Learn How to Download and Verify Airflow Using Released Packages
Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow's extensible Python framework enables you to build workflows connecting with virtually any technology. A web interface helps manage the state of your workflows. Airflow is deployable in many ways, varying from a single process on your laptop to a distributed setup to support even the biggest workflows.
Workflows in Airflow are defined as Python code, which means they are dynamic, extensible, flexible, and testable. Workflows can be stored in version control, developed by multiple people simultaneously, and parameterized using the Jinja templating engine. Workflows consist of tasks that can run any arbitrary code, such as running a Spark job, moving data between buckets, or sending an email. Tasks can be configured with dependencies, retries, alerts, and more.
If you prefer coding over clicking, Airflow is the tool for you. Airflow allows you to automate and orchestrate your data pipelines, tasks, and jobs in a scalable, reliable, and elegant way. In this article, you will learn how to download and install Airflow, how to create and run a simple Airflow DAG (Directed Acyclic Graph), what are the benefits of using Airflow for workflow management, and what are some best practices to optimize your Airflow usage.
Before installing Airflow, you need to check the prerequisites and supported versions. Airflow requires Python as a dependency. Therefore, the first step would be to check the Python installation on the server where you wish to set up Airflow. It can be easily achieved by logging in to your server and executing the command python --version or python3 --version.
Airflow is tested with Python 3.7, 3.8, 3.9, 3.10, and 3.11. You can use any of these versions to run Airflow. However, we recommend using the latest stable version of Python for better performance and compatibility.
How to download airflow on windows
Download airflow docker image
Download airflow source code from github
Download airflow helm chart for kubernetes
Download airflow providers packages
Download airflow python package from pypi
Download airflow documentation pdf
Download airflow examples dags
Download airflow cli tool
Download airflow webserver ui
Download airflow logs and metrics
Download airflow scheduler and executor
Download airflow plugins and hooks
Download airflow dependencies and requirements
Download airflow configuration file
Download airflow security certificates
Download airflow database schema and migrations
Download airflow testing and debugging tools
Download airflow custom operators and sensors
Download airflow rest api client
Download airflow dag serialization and parsing
Download airflow backport packages for 1.10.x
Download airflow upgrade check script
Download airflow stable release version
Download airflow nightly build version
Download airflow tutorial videos and courses
Download airflow best practices and tips
Download airflow community cookbook and recipes
Download airflow official logo and branding
Download airflow roadmap and vision
Download airflow integration with aws services
Download airflow integration with google cloud platform services
Download airflow integration with azure services
Download airflow integration with apache spark and hadoop
Download airflow integration with snowflake and redshift
Download airflow integration with postgresql and mysql
Download airflow integration with mongodb and cassandra
Download airflow integration with slack and email
Download airflow integration with jira and github
Download airflow integration with looker and tableau
Download airflow integration with salesforce and hubspot
Download airflow integration with stripe and paypal
Download airflow integration with twilio and sendgrid
Download airflow integration with shopify and woocommerce
Download airflow integration with spotify and youtube
Download airflow integration with twitter and facebook
Airflow also requires a database backend to store its metadata and state information. You can use PostgreSQL, MySQL, SQLite, or MSSQL as your database backend. However, SQLite is only used for testing purposes and should not be used in production. PostgreSQL is the most commonly used database backend for Airflow and has the best support and features.
The minimum memory required we recommend Airflow to run with is 4GB, but the actual requirements depend wildly on the deployment options you choose. You should also check out the page on the official Airflow documentation for more details.
There are different ways to install Airflow depending on your preferences and needs. You can install Airflow from PyPI (Python Package Index), from sources (released by Apache Software Foundation), or using Docker images or Helm charts (for Kubernetes deployments). In this article, we will focus on installing Airflow from PyPI or from sources.
Installing from PyPI
This installation method is useful when you are not familiar with containers and Docker and want to install Apache Airflow on physical or virtual machines using custom deployment mechanisms. You can use pip (Python package manager) to install Airflow from PyPI.
To install Airflow from PyPI, you need to follow these steps:
Create a virtual environment for your Airflow installation using python -m venv <environment name>. For example: python -m venv airflow-envActivate the virtual environment using source <environment name>/bin/activate. For example: source airflow-env/bin/activate
Upgrade pip to the latest version using pip install --upgrade pip
Install Airflow using pip install apache-airflow. You can also specify the version of Airflow you want to install using pip install apache-airflow==<version>. For example: pip install apache-airflow==2.2.3
Optionally, you can also install extra packages or providers for Airflow using pip install apache-airflow[extras]. For example: pip install apache-airflow[postgres,google]. You can check the list of available extras and providers on the page on the official Airflow documentation.
Initialize the database for Airflow using airflow db init. This will create the necessary tables and users for Airflow in your database backend.
Create a user account for accessing the Airflow web interface using airflow users create --username <username> --password <password> --firstname <firstname> --lastname <lastname> --role Admin --email <email>. For example: airflow users create --username admin --password admin123 --firstname John --lastname Doe --role Admin --email firstname.lastname@example.org
Start the Airflow web server using airflow webserver. This will launch the web server on port 8080 by default. You can change the port using the --port option.
Start the Airflow scheduler using airflow scheduler. This will start the scheduler process that monitors and triggers your workflows.
Open your browser and navigate to You should see the Airflow web interface where you can log in with your user account and manage your workflows.
Installing from sources
This installation method is useful when you want to install the latest development version of Airflow or when you want to customize or contribute to the Airflow codebase. You can install Airflow from sources by cloning the GitHub repository and building it locally.
To install Airflow from sources, you need to follow these steps:
Clone the Airflow GitHub repository using git clone
Navigate to the cloned directory using cd airflow
Create a virtual environment for your Airflow installation using python -m venv <environment name>. For example: python -m venv airflow-env
Activate the virtual environment using source <environment name>/bin/activate. For example: source airflow-env/bin/activate
Upgrade pip to the latest version using pip install --upgrade pip
Install all the dependencies for Airflow using pip install -e .[all]. This will install all the extras and providers for Airflow as well as some development tools.
If you want to run tests or use Breeze (a development environment for Airflow), you also need to install some additional dependencies using pip install -e .[devel].
You can now follow the same steps as installing from PyPI to initialize the database, create a user account, start the web server and scheduler, and access the web interface.
In this section, we will show you how to create and run a simple Airflow DAG that prints "Hello, world!" to the console. A DAG is a collection of tasks that define a workflow in Airflow. Each task is an instance of an operator, which is a class that defines what action to perform. Operators can be built-in (such as BashOperator, PythonOperator, etc.) or custom (such as your own Python class).
Creating a DAG file
To create a DAG in Airflow, you need to write a Python script that defines the DAG object and its tasks. The script should be placed in the d