Demonstrating and Building ETL pipelines in Airflow This repo demonstrates a use case for n Ecommerce business that has a platform that generates transaction data each time a purchase is made. With this transaction data, the functions in the pipeline seek to answer 3 business questions

1. Who is our platinum customer? Anyone with purchase value equal to or more than 5000
2. What is the purchase history like for each user? This builds a dataset that can be used for a recommendation engine downstream
3. What items are commonly purchased together? This builds a dataset that can be used for Basket Analysis downstream

The code can be found in etl_utils.py file. Question 1 is implemented using pd.merge() to get the combined dataset and df.groupby().sum() to get total purchases.

To get the platinum customer, we apply a filter

final_df = df.loc[df['total_purchase_value']>=10000]

Both question 2 and 3 are achieved using Pandas Pivot Tables pd.pivtot_table()

The sample CSVs generated by the above functions can be found in the samples/ folder.

I have used the PostgresOperator which requires a postgres connection and a SimpleHttpOperator which requires a http connection. This is set in the Admin UI in the connections tab.