How can you use Airflow to schedule data tasks?

There are also fully managed cloud-based services available for running Airflow without the need for installation, such as GCP Cloud Composer and Astro.

Airflow runs on a few components: - Scheduler to schedule tasks - Executors to execute tasks - Webserver to provide the UI - Folder structure to read Dags - Metadata database to keep track of task states To avoid being intimidated and getting started quickly with Airflow, I recommend to test your pipelines on an airflow docker container, instructions on the official documentation. By doing this, you can get airflow up as easy as one command, which will install and run all the components above in an isolated container. The container can also be restarted anytime when you encounter errors. This set up simulates an airflow deployment accurately in a risk-free way, exposing you to the airflow components like DAG folder and the database.

1) Create a docker compose for the components of Airflow. 2) Open a terminal and run the following command to start Apache Airflow: bash docker-compose up -d This will pull the necessary Docker images and start Apache Airflow in the background. 3) Access the Airflow Web UI by opening a web browser and going to https://localhost:8080 (or the address specified in the docker-compose.yml file). You can use the Airflow Web UI to manage and schedule your workflows. To stop and remove the containers, run the following command in the same directory: bash docker-compose down

Creating a DAG file is as simple as: 1. Create a .py file in the DAG folder 2. Import the DAG class from Airflow 3. Instantiate the DAG with default parameters 4. Create a task and tag it to the DAG You must define default parameters like the schedule interval, owner, and start date of the DAG, otherwise, the DAG will not be loaded. This is the simplest form of DAG creation. You can also create task dependencies, sensors, branching tasks, and more by properly defining them in the DAG file. Once the DAG file is uploaded to the DAG folder. Navigate to the airflow UI to verify that your DAG is indeed live. Depending on the airflow config min_file_process_interval, it takes some time, the default is 30 seconds.

A task is isolated to a single action to enable proper debugging, dependency handling, and reusability in pipelines. A good starting point is to properly define all the tasks required in the pipeline. To write a task in the DAG file, you need to use an operator. An operator is an airflow class that takes in parameters while providing additional features like timeouts and retries. Airflow has a plethora of Operators for all your needs. If you can think of one, airflow probably already has it. Start by instantiating the operator class by providing the required arguments in the DAG file. You can create your own custom operator if needed. It is done by importing the BaseOperator from airflow and defining your own arguments and methods.

Set task dependencies in the DAG file by using 1. >> or << 2. set_upstream or set_downstream Both options achieve the same result. A few tips 1. Use list elements like [task1, task2] >> task3 to combine dependencies. The command says that task3 will trigger if either task1 or task2 completes. 2. Use dummy operators to aggregate dependencies like [task1, task2] >> dummy_operator >> task3. The command says that task3 will trigger if both task1 and task2 are completed. 3. Sensors can be defined as tasks, which function by checking the completion of other tasks or DAGS. 4. Triggering a DAG can be converted into a Task. Be cautious about which task can run in parallel with other tasks and which cannot.

Ideally, in a production like environment, monitoring every DAG through a web based environment is not practical. Instead we can enable notification on email or slack on failures. Also we can enable retry on failures (on failure /retry callback)

Airflow has been one of the go to tools to refer for orchestrating data tasks in the recent data driven industry. Having clarity of the basic concepts related to Install and setting up Airflow, Defining the data tasks and setting up relevant dependencies is important. For example, if Task B depends on the successful completion of Task A, you can define Task B as a downstream task of Task A in the DAG. Setting up task schedule based on the requirement and emphasizing on Monitoring and managing tasks helps to supports automatic retries for failed tasks. One additional factor is to Extend Airflow with custom operators that provides a set of built-in operators for common data tasks, such as PythonOperator, BashOperator, and SQLOperator.

While reading an article or watching a video about Airflow can provide you with a basic understanding of its features and capabilities, practical experience takes your knowledge to the next level. The benefits of getting your hands dirty with a tool cannot be overstated. When you install, configure, and code with a tool, you gain a much deeper understanding of its limitations. Each problem you encounter becomes an opportunity to develop problem-solving skills that are essential in any career. Ultimately, the purpose of using tools in a professional setting is to achieve tangible results and make a real-world impact. A hands-on approach ensures that you are well-equipped to use the tool effectively to drive positive outcomes in your work.