Apache Airflow is an open-source tool for coordinating and scheduling intricate workflows and data pipelines. Apache Airflow enables programmers and data engineers to specify, plan, and keep track of workflows as Directed Acyclic Graphs (DAGs) which can be construed as processes similar to any other ETL tool, where each node stands for a necessary action or process. Airflow is a popular option for ETL (Extract, Transform, Load) operations, data processing, and automation jobs since it offers a robust collection of tools and functionalities to manage data workflows.
Key attributes and applications of Apache Airflow:
Workflow orchestration: Airflow makes it simpler to manage and automate data pipelines by allowing users to build and schedule complicated workflows including numerous jobs with dependencies.
Airflow’s flexible architecture enables developers to define their own custom operators, making it dynamic and extensible. Some critical features of Apache Airflow include:
- Task Dependency Management: Airflow handles task dependencies automatically, ensuring that tasks run in the correct order based on their dependencies, ensuring data integrity and consistency.
- Monitoring and Alerting: Airflow offers a web-based user interface to monitor the status and progress of workflows. It also supports email notifications and integrations with external monitoring tools.
- Parallel Execution: Airflow allows parallel execution of tasks, which is beneficial for processing large volumes of data efficiently.
- Integration with External Systems: Airflow can easily integrate with various external systems, including databases, cloud storage, and Big Data platforms.
- Extensive Ecosystem: Airflow has a rich ecosystem of plugins and integrations, providing additional features and functionalities to meet specific requirements.
Here are the steps and prerequisites to install Apache Airflow on your system:
Prerequisites: Apache Airflow requires Python 3.6+ and pip to be installed on your system. It is recommended to set up a virtual environment to manage dependencies.
Airflow Web Server:
The Airflow Web Server is a user interface (UI) component that provides a web-based dashboard for managing and monitoring workflows. It allows users to interact with Airflow, visualize the status of DAGs and tasks, and perform various actions related to workflow management. The web server enables users to:
- View the DAGs and their relationships in a graphical representation.
- Monitor the progress and status of individual tasks and workflows.
- Trigger DAG runs manually or set up scheduling intervals for automatic execution.
- View logs and details of past and ongoing workflow executions.
- Enable/disable DAGs and individual tasks as needed.
- Access various configuration settings and environment variables.
The web server enhances the user experience and makes it easier for data engineers and operators to manage workflows, troubleshoot issues, and gain insights into workflow performance.
Install Apache Airflow: You can install Apache Airflow using pip by running the following command:
pip install apache-airflow
Initialize Airflow Database: After installing Airflow, you need to initialize the Airflow metadata database. Run the following command to set up the database:
airflow db init
In Apache Airflow ecosystem, the “Airflow Web Server” and “Airflow Scheduler” are two critical components responsible for the management and execution of workflows defined as Directed Acyclic Graphs (DAGs).
The Airflow Scheduler is a core component responsible for executing and managing DAG runs based on the specified schedules or triggers. It continuously polls the database to identify which workflows and tasks need to be executed at specific intervals or in response to external events. The scheduler ensures that tasks are executed in the correct order, taking into account their dependencies and any specified scheduling options.
Key uses of the scheduler are:
- Determining the execution order of tasks based on their dependencies defined in the DAG.
- Initiating task instances for execution at the appropriate time according to the specified schedules or triggers.
- Handling retries for failed tasks based on the configured retry policy.
- Updating the status and metadata of tasks and workflows in the Airflow metadata database.
- Managing the concurrency and parallel execution of tasks to optimize resource utilization.
- Coordinating task execution with the Airflow Workers, which are responsible for running the actual task code.
The Airflow Scheduler plays a crucial role in automating the execution of workflows, ensuring that tasks run in the correct sequence, and managing the overall workflow execution process.
# Start the web server
# Start the scheduler
The web server will be accessible at http://localhost:8080, where you can access the Airflow UI.
#Change the username details:
airflow users create --username admin –password pwd –firstname fname –lastname lname –role Admin –email firstname.lastname@example.org
Optional: Set Up Airflow Configuration: You can configure Airflow by modifying the airflow.cfg file located in the AIRFLOW_HOME directory. This file contains various settings, including database connections, authentication, and more.
Define and Run DAGs: Once Airflow is installed, you can define your workflows as Python scripts using the Airflow API. These Python scripts are referred to as DAGs (Directed Acyclic Graphs). Place your DAG scripts in the dags_folder specified in the airflow.cfg file.
Hence, Airflow should is now installed on your and ready to manage and schedule your data workflows.