2/25/2023 0 Comments Airflow dag![]() ![]() Line 1-2 – The first two lines are importing various airflow components we would be working on DAG, Bash Operator.So let’s go thru the code and try and understand it. One thing to wrap your head around (it may not be very intuitive for everyone at first) is that this Airflow Python script is really just a configuration file specifying the DAG’s structure as code. An important thing to note and I quote from the airflow website Please refer to the previous blog which has the details on the location. Remember this code is stored in the $DAGS_FOLDER. set_upstream (t1 ) Understanding an Airflow DAG 'retry_delay': timedelta (minutes = 5 ) ,ĭag = DAG ( 'hello_world2', schedule_interval = '0 0 * * *' ,Ĭreate_command = 'echo HELLOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO ' bash_operator import BashOperatorįrom datetime import datetime, timedelta Our pipeline is complete and scheduled to automatically update on a daily basis!Ĭheck out the full repository on my GitHub.From airflow. Head over to the Postgres database and perform a SELECT on the covid_data table to verify that our DAG has successfully executed. Make sure you toggle the covid_nyc_data DAG on, and click the play button under the links column to immediately trigger the DAG. You should be able to access Airflow’s UI by going to your localhost:8080 in your browser. To test our project, navigate to your terminal and run the following commands airflow initdb "start_date": datetime.today() - timedelta(days=1)Īppend this piece of code to the main covid_dag.py script and voila! our ETL/DAG is complete. from airflow import DAGįrom _operator import PythonOperator Note the value of “0 1 ” in our schedule_interval argument which is just CRON language for “run daily at 1am”. ![]() To get started, we set the owner and start date (there are many more arguments that can be set) in our default arguments, establish our scheduling interval, and finally define the dependency between tasks using the bit shift operator. In our case, we will be using two PythonOperator classes, one for each ETL function that we previously defined. Here is a complete look after wrapping our ETL tasks in functions and importing the necessary libraries Setting Up Our Airflow DAGĪirflow DAGs are composed of tasks created once an operator class is instantiated. ![]() Taking a peek at an example response from the NYC OpenData API, you can see that it shouldn’t be too difficult coming up with a schema for our database.csv".format(date.today().strftime("%Y%m%d"))) as f: ![]() Project Structure airflowĮvery pipeline should start with a careful look at the data that will be ingested. For the sake of keeping this article short and focused on Airflow’s scheduling capabilities, please check out this link to setup Postgres and Airflow. Setting up Airflow and an Airflow database is fairly simple but can involve a few steps. Finally, we’ll be using Airflow to orchestrate and automate this pipeline by scheduling the aforementioned steps on a daily basis. The dataset will be pulled as a JSON file and converted to CSV format before being loaded into a PostgreSQL database. We will be using a public open dataset on the counts of COVID-19 related hospitalization, cases, and deaths in New York City as our external data source. ![]()
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