable custom integrations. Airflow provides a central place to manage, monitor, and retry business process steps instead of spreading automation logic across ad-hoc scripts and tools. |
When Not to Use Airflow
Although Airflow is powerful, it is not the right solution for every scenario. Knowing its limitations helps you choose the most suitable orchestration approach.
Real-Time or Streaming Workloads
Airflow is designed for batch-oriented workflows with a clear start and end. It is not intended for long-running, event-driven, or streaming workloads. If you are processing continuous event streams, such as user clicks or IoT sensor data, and require very low-latency real-time processing, Airflow is generally not the best choice.
High-Frequency or Many Short Tasks
Airflow may also be inefficient when workflows involve very frequent runs or large numbers of very small tasks. Each Airflow task introduces some overhead, including database tracking. If something needs to run every few seconds or thousands of tiny tasks need to be triggered, Airflow may not scale efficiently for that pattern.
Purely Event-Driven Workflows
This limitation is similar to the streaming case but applies to workflows that should run only when an event occurs. A pattern such as “whenever event X happens, do Y” can be implemented in Airflow, but if that event is the only trigger, for example “when a file arrives in an object storage bucket, do X,” Airflow may not be the lightest solution. Airflow often uses sensors, such as file or object storage sensors, to detect files or events. Sensors can have disadvantages because they typically rely on polling. Potential issues include triggering on files that already exist or not reacting immediately to events because sensors check at intervals.
In summary, Airflow works well for workflows that are periodic, batch-oriented, or complex to define and manage. It may not be a good fit if the use case requires real-time or continuous processing, extremely frequent jobs, or very simple workflows that do not justify Airflow’s overhead. In those cases, other options or a simpler solution may be more appropriate.
Alternatives to Airflow
The workflow orchestration ecosystem has grown significantly in recent years. Although Airflow is a leading option, several alternatives are worth considering.
| Tool / Service | Summary | Reference |
|---|---|---|
| Luigi | Open-source Python workflow scheduler created by Spotify. It is useful for creating batch pipelines with tasks and dependencies in code. It has a simpler and lighter architecture than Airflow, but a smaller ecosystem and fewer built-in integrations. | Luigi documentation |
| Prefect | Python-native orchestration framework positioned as a more modern and developer-friendly alternative to Airflow. It uses Flows and Tasks and includes scheduling, retries, and observability. It can run fully open-source or with Prefect Cloud as a hosted UI and control plane. | Prefect website Prefect docs |
| Dagster | Data orchestrator focused on software-defined assets and data-aware pipelines. It emphasizes type safety, testing, and development workflows. It is a strong fit for teams focused on data lineage, quality, and modern engineering practices. | Dagster website Dagster docs |
| Kedro | Python framework for building reproducible and maintainable data and machine-learning pipelines. It focuses on project structure, modular pipelines, and best practices. It is often used together with an orchestrator such as Airflow rather than replacing one. | Kedro website Kedro docs |
| Argo Workflows | Kubernetes-native workflow engine implemented as a CRD. Each step runs in a container, making it suitable for cloud-native batch jobs, CI/CD, and machine-learning pipelines in Kubernetes-focused environments. | Argo Workflows website |
| Mage AI | Modern data pipeline tool for building, running, and managing ETL, streaming, and machine-learning pipelines through a notebook-style visual interface. It focuses on developer experience and fast iteration. | Mage AI website |
| Kestra | Open-source event-driven orchestration platform that uses declarative YAML-based workflows. It is designed for scalable scheduled and event-driven data and process orchestration and includes a strong plugin ecosystem. | Kestra website Kestra docs |
Airflow is not the only available option, and the best choice depends on the specific use case. Luigi, Prefect, and Dagster are frequently mentioned as other major open-source tools in the same category, especially among Python-based workflow orchestrators. If Airflow’s older interface or other limitations feel restrictive, these alternatives may be worth evaluating. Prefect aims to provide a simpler or improved Airflow-like experience. Dagster focuses on a more structured orchestration model centered on data assets. Luigi is a simpler predecessor to Airflow. If the use case is very different, such as real-time streaming or a fully cloud-native workflow, Airflow may not be used at all, and streaming platforms or managed orchestration services may be better choices.
Final Thoughts
Apache Airflow is an open-source platform for orchestrating complex computational workflows and data processing pipelines. Originally created at Airbnb, it has become highly popular in data engineering and machine-learning environments. Airflow’s main components include a scheduler, executor, workers, metadata database, and web-based interface. Together, these components help teams run and maintain production-ready ETL, MLOps, and infrastructure pipelines. Airflow’s DAG abstraction, meaning Directed Acyclic Graph, keeps workflows explicit, testable, and maintainable over time. Its ecosystem of community-built operators and providers also simplifies workflow orchestration across many different technologies and tools.
However, Airflow is not a universal solution for every workflow. It performs best with batch or micro-batch pipelines, and users should be comfortable working with Python. For streaming or high-frequency workflows, other solutions may be more appropriate. For data scientists or teams that prefer declarative pipeline definitions, Airflow alternatives may also be worth considering.
Airflow continues to be actively developed, with new capabilities being added over time, including event-driven scheduling, datasets, and assets. The concepts and best practices presented in this guide should help you understand how workflow orchestration works and when Airflow is or is not the right choice. This introduction can serve as a foundation for exploring the broader workflow orchestration ecosystem further.
