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Airflow for Windows: The Best Way to Manage Your Workflows

How to Download Airflow for Windows

Airflow is a popular open-source platform that lets you build and run workflows or data pipelines. It allows you to orchestrate tasks across different systems, such as databases, APIs, cloud platforms, and more. Airflow also provides a user-friendly interface to monitor and debug your pipelines.

However, installing Airflow on Windows can be challenging, as it is not officially supported by the Apache Airflow project. One of the common ways to run Airflow on Windows is to use Docker, which is a tool that creates containers for applications. However, Docker can be resource-intensive and complicated to set up.

download airflow for windows

In this article, we will show you how to download and install Airflow on Windows without Docker, using a virtual environment and pip. We will also show you how to access the Airflow UI and enable the example DAG (Directed Acyclic Graph) that comes with Airflow.

What is Airflow and Why You Need It

Airflow is a platform that lets you build and run workflows or data pipelines. A workflow is a sequence of tasks that need to be executed in a certain order, with dependencies and data flows taken into account. A task is a unit of work that can be anything, such as fetching data, running analysis, triggering other systems, or more.

Airflow uses DAGs (Directed Acyclic Graphs) to represent workflows. A DAG is a graph that shows the tasks and their dependencies as nodes and edges. Airflow also has a scheduler that triggers the tasks according to their schedule and priority. The tasks are executed by an executor, which can run them on different machines or clusters.

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Airflow Features and Benefits

Some of the benefits of using Airflow are:

  • Ease of use: You only need a little Python knowledge to get started with Airflow. You can write your workflows as Python scripts and use built-in or custom operators to define your tasks.

  • Open-source community: Airflow is free and has a large community of active users and contributors. You can find many resources, tutorials, plugins, and integrations for Airflow online.

  • Integrations: Airflow has ready-to-use operators that allow you to integrate with various cloud platforms (Google, AWS, Azure, etc.), databases, APIs, and other systems. You can also create your own operators or use third-party plugins.

  • Coding with standard Python: You can create flexible workflows using Python with no knowledge of additional technologies or frameworks. You can also use any Python libraries or modules in your tasks.

  • Scalability: You can scale up or down your Airflow installation depending on your needs. You can use different executors to run your tasks on multiple machines or clusters. You can also use Kubernetes or Celery to distribute your workload.

  • Monitoring and debugging: Airflow has a powerful web interface that lets you visualize your pipelines, track their progress, inspect logs, and troubleshoot issues. You can also set up alerts and notifications for your workflows.

Airflow Alternatives and Comparisons

There are many other tools that offer similar functionality as Airflow, such as Luigi, Apache NiFi, AWS Step Functions, Prefect, Dagster, Kedro, Apache Oozie, etc. Each tool has its own advantages and disadvantages, depending on your use case and preferences.

Some of the factors that you may want to consider when choosing a workflow orchestration tool are:

  • Language support: Some tools are language- agnostic, meaning that you can write your workflows in any programming language, while others are specific to one language, such as Python or Java.

  • Complexity and flexibility: Some tools are more suitable for simple and linear workflows, while others can handle complex and dynamic workflows with branching, looping, parallelism, etc.

  • UI and monitoring: Some tools have a graphical user interface that lets you design and visualize your workflows, while others are code-based. Some tools also have better monitoring and debugging features than others.

  • Scalability and performance: Some tools can scale up or down more easily and efficiently than others, depending on the architecture and the executor that they use.

  • Cost and maintenance: Some tools are free and open-source, while others are paid or require a subscription. Some tools also require more installation and configuration than others.

To compare Airflow with some of the popular alternatives, you can check out this table:


Language Support

Complexity and Flexibility

UI and Monitoring

Scalability and Performance

Cost and Maintenance




Web interface

High (with different executors)

Free and open-source; requires installation and configuration




Web interface

Medium (with Celery)

Free and open-source; requires installation and configuration


Any (with processors)


Web interface (drag-and-drop)

High (with clusters)

Free and open-source; requires installation and configuration

AWS Step Functions

Any (with AWS services)


Web interface (drag-and-drop)

High (with AWS resources)

Paid (per state transition); requires AWS account and setup




Web interface (Prefect Cloud or Server)

High (with Kubernetes or Dask)

Free and open-source; paid for Prefect Cloud; requires installation and configuration To run the webserver, run the following command: airflow webserver

This will start the webserver on port 8080 by default. You can change the port by adding the -p option to the command, such as: airflow webserver -p 8081

This will start the webserver on port 8081 instead. How to Access the Airflow UI and Enable the Example DAG

To access the Airflow UI, you will need to open your browser and go to the following URL:

This will take you to the Airflow login page, where you will need to enter the username and password that you created in step 4. After logging in, you will see the Airflow dashboard, which shows you an overview of your DAGs, tasks, schedules, and more.

To enable the example DAG that comes with Airflow, you will need to toggle the switch next to the example_dag name on the dashboard. This will activate the DAG and make it ready for execution. You can also click on the DAG name to see more details about it, such as its graph view, tree view, code view, etc.

The example DAG is a simple workflow that consists of three tasks: print_date, sleep, and templated. The print_date task prints the current date and time to the log. The sleep task waits for 5 seconds before completing. The templated task prints a templated message that includes some variables from Airflow.

You can manually trigger the example DAG by clicking on the play button next to its name on the dashboard. This will start a new run of the DAG and execute its tasks. You can monitor the progress and status of the tasks on the dashboard or on the graph view. You can also inspect the logs and outputs of each task by clicking on their icons.


In this article, we have shown you how to download and install Airflow on Windows without Docker, using a virtual environment and pip. We have also shown you how to create an Airflow user, run the webserver, access the Airflow UI, and enable the example DAG.

Airflow is a powerful platform that lets you build and run workflows


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