Step 6: Creating the connection.Ĭreating the connection airflow to connect the Postgres DB as shown in below And it is your job to write the configuration and organize the tasks in specific orders to create a complete data pipeline. Instead, tasks are the element of Airflow that actually "do the work" we want to be performed. DAGs do not perform any actual computation. A DAG is just a Python file used to organize tasks and set their execution context. The above code lines explain that 1st create table task will run then after the insert data execute. Here are a few ways you can define dependencies between them: Here we are Setting up the dependencies or the order in which the tasks should be executed. Insert into employee (id, name, dept) values(1, 'vamshi','bigdata'),(2, 'divya','bigdata'),(3, 'binny','projectmanager'), Here in the code create_table,insert_data are codes are tasks created by instantiating, and also to execute the SQL query we created create_table_sql_query ,insert_data_sql_queryĬREATE TABLE employee (id INT NOT NULL, name VARCHAR(250) NOT NULL, dept VARCHAR(250) NOT NULL) The next step is setting up the tasks which want all the tasks in the workflow. Note: Use schedule_interval=None and not schedule_interval='None' when you don't want to schedule your DAG. We can schedule by giving preset or cron format as you see in the table.ĭon't schedule use exclusively "externally triggered" once and only once an hour at the beginning of the hourĠ 0 * * once a week at midnight on Sunday morningĠ 0 * * once a month at midnight on the first day of the monthĠ 0 1 * once a year at midnight of January 1 # schedule_interval='0 0 * * case of psql operator in airflow', Give the DAG name, configure the schedule, and set the DAG settings # If a task fails, retry it once after waiting Import Python dependencies needed for the workflowįrom _operator import PostgresOperatorĭefine default and DAG-specific arguments
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