pelaospeed

COPY live.order (order_id, user_name, order_status, order_date, order_approved_date, pickup_date, delivered_date, estimated_time_delivery)
FROM 'C:\Users\William\Documents\DataScience\blibli\e-commerce-datsci-proj\data\raw\order_dataset.csv' 
DELIMITER ',' 
CSV HEADER;

-- and so on

pelaospeed

select user_name , customer_zip_code , customer_city , customer_state 
from (
	SELECT 
		DISTINCT *,
		RANK() over(
				PARTITION BY user_name 
				ORDER BY customer_zip_code DESC, customer_state DESC , customer_city DESC
		) as rank
	FROM 
		live.user u

pelaospeed

insert into staging.dim_product (
	product_id, 
	product_category,
    product_name_length, 
    product_description_length, 
    product_photos_qty, 
    product_weight_g, 
    product_length_cm, 
    product_height_cm, 
    product_width_cm, 

pelaospeed

-- Notes : Inserting updated data on product dimenstion table
insert into staging.dim_product (
	product_id, 
	product_category,
    product_name_length, 
    product_description_length, 
    product_photos_qty, 
    product_weight_g, 
    product_length_cm, 
    product_height_cm, 

pelaospeed

-- Fact table 
with stg_snapshot_user as (
	select distinct 
		user_name,
		count(order_id) as num_order 
	from live."order"
    where order_status is not null and order_status not in ('canceled', 'unavailable') 
	group by user_name 
), stg_snapshot_spending as (
	select distinct  

pelaospeed

"""
Code that goes along with the Airflow tutorial located at:
https://github.com/airbnb/airflow/blob/master/airflow/example_dags/tutorial.py
"""
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.generic_transfer import GenericTransfer
from airflow.contrib.hooks import FTPHook
from airflow.hooks.mysql_hook import MySqlHook