marquez-airflow 0.2.1

Marquez integration with Airflow

marquez-airflow

A library that integrates Airflow DAGs with Marquez for automatic metadata collection.

Status

This library is under active development at Datakin.

Requirements

• Python 3.5.0+
• Airflow 1.10.3+

Installation

$ pip3 install marquez-airflow

To install from source, run:

$ python3 setup.py install

Usage

from datetime import datetime
from marquez_airflow import DAG
from airflow.operators.postgres_operator import PostgresOperator
from airflow.utils.dates import days_ago

default_args = {
    'owner': 'datascience',
    'depends_on_past': False,
    'start_date': days_ago(1),
    'email_on_failure': False,
    'email_on_retry': False,
    'email': ['[e-mail]']
}

dag = DAG(
    'orders_popular_day_of_week',
    schedule_interval='@weekly',
    default_args=default_args,
    description='Determines the popular day of week orders are placed.'
)

t1 = PostgresOperator(
    task_id='if_not_exists',
    postgres_conn_id='food_delivery_db',
    sql='''
    CREATE TABLE IF NOT EXISTS popular_orders_day_of_week (
      order_day_of_week VARCHAR(64) NOT NULL,
      order_placed_on   TIMESTAMP NOT NULL,
      orders_placed     INTEGER NOT NULL
    );''',
    dag=dag
)

t2 = PostgresOperator(
    task_id='insert',
    postgres_conn_id='food_delivery_db',
    sql='''
    INSERT INTO popular_orders_day_of_week (order_day_of_week, order_placed_on, orders_placed)
      SELECT EXTRACT(ISODOW FROM order_placed_on) AS order_day_of_week,
             order_placed_on,
             COUNT(*) AS orders_placed
        FROM top_delivery_times
       GROUP BY order_placed_on;
    ''',
    dag=dag
)

t1 >> t2

Contributing

See CONTRIBUTING.md for more details about how to contribute.