This notebook will cover an analysis of a subset of customer applications at Snap Finance. Below summarizes my findings and includes code used to conduct my analysis.

Data

I used AWS's Relational Database Services(RDS) to create and populate a PostgreSQL table with Jupyter Notebooks. Once I had the database instance setup, I was ready to store both the customer_data.csv and merchant_data.csv tables out on the cloud.

In previous work environments, we have used the python package psycopg2 as a wrapper to load the tables into the database. During this exercise, I found a more straight forward approach with Python by using the sqlalchemy library. With this function, I was able to insert my data with pandas API df.to_sql. It was easy to implement however the table loading speed could use optimization.

Customer_data