Fast Food ETL Demo

The purpose of this project is to demonstrate skills in the ETL (extract, transform, load) process. For this project our team saught out several different fast food databases from different fast food franchasis (in this case: Subway, Burger King, McDonalds, and Starbucks) and loaded the information into one cleaned SQL database.

Data for this project was found at https://www.kaggle.com/

Instructions on how to implement our process can be found below.

Follow these steps to create database and transfer data:

1. Clone repo to your desktop.
2. Open pgAdmin4 and create a new database titled FastFood_db.
3. Navigate to FastFood_db and open a query tool.
4. Open ERD.sql from the cloned repo folder.
5. Select all and run query.
6. Close the query tool and open a new query tool from FastFood_db.
7. Open food_class.sql.
8. Select all and run query.
9. Close query tool.
10. On your desktop navigate to the cloned repo folder.
11. Right-click the repo folder and select Git Bash Here to open new a git bash terminal.
12. Type source activate PythonData in the terminal and press enter.
13. From the terminal open jupyter notebook.
14. In jupyter notebook create a new text file and rename it config.py.
15. Within config.py create a variable called username and a variable called password. Assign your postgres username and password to the respective variables.
16. Save and close config.py.
17. Navigate to and open Restaurant_ETL.ipynb.
18. Go to Kernel and select Restart & Run All.

Example Query

To test database run the following example query.

1. Open pgAdmin4.
2. Navigate to FastFood_db and open a query tool.
3. Open example.sql.
4. Select all and run query.
5. Close query tool.